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Aiming at the electric vehicle charging pile control system has the characteristics of multi-parameter, strong coupling and non-linearity, and the existing traditional PID control and fuzzy PID control methods have the problems of slow charging speed, poor control performance and anti-interference ability, as well as seriously affecting the service life of the battery, this paper designs a kind of improved particle swarm algorithm to optimize the PID controller of the charging control system for electric vehicle charging piles, and utilizes the improved particle swarm algorithm to Adaptive and precise adjustment of proportional, integral and differential parameters, so that the system quickly reaches stability, so as to improve the accuracy of the system control output current or voltage. Simulation results show that the optimized system response speed of the improved particle swarm algorithm is improved by 3.077 s, the overshooting amount is reduced by 1.01%, and there is no oscillation, which has strong adaptability and anti-interference ability, and can significantly improve the control accuracy and charging efficiency of the charging pile control system. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop, extend and propose an improved proportional integral derivative (PID) rate control of a quadrotor unmanned aerial vehicle based on a convexity-based surrogated firefly algorithm. Design/methodology/approach: An improved PID controller structure is proposed for the rate dynamics of the quadrotor. Optimality of the controller is ensured by a recent, simple yet efficient firefly optimization method. The hybrid structure is further enhanced with a convexity-based surrogated model function. Findings: Monte Carlo, transient response, error metrics and histogram distribution analyzes are conducted to show the performance of the proposed controller. The performance of the proposed method is evaluated under various convex combination values to further investigate the effect of the proposed surrogated model. According to the results, the proposed method is capable of controlling the rate quadrotor dynamics with the steady-state error of 0.0023 (rad/s) for P, −0.0024 (rad/s) for Q and 0 (rad/s) for the R state, respectively. Also, the least mean objective value is achieved at = 0 value of convexity in Monte Carlo trials. Originality/value: The originality of this paper is to propose an improved PID rate controller with a convexity-based surrogated firefly algorithm. [ABSTRACT FROM AUTHOR]

To enhance the performance of power inspection robots in intricate nuclear power stations, it is necessary to improve their response speed and accuracy. This paper uses the manipulator of the power inspection robot as the primary research object, and unlike previous control algorithm research, which only remained in the software simulation stage, we constructed a set of physical verification platforms based on CAN communication and physically verified the robotic arm's control algorithm. First, the forward motion model is established based on the geometric structure of the manipulator and D-H parameter method, and the kinematic equation of the manipulator is solved by combining geometric method and algebraic method. Secondly, in order to conduct comparison tests, we designed PID controllers and expert PID controllers by utilising the expertise of experts. The results show that compared with the traditional PID algorithm, the expert PID algorithm has a faster response speed in the control process of the manipulator. It converges quickly in 0.75 s and has a smaller overshoot, with a maximum of only 6.9%. This confirms the expert PID algorithm's good control effect on the robotic arm, allowing the six-degree-of-freedom robotic arm to travel more accurately and swiftly along the trajectory of the target point. [ABSTRACT FROM AUTHOR]

A key component of industrial applications is the direct current (DC) motor. Hence, due to their superior features and performance, brushless DC (BLDC) motors are more suitable for fractional kilowatt motor's applications. Albeit, for the purpose of controlling the speed of BLDC motor easily, it is quite difficult for obtaining the best controlling performance through the use of the conventional approaches of tuning. In order to search the proportional--integral--derivative (PID) tuning parameters optimally for the different controllers taken into consideration, the use of modern bio-inspired metaheuristic technique called ant colony optimization (ACO) algorithm is employed. This paper particularly discusses and presents on the tuning parameters of kp; ki; and kd. The performance of traditional controller and novel approach is compared, analyzed, and presented. BLDC motor is buildup in MATLAB, and the usage, importance, efficiency, and strength of the proposed approach are validated against traditional tuning methods. The obtained result shows better performance of the proposed system with the aid of the proposed controllers for different speed trajectories of the drive when compared with that of the classical PID controllers. ACO seems to be one of the most effective tuning techniques of PID controllers. This research has significant impact on modern control applications. [ABSTRACT FROM AUTHOR]

Liquid-level control in tanks is widely used in various industrial sectors. Due to problems arising from the case that the liquid level is above or below the recommended, the process of its control is of great significance. The proportional integral differential (PID) controller is one of the most widely used controllers in applications that require accuracy and optimal automatic control. In this paper, single and coupled ship fuel tank systems are discussed. In addition, mathematical models and their linearization using two different approaches are presented. The two control approaches are used to maintain the specified liquid level in tanks. The first is based on the design of standard PID controllers, with the application of the Ziegler–Nichols, Takahashi tuning, and Auto-tuning methods. The second approach, proposed by the authors, is based on the application of the Dahlin algorithm. The simple modification of the Dahlin controller, which can avoid the "ringing" of the control signal, is suggested in the paper. Moreover, the lower limit of the decrease in the desired time constant, which ensures the desired behavior of the system, is determined. The comparisons provided for different performance indexes show the advantages of the modified Dahlin algorithm approach compared to the standard PID controllers. Furthermore, the comparison with the literature-known approaches is realized. [ABSTRACT FROM AUTHOR]

In this paper, a new method of data-driven controller (DDC) design using Simultaneous Perturbation Stochastic Approximation Algorithm (SPSA) and Neural Network (NN) training is presented. This method can be used to control a variety of linear and nonlinear systems. In the simultaneous perturbation stochastic approximation algorithm, the controller is assumed to have a fixed structure and its parameters must be estimated. In this paper, a Proportional, Integral, and Derivative controller (PID) is considered and the parameters that should be estimated by the proposed algorithm are proportional, integral and derivative terms of this controller. In the proposed method, the simultaneous perturbation stochastic approximation algorithm is quantified by using neural network training which increases the convergence speed and also improves the performance of the algorithm against system input changes. Simulations performed on cement grinding particle size distribution process and pitch angle control of aircraft show the high efficiency and potential of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the inconvenience of inspection and monitoring of coal mine pump room equipment in a narrow and complex environment, this paper proposes and designs a two-wheel self-balancing inspection robot based on laser SLAM. Using SolidWorks, the three-dimensional mechanical structure of the robot is designed, and the overall structure of the robot is analyzed by finite element statics. The kinematics model of the two-wheel self-balancing robot was established, and the multi-closed-loop PID controller was used to design the two-wheel self-balancing control algorithm of the robot. The 2D LiDAR-based Gmapping algorithm was used to locate the robot and construct the map. Through the self-balancing test and anti-jamming test, it is verified that the self-balancing algorithm designed in this paper has a certain anti-jamming ability and good robustness. By using Gazebo to build a simulation comparison experiment, it is verified that the selection of the particle number is of great significance for improving the map accuracy. The actual test results show that the constructed map has high accuracy. [ABSTRACT FROM AUTHOR]

Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema. [ABSTRACT FROM AUTHOR]

Summary: Automatic generation control (AGC) plays a crucial role in modern power system to control the frequency with change in power demand. A novel fractional order (FO) hybrid PID‐fuzzy‐PID (FO‐hPID‐FPID) controller is proposed in this paper as the secondary controller for AGC of a three unequal area interconnected nonlinear reheat thermal power system considering generation rate constraint. This paper also deals with the implementation of FO fuzzy PID (FOFPID) and FO PID (FOPID) controllers in addition to the proposed controller along with their counterpart integer order controllers to establish the dominance of FO controllers over integer order controllers. Various parameters of the controllers are optimized by hybridizing two global optimization techniques: particle swarm optimization and modified sine cosine algorithm (MSCA) and the optimization techniques so obtained is called as hybrid PSO‐MSCA (hPSO‐MSCA) technique. A step load perturbation of 0.01 p.u. is injected in area‐1, and the system response is investigated independently with different controllers. It is revealed that the proposed FO‐hPID‐FPID delivers superior performance than the other controllers. Sensitivity analysis for parametric variations is carried out, and random load test is performed to validate the robustness of the proposed controller. Further, it is observed that the controllers deliver result in an ascending order of control performance as PID, FOPID, FPID, FOFPID, hPID‐FPID and FO‐hPID‐FPID controllers with FO‐hPID‐FPID controller delivering the best result and the FO controllers deliver better control performance than their integer order counterparts. [ABSTRACT FROM AUTHOR]

Given the challenges associated with accurately modeling complex nonlinear systems with time delays in industrial processes, this paper introduces an advanced model-free control algorithm that combines data-driven and model-driven approaches. Initially, an enhanced algorithm for multi-innovation model-free control, incorporating error feedback, is presented based on the error feedback principle. Subsequently, a novel control strategy is introduced by delving into PID neural network (NN) recognition and control theory, merging PID NN control with multi-innovation feedback control. Through meticulous mathematical derivation, the proposed strategy is proven to ensure system stability. Compared with traditional NN PID controllers, the convergence rate of the proposed scheme is 50 s faster and the steady-state errors are limited to ±1. [ABSTRACT FROM AUTHOR]

In today's automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods. [ABSTRACT FROM AUTHOR]

Traditional methods often ignore the direct influences of vehicle vibration on the flywheel battery system, which leads to an inaccurate analysis of the dynamic performance of the flywheel battery system and its control effect. Therefore, to make up for the deficiencies of existing studies, a more accurate dynamic performance analysis method and efficient control parameter adjustment algorithm for flywheel batteries based on automotive direct action are proposed in this study. First, the influence of road conditions and vehicle driving conditions on the stability of a vehicle is analyzed primarily. Then, the vibration signal generated by the vehicle is transmitted to the vehicle's magnetic flywheel battery system for analysis, and the accuracy of the analysis process is realized. Then, according to the stability analysis results for the direct action of the vehicle and the actual PID controller, the control parameter adjustment algorithm is summarized using the curve-fitting method. Finally, a performance test is carried out on the mobile experimental platform. Good experimental results show that the flywheel can quickly return to its equilibrium position and effectively reduce the influence of interference from road conditions and different working conditions and improve the robustness. Therefore, the correctness of the theoretical analysis and parameter adjustment method proposed in this paper was effectively verified. [ABSTRACT FROM AUTHOR]

As one of recently proposed optimization algorithm, sine cosine algorithm (SCA) suffers from the problems of skipping over true solutions, stagnating in local optima and premature convergence. Therefore, an improved sine cosine algorithm with heterogeneous subpopulations (HSISCA) is developed in this paper. The population of HSISCA is divided into two subpopulations which comply with different search mechanisms and information sharing rules. One subpopulation is specified to enhance exploration by incorporating Levy flight and random search guidance into SCA, while the other is specified to enhance exploitation by adaptive differential mutation operator. The exploration subpopulation is denied access to the information of exploitation subpopulation, but the exploitation subpopulation is allowed access to the information of exploration subpopulation. Moreover, the greedy selection is applied to each individual to preserve useful information. HSISCA is tested on the CEC2014 and CEC2017 benchmark functions, and is used for designing fractional order PID (FOPID) controller. The results confirm the better performance of HSISCA compared to other competitive algorithms, and demonstrate the effectiveness of HSISCA in designing FOPID controller for complex systems. [ABSTRACT FROM AUTHOR]

Proportional Integral Derivative (PID) control is the main control method in the process of agricultural water and fertilizer regulation, and its parameter setting directly affects the control effect of water and fertilizer regulation. However, the traditional PID parameters are adjusted manually such as using the critical proportionality method, which is time-consuming and difficult to achieve optimal control effects. To solve the optimal combination of PID control parameters and improve the control effect of water and fertilizer regulation, a partial attraction adaptive firefly algorithm (PAAFA) is proposed in this paper. Specifically, a partial attraction strategy is designed to speed up the convergence of the PAAFA and reduce the oscillation problem at the late stage of the algorithm. In addition, an adaptive inertia weight operator is proposed to balance the global search capability and local search capability of PAAFA and avoid the algorithm from trapping in the local optimum. Subsequently, to test the performance of PAAFA, the algorithm is subjected to a series of simulation experiments and bench tests with the latest methods, i.e., genetic algorithm (GA), Adaptive genetic algorithm (AGA), and firefly algorithm (FA) applied to PID parameter optimization problems. The simulation results demonstrate that the regulation times of the response curve of PAAFA-based PID control are reduced by 22.75%,10.10%and 20.61%, respectively, compared with GA, AGA, and FA. The bench test results show that the PAAFA-based PID control has the smallest relative error, and best control accuracy compared to GA, AGA, and FA, with an average relative error reduction of 3.99, 2.42, and 3.50 percentage points respectively. [ABSTRACT FROM AUTHOR]

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This paper is an attempt to accomplish a performance analysis of the different control techniques on current spike reduction by means of magnetization level control in the primary winding on the medium frequency transformer based DC spot welding system. Current Spike reduction is an important factor to be considered while spot welding systems are concerned. The current control technique is a piecewise linear control technique that is inspired from the DC-DC converter control algorithms to register a novel current spike reduction method in the MFDC spot welding applications. The different controllers that were used for the current spike reduction by means of magnetization level control of the overall applications involve the hysteresis controller, Proportional Integral (PI) controller. Care is taken such that the current control technique would maintain a reduced current spike in the primary current of the transformer while it reduces the Total Harmonic Distortion. In this research, intelligent control system is introduced by using Adaptive Neuro Fuzzy Inference System (ANFIS). The performance of ANFIS controller is presented considering the parameters, percentage of current spike reduction, and THD. It is proved that current spikes are controlled successfully and has less TM THD than the above mentioned conventional controllers. Matlab/Simulink based simulation is carried out for the MFDC with 220 KVA welding transformer in resistance spot welding systems. [ABSTRACT FROM AUTHOR]

This paper presents a survey on optimization techniques used to tune the controller parameters on Automatic Voltage Regulator (AVR) system. AVR is a device used to adjust the terminal voltage of synchronous generator. Since output voltage has slow response and instability, a Controller is used to improve stability and to get better response by minimizing maximum overshoot, reducing rise time, reducing settling time and improving steady state error. Proportional-Integral- Derivative (PID), Fraction Order PID (FOPID) and fuzzy logic are some examples of controllers which are used. Optimization techniques are used to tune the Controller due to nonlinear loads, time delays, variable operating points and others. There are different types of optimization techniques as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Harmony Search Algorithm (HSA), Local Unimodal Sampling (LUS), Teaching Learned Based Optimization (TLBO), and others. Researches are performed on different optimization techniques to improve terminal voltage response and stability. [ABSTRACT FROM AUTHOR]

Proportional-integral-derivative (PID) controllers are extensively used in engineering practices for their simple structures, robustness to model errors, and easy operations. At present, there is a great variety of PID controllers. Companies have developed intelligent regulators with functions for automatically tuning PID parameters. For present PID controllers, strategies such as intelligence, self-adaptation, and self-correction are extended to transmission PID. PID controllers and corresponding improved ones are utilized in 90% of industrial control processes. In this paper, PID control algorithms are summarized. This paper focuses on advanced control strategies such as PID control, predictive PID control, adaptive PID control, fuzzy PID control, neural network PID control, expert intelligent PID control, PID control based on genetic algorithms, and PID control based on ant colony algorithms. Besides, these kinds of algorithms are compared, and prospects of PID algorithms are forecast at the end of this paper. [ABSTRACT FROM AUTHOR]

For a fatigue testing machine, when the aerospace flexible thermal connection components are subjected to tension and compression testing on the machine, the actual vibration frequency and amplitude are lower than the setting value. In order to solve this problem, this paper proposes an optimized PID controller based on improved non-dominated sorting genetic algorithm (reference point-based non-dominated sorting genetic algorithm, NSGA-III), to promote the test speed and efficiency of the test system. First, the stability in the frequency domain is taken as the constraint condition, the overshoot, adjustment time and ITAE of the system are taken as the optimization targets, and the parameters Kp and Ki are used as the design variables to establish a multi-objective optimization model. Secondly, in view of the fixed rate of crossover and mutation operators used by NSGA-III algorithm, which is prone to problems such as premature convergence and poor search ability, an adaptive crossover and mutation operator improved non-dominated sorting genetic algorithm (NSGA-III) is proposed. Finally, MATLAB/Simulink conducts system simulation and compares the PID controller, NSGA-III optimized PID controller and improved NSGA-III optimized PID controller. The results show that the NSGA is improved when the input step response imposes disturbance. Compared with PID controller and NSGA-III optimized PID controller, the adjustment time of NSGA-III optimized PID controller is reduced by 0.21 s and 0.03 s, respectively, which shows that the improved NSGA-III optimized PID controller has stable position output and is more fast adjustment response and better anti-interference ability. Under the input sinusoidal response, the maximum position error of the improved NSGA-III is 0.05, which is 0.38 compared to the maximum position error of the PID controller, and the maximum position error of the NSGA-III optimized PID controller is 0.1, which reduces 86% and 50%, respectively. Improving NSGA-III to optimize the PID controller can significantly improve the dynamic tracking accuracy. [ABSTRACT FROM AUTHOR]

This paper presents Load Frequency Control (LFC) strategy for Microgrid (MG) systems, proposing a strategy based on a PID optimized controller using computational intelligence (CI) technique. The MG system, relying solely on Renewable Energy Sources(RES) is modeled in SIMULINK. A novel multi-objective function comprising of Weighted Integral Square Error, WISE and Weighted Integral Absolute Square Error WIASE (WISE+WIASE) is proposed to enhance system performance. Using Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO), the Proportional, Integral and Derivative controller parameters are tuned. Simulation results revealed the superiority of the PSO-PID with the least steady state error of 4.1560e-07 and overshoot 0.0611 respectively over the APSO-PID with steady state error value of 4.8144e-07 and overshoot of 0.1334 respectively. PSO-PID also outperformed APSO-PID across other indices (ITAE, IAE, ITSE and ISE) making the PSO-PID to excel over the APSO-PID in terms of steady state error and overshoot. The controllers proved to be robust in various conditions with the PSO-PID controller exhibiting more robustness and stability than the APSO-PID controller. Comparing PSO and APSO algorithms, PSO outperformed APSO in terms of robustness because for five number of runs in both algorithms, the PSO algorithm presents a more quality and consistent results and also display a better convergence than the APSO algorithm. Conversely, APSO surpasses PSO in fast computational time and convergence speed. These findings underscore the effectiveness of the proposed LFC strategy for RES based Microgrid systems, providing valuable insights into comparative performance of PSO and APSO algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Improved Artificial Electric Field Algorithm (IAEF)-based AGC in a multi-area power system to minimize the area control error and to obtain an optimal solution. Here, four area AGC systems equipped with the PID controller are examined for the study processes. Also, an optimal gain value for the PID controller is obtained by employing an IAEFA; thus minimizing the area control error (ACE). In addition to this, nine different test functions are employed to evaluate the performances of the proposed IAEF-based AGC of a multi-area power system and are compared with several optimization approaches such as AEF algorithm, BBO Algorithm, PSO Algorithm as well as DE Algorithm. Also, the performance evaluation based on three different simulation strategies such as simulation based on time domain, Random disturbances of the AGC system as well as System Dynamics for maximum frequency deviation and Percentage Improvement for a four area power system. Here, the maximum frequency deviations for four areas are −0.0132, −0.0145, −0.0122 and −0.0120, respectively. Moreover, the percentage improvements with respect to the IAEF with respect to the PID controllers are 67.32%, 57.21%, 47.26% and 55.47%, respectively. The experimental analysis reveals that the proposed approach provides very less tie-line control error and better optimal tuning of the PID controller when compared with all other approaches. [ABSTRACT FROM AUTHOR]

This paper discusses the merging of two optimization algorithms, atom search optimization and particle swarm optimization, to create a hybrid algorithm called hybrid atom search particle swarm optimization (h-ASPSO). Atom search optimization is an algorithm inspired by the movement of atoms in nature, which employs interaction forces and neighbor interaction to guide each atom in the population. On the other hand, particle swarm optimization is a swarm intelligence algorithm that uses a population of particles to search for the optimal solution through a social learning process. The proposed algorithm aims to reach exploration-exploitation balance to improve search efficiency. The efficacy of h-ASPSO has been demonstrated in improving the time-domain performance of two high-order real-world engineering problems: the design of a proportional-integral-derivative controller for an automatic voltage regulator and a doubly fed induction generator-based wind turbine systems. The results show that h-ASPSO outperformed the original atom search optimization in terms of convergence speed and quality of solution and can provide more promising results for different high-order engineering systems without significantly increasing the computational cost. The promise of the proposed method is further demonstrated using other available competitive methods that are utilized for the automatic voltage regulator and a doubly fed induction generator-based wind turbine systems. [ABSTRACT FROM AUTHOR]

Data-driven control methods are strong tools due to their predictions for controlling the systems with a nonlinear dynamic model. In this paper, the Koopman operator is used to linearize the nonlinear dynamic model. Generating the Koopman operator is the most important part of using the Koopman theory. Dynamic mode decomposition (DMD) is used to obtain eigenfunction for producing the Koopman operator. Then, a fractional order PID (FOPID) controller is applied to control the linearized dynamic model. A swarm intelligence bat optimization algorithm is utilized to tune the FOPID controller's parameters. Simulation results on micro-electromechanical systems (MEMS) gyroscope under conventional PID controller, FOPID, Koopman-based FOPID controller (Koopman-FOPID), and Koopman-FOPID control optimized by bat algorithm (Koopman-BAFOPID) show that the proposed Koopman-BAFOPID controller has better performance in comparison with three other controllers in terms of high tracking performance, low tracking error, and low control efforts. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for tuning the parameters of multi-input multi-output (MIMO) fractional-order PID (FOPID) controller. The aim of the proposed method is to calculate the parameters of this controller such that the rise time and steady-state errors of the feedback system are minimized without violating the predetermined stability margins. Mathematically, this problem is formulated as maximizing the spectral norm of the open-loop transfer matrix at zero frequency subject to a constraint on the H∞-norm of the sensitivity function. This problem is nonlinear in parameters of the MIMO FOPID, which can be solved using the iterative algorithm developed in this paper based on non-smooth H∞ synthesis. [ABSTRACT FROM AUTHOR]

In this paper, we present the development of a non-linear model predictive controller for the trajectory tracking of a quadrotor using the CasADi optimization framework. The non-linear dynamic model of the quadrotor was derived using Newton–Euler equations, and the control algorithm and drone dynamics were wrapped in Matlab. The proposed controller was tested by simulating the tracking of a 3D helical reference trajectory, and its efficiency was evaluated in terms of numerical performance and tracking accuracy. The results showed that the proposed controller leads to faster computational times, approximately 20 times faster than the Matlab toolbox (nlmpc), and provides better tracking accuracy than both the Matlab toolbox and classical PID controller. The robustness of the proposed control algorithm was also tested and verified under model uncertainties and external disturbances, demonstrating its ability to effectively eliminate tracking errors. [ABSTRACT FROM AUTHOR]

The wireless sensor network is a collection of diverse sensor nodes in which data is communicated via physical sensors and relayed using self-configured protocols. The network becomes congested because myriad nodes are involved in the sensing and data transmission. Congestion decreases the packet delivery ratio (PDR) virtue of significant packet drop and increases transmission delay. The lost packets are retransmitted at the cost of additional energy and time, which provokes the reduction in network performance and the extravagance of energy resources. Traditional techniques for congestion control are inflexible and lack adaptive capability. To overcome these constraints, this paper proposes a PFP-PID controller (Progressive Fuzzy Particle Swarm Optimization (PSO) Proportional Integral Derivative (PID)). The initial position of the reference particle is determined using fuzzy logic, which accelerates PSO convergence by escaping numerous initial iterations. Additionally, this hybrid mechanism based on PSO and fuzzy logic controller is employed to generate the ideal PID controller design with rapid convergence, resulting in an optimized transmission rate for sensor nodes. The PFP-PID is implemented to alleviate congestion by simulating it in network simulator (NS3) and comparing it to cuckoo fuzzy PID (CFPID), PSO-neural PID, fuzzy-PID, and traditional PID. The simulation results demonstrate that the PFP-PID is scalable and has outclassed existing mechanisms and significantly improved performance, increasing PDR by 4.89%, minimizing packet drop, delay, and active queue length deviation by 36.83%, 25.32%, and 10.84%, respectively, against CFPID. [ABSTRACT FROM AUTHOR]

In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms. [ABSTRACT FROM AUTHOR]

A major problem in power systems is achieving a match between the load demand and generation demand, where security, dependability, and quality are critical factors that need to be provided to power producers. This paper proposes a proportional–integral–derivative (PID) controller that is optimally designed using a novel artificial rabbits algorithm (ARA) for load frequency control (LFC) in multi-area power systems (MAPSs) of two-area non-reheat thermal systems. The PID controller incorporates a filter with such a derivative coefficient to reduce the effects of the accompanied noise. In this regard, single objective function is assessed based on time-domain simulation to minimize the integral time-multiplied absolute error (ITAE). The proposed ARA adjusts the PID settings to their best potential considering three dissimilar test cases with different sets of disturbances, and the results from the designed PID controller based on the ARA are compared with various published techniques, including particle swarm optimization (PSO), differential evolution (DE), JAYA optimizer, and self-adaptive multi-population elitist (SAMPE) JAYA. The comparisons show that the PID controller's design, which is based on the ARA, handles the load frequency regulation in MAPSs for the ITAE minimizations with significant effectiveness and success where the statistical analysis confirms its superiority. Considering the load change in area 1, the proposed ARA can acquire significant percentage improvements in the ITAE values of 1.949%, 3.455%, 2.077% and 1.949%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering the load change in area 2, the proposed ARA can acquire significant percentage improvements in the ITAE values of 7.587%, 8.038%, 3.322% and 2.066%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering simultaneous load changes in areas 1 and 2, the proposed ARA can acquire significant improvements in the ITAE values of 60.89%, 38.13%, 55.29% and 17.97%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. [ABSTRACT FROM AUTHOR]

In this paper, a low-order approximation (LOA) of fractional order PID (FOPID) for an automatic voltage regulator (AVR) based on the modified artificial bee colony (ABC) is proposed. The improved artificial bee colony (IABC) high-order approximation (HOA)-based fractional order PID (IABC/HOA-FOPID) controller, which is distinguished by a significant order approximation and by an integer order transfer function, requires the use of a large number of parameters. To improve the AVR system's performance in terms of transient and frequency response analysis, the memory capacity of the IABC/HOA-FOPID controller was lowered so that it could fit better in the corrective loop. The new robust controller is named the improved artificial bee colony (IABC) low-order approximation (LOA)-based fractional order PID (IABC/LOA-FOPID). The performance of the proposed IABC/LOA-FOPID controller was compared not only to the original ABC algorithm-tuned PID controller, but also to other controllers tuned by state-of-the-art meta-heuristic algorithms such as the improved whale optimization algorithm (IWOA), particle swarm optimization (PSO), cuckoo search (CS), many optimizing liaisons (MOL), genetic algorithm (GA), local unimodal sampling (LUS), and the tree seed algorithm (TSA). Step response, root locus, frequency response, robustness test, and disturbance rejection abilities are all compared. The simulation results and comparisons with the proposed IABC/LOA-FOPID controller and other existing controllers clearly show that the proposed IABC/LOA-FOPID controller outperforms the optimal PID controllers found by other algorithms in all the aforementioned performance tests. [ABSTRACT FROM AUTHOR]

A decentralized PI/PID controller based on the frequency domain analysis for two input two output (TITO) coupled tank systems is exploited in this paper. The fundamentals of the gain margin and phase margin are used to design the proposed PI/PID controller. The basic objective is to keep the tank at the predetermined level. To satisfy the design specifications, the control algorithm is implemented for decoupled subsystems by employing a decoupler. First-order plus dead time (FOPDT) models are obtained for the decoupled subsystems using the model-reduction technique. In addition, the control law is realized by considering the frequency domain analysis. Further, the robustness of the controller is verified by considering multiplicative input and output uncertainties. The proposed method is briefly contrasted with existing techniques. It is envisaged that the proposed control algorithm exhibits better servo and regulatory responses compared to the existing techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design of a proportional filtered-integral (P-FI) controller whose parameters are auto-tuned using the backpropagation neural networks (BPNN) algorithm. The proposed controller is designed to address the main challenges posed by a class of complex systems characterized by uncertain high-gain and pure integrator dynamics, including high-frequency noise amplification and poor robustness against parameter variations. Designing such a controller involves three main steps: First, a proportional–integral–derivative (PID) controller is designed, with its parameters auto-tuned online using the BPNN algorithm, resulting in the primary (BPNN-PID) controller. Second, the obtained parameters of the previous controller are utilized to compute those of a low-pass filter offline. This filter is then cascaded with the integral parts of a PID controller, forming a P-FI controller structure. This configuration introduces a phase lead within a specific frequency range without amplifying high-frequency noise, overcoming the primary disadvantage of the derivative term. Finally, the parameters of the resulting P-FI controller are again auto-tuned online using the BPNN algorithm, resulting in the final robust (BPNN-P-FI) controller. This novel controller structure and its parameters tuning procedure, based on a two-stage BPNN learning approach, constitute the main contributions of this paper. Simulation results demonstrate the superiority of the proposed controller in terms of time-domain performance, sensor noise attenuation, and closed-loop robustness compared to those obtained with the BPNN-PID and optimally tuned PID controllers. [ABSTRACT FROM AUTHOR]

This paper presents a new modified variable step size Fractional Order Incremental Conductance (FOIC) with maximum power point tracking using Fractional Order PID controller tuned by bio‐inspired Particle Swarm Optimization (PSO) to find optimal gain values of fractional integrator order (λ) and fractional derivative order (μ). The classical incremental conductance and FOIC show drawbacks under changing irradiance, oscillation around maximum power point (MPP) which decreases its convergence speed. To resolve these prone a variable step size FOIC is proposed to achieve an adaptive duty cycle via tuning of FOPID through PSO. The robustness of the proposed technique is judged by its steady‐state and dynamic response with fast converges, less response time, overshoot, and ripples under changing environmental conditions. Furthermore, the performance of the proposed technique is evaluated by comparing it with a fixed step size conventional incremental conductance algorithm and FOIC. [ABSTRACT FROM AUTHOR]

The objective of this paper is to design a load frequency controller for Autonomous hybrid power system with heuristic optimization approach like Differential Evolution Algorithm. A novel controller such as TID (Tilt-Integral-Derivative) Controller is employed to suppress the oscillations in the system that occurs due to variation or disturbances. The gain values of the controller are obtained by the DE algorithm. In order to alleviate the effective nature of the proposed controller, the responses are compared with the conventional PID controller in all test conditions applied. The DE tuned TID controller resembles stronger robustness properties than the conventional PID controller. [ABSTRACT FROM AUTHOR]

Since the swing of the lifting load and the positioning of the trolley during the operation of a bridge crane seriously affect the safety and reliability of its work, we have not only designed Proportional Integral Derivative (PID) controllers for the anti-swing and positioning control but also proposed a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm to optimize the gains of the controllers. In updating the PSO algorithm, a nonlinear adaptive method is utilized to update the inertia weight and learning coefficients, and the SA algorithm is also integrated when the PSO algorithm is searching for a global optimal solution, to reduce the probability of falling into the local optimal solution. The simulation results demonstrate that the PSO–SA algorithm proposed in this paper is prone to be a more effective method in searching for the optimal parameters for the controllers, compared with three other algorithms. As shown by the experimental results, the swing angle stabilization time of the novel algorithm is 6.9 s, while the values of the other algorithms range from 10.3 to 13.1 s under a common working condition. Simultaneously, the maximum swing angle of the novel algorithm is 7.8°, which is also better than the other algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an improved sine–cosine algorithm (ISCA) based 2-DOF-PID controller for load frequency control. A three-area test system is built for study, while some physical constraints (nonlinearities) are considered for the investigation of a realistic power system. The proposed method is used as the parameter optimizer of the LFC controller in different scenarios. The 2-DOF-PID controllers are used because of their capability of fast disturbance rejection without significant increase of overshoot in set-point tracking. The 2-DOF-PID controllers' efficacy is observed by examining the responses with the outcomes obtained with PID and FOPID controllers. The simulation results with the suggested scheme are correlated with some of the existing algorithms, such as SCA, SSA, ALO, and PSO in three different scenarios, i.e., a disturbance in two areas, in three areas, and in the presence of physical constraints. In addition, the study is extended to a four-area power system. Statistical analysis is performed using the Wilcoxon Sign Rank Test (WSRT) on 20 independent runs. This confirms the supremacy of the proposed method. [ABSTRACT FROM AUTHOR]

China has had the highest fertilizer use rate in the world for years, but today a large number of farmlands still use traditional manual fertilizer application methods, which rely entirely on personal experience and not only cause the waste of fertilizer and water resources but also make the local ecological environment polluted. This paper researches and designs a BP neural network PID controller based on PSO optimization to address the above problems. The PSO algorithm is used to optimize the initial weights of the BP neural network, and then optimize the control parameters of the PID to achieve accurate control of the liquid fertilizer flow. A precision fertilizer control system based on the STM32 microcontroller was also developed, and the performance of this controller was verified in tests. The results showed that compared with the conventional PID controller and BP neural network-based PID controller, this controller had good control accuracy and robustness, the average maximum overshoot was 6.35%, and the average regulation time was 41.17 s; when the fertilizer application flow rate was 0.6 m3/h, the shortest adjustment time is 30.85 s, which achieves the effect of precise fertilizer application. [ABSTRACT FROM AUTHOR]

Gong, X., 2019. Optimization of the power generation control process of hydraulic turbine set based on the improved BFO-PSO algorithm. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 227–231. Coconut Creek (Florida), ISSN 0749-0208. In light of the problem that it is hard to analyze the non-linear and time-varying model in the traditional hydro-generator system identification, based on the PID control parameters, this paper proposes a hydro-turbine governing system using the improved BFO-PSO algorithm, and introduces the sliding mode variable structure into the proposed improved governing system. The research results show that: the proposed BFO-PSO model has integrated the advantages of the two algorithms and has been successfully applied in the parameter optimization of the hydraulic turbine set. This paper also studies the selection of indices for optimization of PID parameters. Considering the deficiencies in the PID parameters in previous research, this paper proposes an improved fitness evaluation method, and also puts forward the concepts of stability and instability parameter sets and applies them in the stable search strategy, which effectively improves the local search capability of the model algorithm, and guarantees the stability and accuracy of the hydro-generator model. The design of multi-control-input hydro-turbine state equations can effectively solve the steady-state errors in the simulation process and explain the generation mechanism of such errors. The simulation results show that the optimization result of this algorithm significantly shortens the ITAE transition rising time of the hydro-turbine set and that the target population has been moving towards the global optimal solution, thus increasing the searching ability of the algorithm. The curve fluctuates slightly and reaches the steady state in a very short time. [ABSTRACT FROM AUTHOR]

Direct Current (DC) motors are broadly used in various industrial applications such as robotics, automobiles, toys and for many other motoring purposes. This is attributable to their extraordinary flexibility, durability and low implementation cost. It is essential to control the speed, position, torque and other variables of the DC motor to achieve the needed performance depending on the area of application. Many classical techniques have been used in the past to control the DC motor, however, such methods typically take a long time, particularly when used for complex nonlinear systems. The application of metaheuristic algorithms as a means of implementing Artificial Intelligence (AI) in this area has proven to be highly effective in overcoming these shortcomings. In recent decades, metaheuristic algorithms have become increasingly prevalent due to their tremendous success in addressing a number real-world optimization challenges in various fields of human activities extending from economic, pharmaceutical and industrial applications to intellectual applications. This review, therefore, presents the optimization of the PID controller with metaheuristic algorithms for controlling the DC motor drives. A short description for each algorithm is presented along with papers published in various renowned journals. For a robust review, the application of various forms of PID controller, as well as different types of DC motors are examined. Finally, the paper presents some open issues and future directions for research. [ABSTRACT FROM AUTHOR]

The magnetorheological elastomer (MRE) is a kind of smart material, which is often processed as vibration isolation and mitigation devices to realize the vibration control of the controlled system. The key to the effective isolation of vibration and shock absorption is how to accurately and in real time determine the magnitude of the applied magnetic field according to the motion state of the controlled system. In this paper, an optimal fuzzy fractional-order PID (OFFO-PID) algorithm is proposed to realize the vibration isolation and mitigation control of the precision platform with MRE devices. In the algorithm, the particle swarm optimization algorithm is used to optimize initial values of the fractional-order PID controller, and the fuzzy algorithm is used to update parameters of the fractional-order PID controller in real time, and the fractional-order PID controller is used to produce the control currents of the MRE devices. Numerical analysis for a platform with the MRE device is carried out to validate the effectiveness of the algorithm. Results show that the OFFO-PID algorithm can effectively reduce the dynamic responses of the precision platform system. Also, compared with the fuzzy fractional-order PID algorithm and the traditional PID algorithm, the OFFO-PID algorithm is better. [ABSTRACT FROM AUTHOR]

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative simplified two-stage algorithm for precise and durable regulation of the buck power systems. A critically damped of response is captured and synthesized in the first stage via a second order model. In the second stage, the desired robust response of the system is enforced and stirred by a new parameterless controller. The main feature of this controller is simplicity, which is facilitated by its independence from tunable control parameters. Practical numerical simulations that take into account the impact of plant uncertainties and noise measurements with varying variances are used to thoroughly assess the feasibility of the proposed controller. To highlight the main contribution of this paper, the devised control algorithm is juxtaposed with established controllers, namely the PSO-tuned versions of the sliding mode, backstepping, and PID controllers, alongside other cut cutting-edge controllers, revealing its prowess performance superiority and heightened structural simplicity. Finally, the suggested structure is integrated in a standalone photovoltaic system for appraisal of PV emulation potency. It is found to preserved a superior performance in the context of PV emulation as compared to existing emulators, achieving a dynamic efficiency of 97.85 % in the specific case of MPPT. [ABSTRACT FROM AUTHOR]

In this paper, a new fractional-order fuzzy controller named proportional fuzzy ا integral fuzzy + deferential fuzzy (FP + Fla + FDp) is proposed. The proposed controller is single gain, integrate, derivative and fractional order in its structure. The controller has a structure with two adjustable fractions and is simply designed. This controller performs the stability process in a short time. The response time can also be changed by adjusting the coefficient of time scale a and p, which makes it more flexible and efficient compared to the classic PID controller. On the other hand, in the design of the proposed controller, the optimal adjustment of the controller gain and fuzzy members has turned into an optimization problem, which is done by a hybrid algorithm based on the vcs and the HBC colony (ABC) based on a function in the time domain. In the proposed hybrid algorithm, attempts have been made to take advantage of local and final search as the strengths of these controllers to dramatically reduce the possibility of locating in local points. Investigations of different load variation scenarios, better performance of the proposed controller facing load disturbances from the root mean square, rise and fall time, will show the number of oscillations, and the fall time of the frequency changes. [ABSTRACT FROM AUTHOR]

This paper proposes a new control law for designing an optimal proportional‐integral‐derivative (PID) controller for flexible automatic voltage regulator (AVR) system using modified neural network algorithm (MNNA). First, the exploration capability of neural network algorithm (NNA) is enhanced by addition of a learning factor, α in MNNA. Then, to evaluate the performance of MNNA in terms of its exploration and exploitation capabilities, extensive statistical analysis has been carried out on 23 benchmark functions consisting of unimodal, multimodal and fixed dimension multimodal functions against 12 state of the art algorithms. The results are encouraging and marks the superiority of MNNA against NNA as well as 11 other state of the art techniques. It is followed by application of Kharitonov theorem as a design tool to derive the Interval AVR system. Next, NNA and MNNA have been used for tuning of PID controller parameters in such a way that maximum value of the closed loop eigen values of K‐extreme polynomials is minimized. Further to show the robustness of the proposed methods, the results are taken on set point tracking, noise suppression, load disturbance rejection, and minimum controller effort utilized by these controllers and compared against seven state of the art techniques namely PSO, GA, ABC, MOEO, NSGA‐II, FSA and variants of constrained GA. The results demonstrate the high performance of the PID controller optimized using MNNA for control of I‐AVR and guarantees stability for a wider range of system parameter uncertainty. [ABSTRACT FROM AUTHOR]

This paper has discussed the study of the performance of dynamic voltage restorer (DVR) taking different voltage sag conditions in the supply voltage of the distribution system for linear and non-linear load. DVR is employed to mitigate the voltage sag. Proportional and integral (PI) controller is used for the control of DVR to mitigate the voltage sag. Synchronous reference frame theorybased control algorithm has been implemented for generating reference voltages. In this paper, an efficient improved particle swarm optimisation technique for optimizing the gain parameters of the PI controller has been proposed. The performance and suitability of DVR is validated through MATLAB simulation results and use of Sim Power Systems Blocksets. Integral squared error is implemented to check the performance and suitability of DVR. Extensive results of the proposed method are presented and compared with conventional Ziegler Nichols and genetic algorithm methods of tuning gain parameters of PI controllers. [ABSTRACT FROM AUTHOR]

Summary: Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In this paper, we demonstrate the capabilities of a combined L1 adaptive control and iterative learning control (ILC) framework to achieve high‐precision trajectory tracking in the presence of unknown and changing disturbances. The L1 adaptive controller makes the system behave close to a reference model; however, it does not guarantee that perfect trajectory tracking is achieved, while ILC improves trajectory tracking performance based on previous iterations. The combined framework in this paper uses L1 adaptive control as an underlying controller that achieves a robust and repeatable behavior, while the ILC acts as a high‐level adaptation scheme that mainly compensates for systematic tracking errors. We illustrate that this framework enables transfer learning between dynamically different systems, where learned experience of one system can be shown to be beneficial for another different system. Experimental results with two different quadrotors show the superior performance of the combined L1‐ILC framework compared with approaches using ILC with an underlying proportional‐derivative controller or proportional‐integral‐derivative controller. Results highlight that our L1‐ILC framework can achieve high‐precision trajectory tracking when unknown and changing disturbances are present and can achieve transfer of learned experience between dynamically different systems. Moreover, our approach is able to achieve precise trajectory tracking in the first attempt when the initial input is generated based on the reference model of the adaptive controller. [ABSTRACT FROM AUTHOR]

This paper deals with the load frequency control (LFC) of a three unequal area system with multiple energy sources such as thermal-geothermal, thermal-wind energy system, and thermal-hydro system. An attempt has been made for the first time to utilize cascade integral double derivative and proportional-integral-derivative (IDD-PID) controller for LFC application. A new and powerful evolutionary algorithm called as exchange market algorithm is applied for optimizing the controller gains using an integral square error technique. The investigation is carried out on the dynamic performances of the proposed cascade IDD-PID controller against those of controllers such as PI and PID for a step load perturbation of 1% in area 1. Analysis of results depicts the better response of the proposed cascade IDD-PID controller compared to PI and PID controller responses. In addition, the cascade IDD-PID controller also provides superior dynamic performance even for the application of random load pattern in area 1 which ascertains the robustness of the proposed cascade controllers in containing the area frequency oscillations and tie line power deviations. An unequal three area system with multiple-energy sources is developed for LFC operation. A new cascade IDD-PID controller is introduced for LFC operation. Exchange market algorithm is applied for the first time for tuning the proposed cascade controllers. Cascade IDD-PID controller results in better dynamic performance compared to PI and PID controllers. [ABSTRACT FROM AUTHOR]

An airborne computer system is avionics-based model that consists of an electronic system and a specifically designed equipment. In this paper, we propose a collision-free path finding flight strategy that enhances the output performance of a drone system for free-navigation between buildings. This strategy is based on a reactive method that requires metaheuristic algorithms to process environmental information during drone flying. In addition, a robust controller is designed to achieve drone flight and reach the target point. The goal of this paper is to discuss the determination of the shortest flight path with the minimum cost function evaluation and this flight path should avoid the collision by using four heuristic algorithms including: Chaotic Particle Swarm Optimization (CPSO) algorithm, Fire-Fly (FF) algorithm, Bees (B) algorithm and a hybrid optimization algorithm that combines the CPSO, FF and B algorithms. To fly the drone and to follow the generated flightpath, six adaptive PID controllers are designed in order to control the highly nonlinear model and under-actuated drone system using an on-line CPSO algorithm to learn and tune the eighteen control gain parameters. The purpose of this control design is to precisely and quickly obtain robust thrust forces control actions to control the attitude and altitude of the drone model. The numerical results of the proposed flightpath-finding algorithms and robust control strategy confirm that the hybrid (CPSOFFB) flightpath algorithm has the minimum number of iterations and evaluation functions as well as free navigation and the shortest flight path length generated. Moreover, the proposed six adaptive PID controller results show that the four thrust forces control actions are smooth and accurately generated making the drone take off and follow the desired flight path quickly with the minimum number of cost function and around ±10 cm minimum error tracking translation location. The maximum overshoot of the altitude did not exceed 10 cm in the transient state and the orientation error of the drone is approximately zero in the steady-state. To demonstrate the effectiveness of the proposed flight path finding and control strategy, a comparison is made with the results other types of algorithms. [ABSTRACT FROM AUTHOR]

The oscillation of frequency can cause the generator to run out of sync in a power system. Therefore, load frequency control (LFC) is needed to reduce frequency oscillation and prevent out of sync operation. The LFC regulates the governor to balance the turbine speed with changes in the load of existing. In this paper, a proportional integral differential (PID) controller and a battery energy storage system (BESS) are added to an LFC to improve the frequency stability of a power system. The parameters of the PID controller and BESS are optimized using the Bat algorithm (BA) to attain a good coordination. The frequency performance analysis is done by introducing disturbance in the form of changes in load power. The simulation results show that the frequency deviation of the system with the PID controller and BESS, has a faster settling time and smaller overshoot value. The system performs better than those with only the PID controller or the BESS. In conclusion, the BA algorithm can be used to find optimal parameter values of the PID controller and BESS for a synchronized coordination of an LFC. [ABSTRACT FROM AUTHOR]

To select reasonable PID controller parameters and improve control performances of hydraulic systems, a variable weight beetle antenna search algorithm is proposed for PID tuning in the hydraulic system. The beetle antennae search algorithm is inspired by the beetle preying habit depending on symmetry antennae on the head. The proposed algorithm added the exponential equation mechanism strategy in the basic algorithm to further improve the searching performance, the convergence speed, and the optimization accuracy and obtain new iteration and an updating method in the global searching and local searching stages. In the PID tuning process, advantages of less parameters and fast iteration are realized in the PID tuning process. In this paper, different dimension functions were tested, and results calculated by the proposed algorithm were compared with other famous algorithms, and the numerical analysis was carried out, including the iteration, the box-plot, and the searching path, which comprehensively showed the searching balance in the proposed algorithm. Finally, the reasonable PID controller parameters are found by using the proposed method, and the tuned PID controller is introduced into the hydraulic system for control, and the time-domain response characteristics and frequency response characteristics are given. The results show that the proposed PID tuning method has good PID parameter tuning ability, and the tuned PID has a good control ability, which makes the hydraulic system achieve the desired effect. [ABSTRACT FROM AUTHOR]