Search

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]

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]

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]

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]

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]

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]

The search for a better approach to design a convenient controlling scheme is one of the main challenges of the research field related to power converters. This paper brings forward a significant contribution as part of this challenge. In this regard, novel approaches have been proposed for both controller scheme and tuning algorithm. In terms of controlling scheme, a fractional-order proportional–integral–derivative (FOPID) controller was used alongside the Bode's ideal transfer function to achieve optimum performance of a buck converter system. To achieve a greater performance of the FOPID controller, a novel metaheuristic algorithm, which consists of a balanced structure in terms of explorative and exploitative phases, has also been developed by hybridizing the hunger games search algorithm with simulated annealing technique. The proposed hybrid algorithm helped tuning the FOPID controller optimally. The proposed algorithm and controlling scheme have been proved to be a better approach for controlling a buck converter system in terms of time and frequency domains along with disturbance rejection. To further verify the effectiveness of the proposed hybrid algorithm–based controlling scheme, it was compared with other reported and capable approaches which have also confirmed the greater performance of the proposed approach. [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]

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]

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]

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 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]

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]

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]

This paper is concerned with the synthesis of proportional–integral–derivative (PID) controller according to the H ∞ optimality criterion for seesaw-cart system. The equations of dynamics are obtained through modelling a seesaw-cart system actuated by direct-current motor via rack and pinion mechanism using the Euler–Lagrange approach. The obtained model is linearised and synthesis of the PID controller for linear model is performed. An algorithm based on the sub-gradient method, the Newton method, the self-adapting backpropagation algorithm and the Adams method is proposed to calculate the PID controller gains. The proposed control strategy is tested and compared with standard linear matrix inequality (LMI)-based method on computer simulations and experimentally on a laboratory model. [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]

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]

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]

An effort is given in this paper, for the first time, to discuss the load-frequency control (LFC) problem of an interconnected power system by implementing a novel heuristic optimization method called quasi-oppositional-based JAYA (QOJAYA). The proposed algorithm houses the knowledge of quasi-oppositional-based learning to enhance convergence speed and find the best solutions to the LFC problem. To show the effectiveness, the proposed QOJAYA scheme is, individually, applied to a two-area multi-unit multi-source power plant and a three-area hydrothermal power plant considering the system nonlinearities. The structural simplicity and performance adequacy of the well-known proportional-integral (PI) controller enforces to optimally design it as a secondary controller at the first instant. Afterward, the dynamic stability of the concerned power systems is increased with a 2DOF-PID controller. The proposed algorithm is employed to search the optimal settings of the said controllers by minimizing the time-based single-valued fitness functions. Simulation results validate the success of the proposed QOJAYA scheme and demonstrate its dominance over original JAYA, teaching-learning-based optimization (TLBO), and other recently introduced optimization techniques in the state-of-the-art for the same power system. Finally, the robustness of the designed controller is established by performing sensitivity analysis considering parameters and loading uncertainties. [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]

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach. [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]

The torque-based proportional-integral-derivative (PID) controller proposed in this paper is operated on joint motors by minimizing the error between the target and actual angular displacements of that joint to achieve a superior control. On the basis of the aforementioned phenomenon, the execution effectiveness of the two-legged robot's gait can be controlled. The PID controller parameter tuning is a laborious and time-consuming process, and the controllers' performance depends on the gain values set for the controller. To overcome the said drawbacks, this study develops an adaptive-torque-based PID controller for a two-legged robot that can provide adaptive gains on the basis of the magnitude of input signal received at the input nodes of the neural network (NN). Furthermore, the structure of the feedforward NN has been optimized with the help of the modified chaotic invasive weed optimization (MCIWO) algorithm. Moreover, the concept of zero moment point has been employed to check the balance of the two-legged robot while walking on various terrains (that is, flat, stair, and sloped surfaces). The effectiveness of the developed controller has been verified in both simulations and practical. [ABSTRACT FROM AUTHOR]

In this paper, a new type of modified Smith predictor based on the H 2 and predictive PI control strategy is proposed. The modified Smith predictor not only has H 2 robust performance but also has a similar predictive PI control structure. By introducing a time delay term, the modified Smith predictor controller overcomes the shortcoming that the conventional control algorithm can only use the low-order approximation of time delay term to design the control algorithm. The modified Smith predictor controller's output is related to the current system error and related to the output in a period before the controller. Simultaneously, the modified Smith predictor controller is applied to conventional process systems based on dynamic optimization estimation in the case study to show absolute superiority over the nonpredictive control method (such as the classical PID control method). [ABSTRACT FROM AUTHOR]

Parameter tuning of PID controller for liquid level control of beer filling machine was studied in this paper, which can meet the demand of accurate controlling in beer production and improve the rapidity under the same conditions. Firstly, an improved genetic algorithm was proposed which has been verified by eight kinds of test functions. Simulation results revealed that, in comparation with other modified particle swarm optimization algorithm and modified genetic algorithm, the algorithm proposed in this work is not only capable to improve the convergence speed and precision under the same experimental conditions but also to improve the probability to converge to the optimal value. Finally, the proposed algorithm was applied to the parameter tuning of the PID controller of beer filling machine for liquid level control. Superior property had been obtained, which implied an effective improvement in the rapidity with the premise of steady-state error exclusion. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is well known and widely used optimization method based on swarm intelligence, and it is inspired by the behavior of honeybees searching for a high amount of nectar from the flower. However, this algorithm has not been exploited sufficiently. This research paper proposes a novel method to analyze the exploration and exploitation of ABC. In ABC, the scout bee searches for a source of random food for exploitation. Along with random search, the scout bee is guided by a modified genetic algorithm approach to locate a food source with a high nectar value. The proposed algorithm is applied for the design of a nonlinear controller for a continuously stirred tank reactor (CSTR). The statistical analysis of the results confirms that the proposed modified hybrid artificial bee colony (HMABC) achieves consistently better performance than the traditional ABC algorithm. The results are compared with conventional ABC and nonlinear PID (NLPID) to show the superiority of the proposed algorithm. The performance of the HMABC algorithm-based controller is competitive with other state-of-the-art meta-heuristic algorithm-based controllers in the literature. [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 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]

Highlights • This paper model and discuss the performance of a hybrid energy distributed power system (HEDPS) coordinated with reheat thermal power plant. • Dragonfly algorithm optimized 3DOF-PID controller is proposed to improve the frequency and power profiles of the modeled HEDPS. • Results obtained using Dragonfly algorithm is compared with classical and some recently published evolutionary algorithms. • Time varying step load perturbation, random wind perturbation, and parameter variations are considered to confirm feasibility and superiority of the designed controller. • Probability density function and statistical results are shown to validate the robustness of the proposed method. Abstract The objective of this paper is to study the dynamic stability of a hybrid energy distributed power system (HEDPS) subject to load and wind power variations. A three degree-of-freedom (3-DOF) proportional-integral-derivative (PID) controller is designed and implemented in the HEDPS to stabilize frequency and power fluctuations after the perturbation. For enhancing system dynamics, the parameters of the 3-DOF PID controller are optimized by using dragonfly algorithm (DA). The results are compared with the results obtained by Zeigler–Nichols tuning and some other well-known meta-heuristic algorithms. The efficacy of proposed DA over different reported algorithms is established in terms of convergence rate, minimum fitness value and dynamic performance of the system. The robustness of the 3-DOF PID-controller is ascertained with time-varying step load perturbation, random wind power perturbation, and under system parameter variation. The robust performance of proposed DA has also been established by performing statistical analysis. [ABSTRACT FROM AUTHOR]

This paper analyzed and discussed the nonlinear PI/PD control for wind energy conversion system (WECS) pitch controller design. The nonlinear PI/PD controller consists of a classical PI/PD controller and a nonlinear gain table. In this paper, the detailed design procedures of the controller including not only the selection of control parameters but also the formulation of nonlinear gain table were given. In order to verify the effectiveness and correctness of the algorithm, a detailed 2 MW simulation test platform based on MATLAB/SIMULINK environment was established. Meanwhile, some valuable conclusions were also obtained. The presented contents of this article have the reference value and guiding significance for practical engineering application. [ABSTRACT FROM AUTHOR]

This paper presents a new meta-heuristic bio-inspired optimization algorithm which is called Cuttle Fish Optimization algorithm (CFO). CFO is based on the color changing behavior of cuttlefish to find the optimal solution. The patterns and colors that are seen in cuttlefish are produced by reflected light from different layers of cells and it is the combination of certain cells at once that allows cuttlefish to possess such a large array of patterns and colors. In this paper the Solar Photo Voltaic (SPV) array is used which is receiving wide attention now days because the everlasting solar energy is the best alternative to the conventional energy sources. This work primarily focuses on the design and development of an efficient solar photovoltaic array utilizing a Landsman Converter with PID controller. Fuzzy logic controller and CuttleFish optimization techniques are used for the optimized control of the converter output voltage. The CuttleFish optimization technique has been found to provide better results for randomly varying atmospheric conditions as compared to the existing control schemes. This topology is designed and modeled in MATLAB/Simulink platform and its performance under varying environmental conditions is studied. Moreover the efficiency of the PV array for various control schemes is analyzed rigorously. [ABSTRACT FROM AUTHOR]

Summary: Most of the proposed algorithms for synchrophasor estimation are based on the Discrete Fourier Transform (DFT). However, this technique has well‐known limitations, such as the leakage effect. Alternatively, this paper presents a positive‐sequence synchrophasor measurement algorithm based on a Synchronous Reference Frame Phase‐Locked Loop (SRF‐PLL). The algorithm is modular and consists of four stages. The first one is the abc‐dq transform that, with the aid of the second stage, a Finite Impulse Response (FIR) provides the positive‐sequence of the grid voltages. The filter also helps to improve the signal‐to‐noise ratio. This filter class was chosen due to its characteristic of linear phase and constant group delay to avoid unwanted phase distortions at the measurements. The third stage is an SRF‐PLL based on a PID controller that estimates the amplitude and phase of the grid voltage's positive‐sequence component and the grid frequency. By assessing the dynamic condition tests, it was observed that the PLL could not comply with all the IEEE standard requirements. Therefore, a fourth stage was included to calculate the frequency, and Rate of Change of Frequency (ROCOF) based on the estimated positive‐sequence phase. Experimental results obtained with a test platform shown that the proposed algorithm complies with all IEEE C37.118.1‐2011 and C37.118.1a‐2014 standard requirements. [ABSTRACT FROM AUTHOR]

The objective of this research paper is to design a control system to optimize the operating works of the gantry crane system. The dynamic model of the gantry crane system is derived in terms of trolley position and payload oscillation, which is highly nonlinear. The crane system should have the capability to transfer the material to destination end with desired speed along with reducing the load oscillation, obtain expected trolley position and preserving the safety. Proposed controlling method is based on the proportional-integral-derivative (PID) controller with a series differential compensator to control the swinging of the payload and the system trolley movement in order to perform the optimum utilization of the gantry crane. Standard sine cosine optimization algorithm is one of the most-recent optimization techniques based on a stochastic algorithm was presented to tune the PID controller with a series differential compensator. Furthermore, the considered optimization algorithm is modified in order to overcome the inherent drawbacks and solve complex benchmark test functions and to find the optimal design's parameters of the proposed controller. The simulation results show that the modified sine cosine optimization algorithm has better global search performance and exhibits good computational robustness based on test functions. Moreover, the results of testing the gantry crane model reveal that the proposed controller with standard and modified algorithms is effective, feasible and robust in achieving the desired requirements. [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

In this study, an energy efficiency study has been carried out through the moment values of a quadrotor applied in the position control with the differential evolution algorithm (DE), which is under the evolutionary algorithms among meta-heuristic algorithms particle swarm optimization (PSO) which is among swarm-based algorithms and gravity search algorithm (GSA) among physics-based algorithms and charged system search (CSS) algorithm, which are all well-known in the literature. The efficient use of energy in electrically powered flying mechanisms is important in terms of completing its task. The aim of this study is to obtain proportional–integral–derivative (PID) coefficients that minimize the energy consumption caused by quadrotor moments. First of all, 12 PID coefficients calculated with traditional Ziegler Nicholes, and trying-error methods for the control of the quadrotor have been trained with DE, PSO, GSA and CSS algorithms and their cost values have been found for this problem. In the study, optimum PID coefficients have been found separately with each algorithm and torque data giving energy efficiency have been obtained by applying them to the state equations. Although the cost minimization convergence value obtained from DE does not look good compared to other algorithms, it has been observed that the moment values of the obtained PID coefficients in the control program are lower. In the study, it has been shown that the PID coefficients obtained from DE reach the target with approximately less energy than the maximum-moment value of the PSO, which provides the closest moment value. It can be thought that this value contributes to energy consumption in each orbital movement of the quadrotor. The simulation results show that the coefficients obtained from DE provide better energy efficiency and are superior to other algorithms compared in terms of energy efficiency. • PID coefficients for quadrotor control are optimized with DE,PSO,CSS,GSA algorithms • The torque values to be applied to each motor were adjusted with these coefficients • The total force applied with DEPID in the z direction is smaller than others • DEPID consumes less energy than the other algorithms in the paper [ABSTRACT FROM AUTHOR]

This paper introduces a fractional order tilt-integral-derivative (FOTID) controller which is structurally analogous to fractional order proportional-integral-derivative controller in a power system for solving automatic generation control (AGC) problem. It is optimized by a recent metaheuristic optimizer called pathfinder algorithm (PFA). An interconnected two-area power system model comprising of multi-sources like thermal, hydro and gas generating units including physical constraints namely, governor dead band (GDB) and generation rate constraint (GRC) are taken into consideration for the study. The efficiency of the proposed controller for AGC is shown by comparing it with PFA optimized tilt-integral-derivative (TID) and proportional-integral-derivative (PID) controllers with integral of time multiplied absolute error (ITAE) taken as the objective function. Simulation study supports the claim that the proposed controller provides better dynamic responses as compared to the others. Sensitivity and robust analyses are done to demonstrate the effectiveness of the proposed PFA optimized FOTID controller to a wide variation in system parameters, at different step load and random load disturbances. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear fractional order proportional integral derivative (NL-FOPID) for autonomous underwater vehicle (AUV) to solve the path tracking problem under the unknown disturbances (model uncertainty or external disturbances). The considered controller schemes are tuned by two improved swarm intelligence optimization algorithms, the first on is the hybrid grey wolf optimization with simulated annealing (HGWO-SA) algorithm and an improved whale optimization algorithm (IWOA). The developed algorithms are assessed using a set of benchmark function (unimodal, multimodal, and fixed dimension multimodal functions) to guarantee the effectiveness of both proposed swarm algorithms. The HGWO-SA algorithm is used as a tuning method for the AUV system controlled by NL-FOPID scheme, and the IWOA is used as a tuning algorithm to obtain the PID controller’s parameters. The evaluation results show that the HGWO-SA algorithm improved the minimal point of the tested benchmark functions by 1-200 order, while the IWOA improved the minimum point by (1-50) order. Finally, the obtained simulation results from the system operated with NL-FOPID shows the competence in terms of the path tracking by 1-15% as compared to the PID method. [ABSTRACT FROM AUTHOR]

The power quality and protection enhancement of wind turbine becomes an important paramount. This requires a competitive controller with high performances. For this purpose, the paper presents a novel hybrid controller named fractional fuzzy PID (FFPID) strategy tunning with an efficient metaheuristic optimization bat algorithm (BA) for controlling the wind turbine equipped with permanent magnet synchronous generator (WT-PMSG). The main control objectives are to drive WT-PMSG for extracting the maximum power point (MPPT) in the low range of weather conditions and further to protect the wind turbine in high-wind region using pitch angle control. The performance of proposed strategy is analysed for all regions low–high wind speed disturbances. The obtained results are obviously proved that the optimal hybrid fractional-order fuzzy PID controller is highly better than the optimal classical PID, fuzzy PID (FPID) and fractional-order PID (FOPID) controllers in terms of response, overshoot and indices performances. Further, the WT-PMSG is effectively controlled at different conditions of wind velocities by the proposed scheme. [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 research paper presents implementation of a fuzzy rule and membership function-based fuzzy-aided PID controller for automatic generation control (AGC) in multiarea nonlinear power system. At the initial stage of this proposed work, a three-area nine-unit installed interconnected network is considered for developing different dynamic responses in response to AGC analysis. A modified approach named modified sine cosine algorithm (M-SCA) is proposed for tuning the gain parameters of the above-proposed fuzzy controller to produce close optimum gain values. The proposed modified algorithm is developed from its original sine cosine algorithm by improving and updating few equations which is capable of making the balance between exploration and exploitation levels of this algorithm and improving the updating quality of iteration. To impose supremacy of M-SCA technique, it is examined through convergence curves and its performance is compared with host sine cosine algorithm, genetic algorithm, and particle swarm optimization algorithm. For controller supremacy analysis, the performance of the proposed fuzzy-aided PID controller is compared with conventional I, PI, and PID controllers, and it has been revealed that proposed M-SCA-tuned fuzzy-aided PID controller exhibits better performances through different deviated responses for AGC analysis. To demonstrate most standard and supremacy of proposed approaches, finally these are tested through a five-area ten-unit system considering some physical nonlinear constraints like generation rate constraint, governor dead band, boiler dynamics and time delay. At the final observation level, the proposed fuzzy controller has gone through different sensitivity analyses with variation of different system parametric conditions and different load conditions. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neuro-fuzzy PID controller based on twin delayed deep deterministic policy gradient (TD3) algorithm for nonlinear systems. In this approach, the observation of the environment is embedded with information of a multiple input single output (MISO) fuzzy inference system (FIS) and have a specially defined fuzzy PID controller in neural network (NN) formation acting as the actor in the TD3 algorithm, which achieves automatic tuning of gains of fuzzy PID controller. From the control perspective, the controller combines the merits of both FIS and PID controller and utilizes reinforcement learning algorithm for optimizing parameters. From the reinforcement learning point of view, embedding the prior knowledge into the fuzzy PID controller incorporated in the actor network helps reduce the learning difficulty in the training process. The proposed method was tested on the cart-pole system in simulation environment with comparison of a linear PID controller, which demonstrates the robustness and generalization of the proposed approach. [ABSTRACT FROM AUTHOR]

The design of a two degree of freedom (DOF) proportional–integral–derivative (PID) controller is proposed in this paper to stabilize an unstable second-order plant having time delay. The lower and upper bounds of the controller gains for which the compensated system will exhibit a stable response, is determined using Hermite–Biehler theorem. In order to determine the bounds of the controller gains, this work proposes an extension of the Hermite–Biehler theorem, suitable for the transfer function of the maglev plant, which contains a negative dc-gain and a missing 's' term in the denominator polynomial. The optimal values of the controller gains, from the range thus estimated, are found by a recent bio-inspired algorithm, namely symbiotic organisms search (SOS). The obtained results have been experimentally verified, and it has been validated that the 2-DOF PID controller exhibits better response. The robustness of the compensated system for 1-DOF and 2-DOF configurations, in the presence of external disturbance and measurement noise, has also been validated in real time, and it is observed that the 2-DOF PID controller improves the robustness of the system, as compared to its 1-DOF counterpart. [ABSTRACT FROM AUTHOR]