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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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

An adaptive chaos particle swarm optimization (ACPSO) is presented in this paper to tune the parameters of proportional-integral-derivative (PID) controller. To avoid the local minima, we introduced a constriction factor. Meanwhile, the chaotic searching is combined with the particle swarm optimization to improve the ability of the proposed algorithm. A series of experiment is performed on 6 benchmark functions to confirm its performance. It is found that the ACPSO can get better solution quality in solving the global optimization problems and avoiding the premature convergence. Based on it, the proposed algorithm is applied to tune the PID controller's parameters. The performances of the ACPSO are compared with different inspired algorithms, and these results show that the ACPSO is more robust and efficient when it is used to find the optimal parameters of PID controller. [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]

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In this paper, a new bat algorithm (BA) based on dynamic control parameters selection is presented. The dynamic BA (DBA) uses a new mechanism to dynamically select the best performing combination of the pulse rate coefficient, the pulse frequency coefficient, and the population size. A fractional-order PID (FOPID) controller based on the DBA is implemented to improve the performance of a distillation column process. The proposed FOPID controller is used to control the distillate and bottom mole fractions. The influence of the feed rate disturbance is considered for this model. The efficacy of the DBA-based FOPID is compared with the performance of the controllers based on the conventional BA, directional BA, enhanced BA, genetic algorithm, and particle swarm optimization algorithm. The analyses and simulation results show the superiority of the proposed 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]

In order to optimize the parameters of fractional order PID controller of complex system, an adaptive particle swarm optimization (PSO) method is proposed to realize the parameters adjustment. In this algorithm, the tuning particle population is divided into three subgroups firstly, and through introducing the swarm-aggregation degree factor and the evolution speed factor of particle, dynamically adjusting the inertia weight and size of subgroups respectively, setting to find optimal objective according to the timedomain performance index of the system, and then the controller parameter tuning is realized by iterative calculation. Finally, adaptive particle swarm optimization method of fractional order PID controller is applied to integer order and fractional order of the controlled system for performance simulation in time domain analysis. The experimental results show that the proposed method could improve the performance of the control system and has strong anti-interference ability. [ABSTRACT FROM AUTHOR]

Considering the higher flexibility in tuning process and finer control action of the fractional-order proportional integral derivative (FOPID) controller over the conventional proportional integral derivative (PID) controller, this paper explores its application in the automatic voltage regulator system. FOPID contains five tuning parameters as compared to three in the conventional PID controller. The additional tuning knobs in FOPID provide increased control flexibility and precise control action, however, their inclusion makes the tuning process more complex and tedious. Thus, the intelligence of an artificial intelligence (AI) technique called jaya optimization algorithm (JOA) is utilized in order to obtain an optimal combination of FOPID gains which further led to the optimal transient response and improved stability of the considered AVR system. To validate the performance superiority of the proposed approach its corresponding system's dynamic response is compared with that of the other well-known AI-based approaches explored in recent literature. Furthermore, the stability study of the proposed AVR system is carried out by evaluating its pole/zero and bode maps. Finally, the robustness of the proposed optimized AVR system against the system's parameter variation is evaluated by varying the time constants of all the four components of AVR (generator, exciter, amplifier and sensor) from −50% to +50% independently. The proposed algorithm based FOPID tuning technique provides 59.82%, 56.09%, 14.94%, 34.24%, 35.70%, 21.64%, 12.0%, 41.33%, 14.84% and 15.17% reduced overshoot than that of differential evolution (DE), particle swarm optimization (PSO), Artificial Bee Colony (ABC), Bibliography Based Optimization (BBO), Grasshopper Optimization Algorithm (GOA), Pattern Search Algorithm (PSA), Improved Kidney Inspired Algorithm (IKA), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA) and Local Unimodal Sampling (LUS) algorithm respectively, thus validates its competence and effectiveness. [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 order to improve the functionality and efficiency of a unified power quality conditioner (UPQC), a DC-link bus integrated photovoltaic charging module is proposed in a UPQC. It can generate power for essential loads apart from providing energy to a DC-link bus. A conventional proportional integral (PI) controller fails to run smoothly in dynamic conditions of the micro-grid, since it has poor capabilities in determining suitable values of proportional gain and integral gain. So, the optimization algorithm for a PI controller based on chaos particle swarm optimization based on a multi-agent system (CPSO-MAS) algorithm was developed in this paper to overcome properties such as intermittent instability in the micro-grid. Through verification by simulation and experiment of UPQC harmonic compensation, it showed that the proposed DC link bus voltage control strategy can be effectively applied to UPQC towards various conditions related to voltage and current distortion. In addition, it proved that the proposed strategy has faster convergence than other algorithms, which enhances the stability of DC-link bus voltage. Hence, the contribution presented in this paper is to provide a novel approach for the power quality improvement of UPQC. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel design method for determining the optimal fuzzy PID-controller parameters of active automobile suspension system using the particle swarm optimization (PSO) reinforcement evolutionary algorithm is presented. This paper demonstrated in detail how to help the PSO with Q-learning cooperation method to search efficiently the optimal fuzzy-PID controller parameters of a suspension system. The design of a fuzzy system can be formulated as a search problem in high-dimensional space where each point represents a rule set, membership functions, and the corresponding system’s behavior. In order to avoid obtaining the local optimum solution, we adopted a pure PSO global exploration method to search fuzzy-PID parameter. Later this paper explored the improved the limitation between suspension and tire deflection in active automobile suspension system with nonlinearity, which needs to be solved ride comfort and road holding ability problems, and so on. These studies presented many ideas to solve these existing problems, but they need much evolution time to obtain the solution. Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance. [Copyright &y& Elsevier]

In this paper, a model based intelligent PID controller tuning is attempted for a Magnetic Suspension System (MSS) using Bacterial Foraging Optimization (BFO) algorithm. MSS is an Electro- Mechanical system with highly non linear dynamics and exhibits unstable steady state behavior around the nominal operating range. In this work, Integral Time Absolute Error (ITAE) based performance criterion is employed to guide the BFO to search the optimized controller parameters such as Kp, Ki, and Kd. The work is substantiated through a comparative study with Particle Swarm Optimization (PSO) algorithm by using PID and modified PID controller structure. The effectiveness of the proposed scheme has been validated with a simulation study and the results show that the proposed method provides better result in the control of the unstable MSS system with effective setpoint tracking and load disturbance rejection. [ABSTRACT FROM AUTHOR]

This paper proposes a new technique to design an optimal robust PI-controller for electro-hydraulic servo system to achieve both the robustness and performance. Comparative study between the conventional robust control and the proposed technique is included in the paper. Particle Swarm Optimization (PSO) is adopted to simplify the solving of structured robust control problem to realize the practical robust controller. Simulation results show that the proposed controller has simpler structure than that of the conventional robust loop shaping controller, and the stability margin obtained indicates the robustness of the proposed controller. Simulation and experimental results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the design of proportional integral derivative (PID) based load frequency control (LFC) scheme effectively optimized through novel combination of bacteria foraging oriented particle swarm optimization (BFO-PSO) technique for a hydro dominating energy system model. The design control are implemented for 1% load disturbance in area-1 and compared with classical PID, Pessen integral rule, some overshoot, no overshoot as well as with recently published bacteria foraging optimization algorithm (BFOA) in terms of computed gains and inverse time multiplied absolute error (ITAE). The system performance for hydro energy system is sluggish and oscillatory. Hence, the further enhancement with proposed design are observed by considering the combination of unified power flow control (UPFC) in series with the tie-line and hydrogen aqua electrolyzer units installed at terminal of area-2. The system performance of the designed control is evaluated under various system investigations considering the dead-band and generation rate constraint (GRC) non-linearity and the applications results are presented to show the superiority of the proposed work. [ABSTRACT FROM AUTHOR]

This paper compares the effectiveness of the proposed hybrid metaheuristic algorithms for a class of unstable systems with time delay to that of the existing ones. The local search and global methods of optimization are combined to yield more effective hybrid metaheuristic algorithms. These algorithms are used to tune the proportional-integral-derivative (PID) controllers, satisfying the robust stabilizing vector gain margin (VGM). Six global heuristic algorithms namely ant colony optimization (ACO), particle swarm optimization (PSO), biogeography-based optimization (BBO), population-based incremental learning (PBIL), evolution strategy (ES), and stud genetic algorithms (StudGA) are combined with the local search property of derivative free optimization methods such as simplex derivative based pattern search (SDPS) and implicit filtering (IMF) to yield hybrid metaheuristic algorithms. The efficacy of the proposed control schemes in terms of various time domain specifications and stabilizing VGM are compared with some existing methods for unstable process with time delay (UPTD) systems. The performance of the proposed control schemes particularly in the context of uncertainty in the plant is demonstrated using a case study. The efficacy of the proposed control scheme is illustrated with a nontransfer function based multibody vehicle autosteer control design problem. [ABSTRACT FROM AUTHOR]

In this study, the PID tuning method (controller design scheme) is proposed for a linear quarter model of active suspension system installed on the vehicles. The PID tuning scheme is considered as a multiobjective problem which is solved by converting this multiobjective problem into single objective problem with the aid of scalarization approaches. In the study, three different scalarization approaches are used and compared to each other. These approaches are called linear scalarization (weighted sum), epsilon-constraint and Bensons methods. The objectives of multiobjective optimization are selected from the time-domain properties of the transient response of the system which are over-shoot, rise time, peak time and error (in total there are four objectives). The aim of each objective is to minimize the corresponding property of the time response of the system. First, these four objective is applied to the scalarization functions and then single objective problem is obtained. Finally, these single objective problems are solved with the aid of heuristic optimization algorithms. For this purpose, four optimization algorithms are selected, which are called Particle Swarm Optimization, Differential Evolution, Firefly, and Cultural Algorithms. In total, twelve implementations are evaluated with the same number of iterations. In this study, the aim is to compare the scalarization approaches and optimization algorithm on active suspension control problem. The performance of the corresponding cases (implementations) are numerically and graphically demonstrated on transient responses of the system. [ABSTRACT FROM AUTHOR]

This work describes the best design of a Proportional Integral Derivative (PID) controller for an Unmanned Aerial Vehicle (UAV) Gimbal system utilising a new optimisation approach derived from the Particle Swarm Optimisation and the Sine Cosine Algorithm (PSOSCA). The optimal parameters of PID controller (Kp, Ki, and Kd) is determined by the newly designed hybrid algorithm. The effects of numerous iterations for the new hybrid PSO-SCA settings have been investigated using simulations in MATLAB/Simulink environment. The simulations' findings demonstrate that each PID parameter's value varies from simulation to simulation and are influenced by the iteration and the particles. The controller is evaluated to determine how it responds to overshoot, time rise, and settling time. Additionally, bode analysis was utilised to assess the system's stability following optimisation with the hybrid PSO-SCA technique. To determine the efficacy of the random learning mechanism, we evaluated the new hybrid algorithm with the PSO and SCA algorithms. The simulation result showed better performance in overshoot (0.381474 s), settling time (0.153947 s) and rise time (0.100156 s). This is due to the fact that hybrid PSO-SCA increases value search and enables the rapid and accurate discovery of good solutions. In additional, Peak gain was calculated to be 0.882 dB (9.98 rad/s), phase margin to be 83.7° (11 rad/s), and delay margin to be 0.132 s. This result indicates that the system is stable and has an excellent frequency response. The use of hybrid PSO-SCA can also provide better results when searching for comparisons with PSO or SCA alone due to the combination of global and local search functions. [ABSTRACT FROM AUTHOR]

This paper addresses the position control of valve-controlled cylinder system employed in hydraulic excavator. Nonlinearities such as dead zone, saturation, discharge coefficient and friction existed in the system are highlighted during the mathematical modeling. On this basis, simulation model is established and then validated against experiments. Aim for achieving excellent position control performances, an improved particle swarm optimization (PSO) algorithm is presented to search for the optimal proportional-integral-derivative (PID) controller gains for the nonlinear hydraulic system. The proposed algorithm is a hybrid based on the standard PSO algorithm but with the addition of selection and crossover operators from genetic algorithm in order to enhance the searching efficiency. Furthermore, a nonlinear decreasing scheme for the inertia weight of the improved PSO algorithm is adopted to balance global exploration and local exploration abilities of particles. Then a co-simulation platform combining the simulation model with the improved PSO tuning based PID controller is developed. Comparisons of the improved PSO, standard PSO and Phase Margin (PM) tuning methods are carried out with three position references as step signal, ramp signal and sinusoidal wave using the co-simulation platform. The results demonstrated that the improved PSO algorithm can perform well in PID control for positioning of nonlinear hydraulic system. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice. A selection of the PID controller parameters is required before the UAV can be used. Although there are guidelines for the design of PID parameters, they do not guarantee the stability of the UAV, which in many cases, leads to collisions involving the UAV during the calibration process. In this paper, an offline tuning procedure based on the multi-objective particle swarm optimization (MOPSO) algorithm for the attitude and altitude control of a Px4-based UAV is proposed. A Pareto dominance concept is used for the MOPSO to find values for the PID comparing parameters of step responses (overshoot, rise time and root-mean-square). Experimental results are provided to validate the proposed tuning procedure by using a quadrotor as a case study. [ABSTRACT FROM AUTHOR]

Automated guided vehicles (AGVs) are popular subsets of robots that come in various shapes and sizes. The group's use in the industry ranges from applications for carrying pallets, carts, and utensils to helping the elderly or transporting medicine to hospitals. Even recently, they have been used in libraries to carry books on shelves. The main part of an AGV includes its body, motor, driver, processor, and sensors, which are more or less the same in all types of AGVs, and addons vary depending on the application and the work environment. The part that affects AGV performance is the control strategy, to which researchers have shown different approaches. Using various techniques and simulations to obtain a model is the key and can help to improve and evaluate the performance of the strategy of the robot. In this study, based on the actual design of the AGV system, all data and components are described, and the simulation is performed in MATLAB software. Then, for controlling the platform based on the PID controller tuning, four methods of Ziegler Nichols, empirical, Particle Swarm Optimization (PSO), and Beetle Antennae Searching (BAS) (optimizer) are discussed, and the results are compared in the four paths including the circle, ellipse, Spiral and 8-shaped paths by observing and testing the tuned PID parameters. Finally, a series of subsequent experiences were carried out in CoppeliaSim (VREP) as a famous robot simulator to overcome the environmental constraints for the same paths that were used in Matlab based on the extracted PID values. Based on the results, the empirical methods, PSO, and BAS errors are very close together. But in general, the BAS algorithm is the fastest, and the PSO had better performance. In general, the maximum error is linked to the path of 8 shapes and the minimum is related to circle shape one. Finally, the analysis of results in different paths in both simulators shows the same results. Therefore, concerning the limited test on the real platform and using the PID coefficients obtained from the simulation shows the model's ability for the researchers in robotic research. [ABSTRACT FROM AUTHOR]

This article proposes a novel MPPT algorithm based on the firefly algorithm and elite ant system-trained Elman neural network (FA-EAS-ElmanNN). First, the position of fireflies is randomly initialized by the firefly algorithm (FA), meanwhile the firefly individuals with higher attractiveness degree value are selected as the optimal solution. Second, the extra pheromones are artificially released to boost the positive feedback effect and convergence rate of the elite ant system (EAS). Third, the weight and threshold of the Elman neural network (ElmanNN) are updated by the FA and EAS. Also, the trained ElamnNN is employed to acquire the maximum voltage of the photovoltaic (PV) array. At last, the PID controller and PWM technology are adapted to regulate the switch time of the boost converter. Furthermore, MATLAB/Simulink is adopted to acquire the datasets of irradiance, temperature, and maximum voltage and validate the reliability and superiority of the proposed algorithm under complex atmospheric conditions. The tracking characteristic, response speed, and efficiency of the proposed MPPT algorithm are contrasted with the particle swarm optimization (PSO), ant colony optimization (ACO), ACO-artificial neural network (ACO-ANN), and PSO-RBF neural network (PSO-RBNFNN) algorithm via simulation. The efficiency of the FA-EAS-ElmanNN algorithm is 99.73%, compared with the ACO-ANN, PSO-RBFNN, PSO, and ACO algorithm, which is increased by 0.49%, 0.58%, 1.2% %, and 1.5%, respectively. Additionally, the experimental setup is built to demonstrate the tracking characteristic of the proposed MPPT algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an optimal Proportional Integral Derivate (PID) controller design for an Automatic Voltage Regulator (AVR) system using a new hybrid devised from the Particle Swarm Optimization and the Gravitational Search Algorithm (PSOGSA). The transient response analysis and bode analysis were considered to show the effectiveness of the design technique. Moreover, the comparison of the results between the proposed approach and other techniques such as the Ziegler-Nichols (ZN) tuning method, the Particle Swarm Optimization (PSO) tuning method and the Many Optimizing Liaisons (MOL) tuning method have been given. According to the analysis, the proposed PSOGSA algorithm gives better results than other techniques for the AVR system. [ABSTRACT FROM AUTHOR]

This paper introduces a novel memetic algorithm namely Fractional Particle Swarm Optimization-based Memetic Algorithm (FPSOMA) to solve optimization problem using fractional calculus concept. The FC illustrates a potential for interpreting progression of the algorithm by controlling its convergence. The FPSOMA accomplishes global search over the whole search space through PSO whereas local search is performed by PSO with fractional order velocity to alter the memory of best location of the particles. To assess the performance of the proposed algorithm, firstly an empirical comparison study is presented for solving different test functions adopted from literature. Comparisons demonstrate the preference of FPSOMA than other related algorithms. Subsequently, experiments are conducted to achieve optimal gains of Fractional Order Proportional-Integral-Derivative (FO PID) controller in solving tracking problem. Results verify the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced to initialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjust the network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods. [Copyright &y& Elsevier]

This paper aims at the PID control system design for multivariable input and multivariable output (MIMO) processes. An improved version of a particle swarm optimization (PSO) algorithm is utilized to design PID control gains in MIMO control systems. In addition to the individual best and the global best particles, the velocity updating formula of the developed algorithm includes a new factor, the best particle of each sub-population, to enhance the search capacity. Based on the improved particle swarm optimization (IPSO), a complete design strategy is proposed for MIMO PID control systems. All control gains will be evolved to the optimal values by minimizing the system performance criterion. To show the efficiency of the proposed design method, a multivariable chemical process system with two inputs and two outputs is illustrated. Some experiment results, including different algorithm parameter settings and comparisons with other methods, are given. Numerical simulations indicate that the proposed method is superior to other optimal methods. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents implementation of particle swarm optimization (PSO) algorithm as a C-Mex S-function. The algorithm is used to optimize a 9-rule fuzzy logic controller (FLC) for maximum power point tracking (MPPT) in a grid-connected photovoltaic (PV) inverter. The FLC generates DC bus voltage reference for MPPT. A digital PI current control scheme in rotating dq-reference frame is used to regulate the DC bus voltage and reactive power. The proposed technique simplifies optimal controller design and ensures fast simulation speeds due to seamless integration with the simulation platform. Validity of the proposed method was verified using co-simulation in PSIM and MATLAB/Simulink. Simulation results show that the optimized FLC gives a better performance compared to fixed-step MPPT. [Copyright &y& Elsevier]

Abstract: In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency. [Copyright &y& Elsevier]

Abstract: Tunnel kiln temperature control system is a typical system which has characteristics of complex, dynamic, and uncertainty. Under the conventional PID control, parameters are adjusted by experienced workers. In the steady-state, effect is better. If conditions change, it is difficult to be steady again. According to this characteristic, this paper proposes the use of improved particle swarm algorithm to optimize the PID control. And in the tunnel kiln temperature control system simulation, results of the two control methods are compared. Finally the optimized PID control effect is better. [Copyright &y& Elsevier]

Abstract: In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed. Besides the individual best position and the global best position, a nominal average position of the swarm is introduced in IPSO. The performance of IPSO is compared to different PSO variants with five well-known benchmark functions. The experimental results show that the proposed IPSO algorithm improves the searching performance on the benchmark functions. And then, IPSO, as well as other PSO variants, is applied to optimal tuning of Proportional–Integral–Derivative (PID) gains for a typical PID control system of water turbine governor. The computer simulation results of an actual hydro power plant in China show that IPSO algorithm has stable convergence characteristic and good computational ability, and it is an effective and easily implemented method for optimal tuning of PID gains of water turbine governor. [Copyright &y& Elsevier]

This paper proposes a maiden application of a two dimensional Sine Logistic map based chaotic sine cosine algorithm (2D-SLCSCA) optimized classical PID controller for load frequency control (LFC) of an islanded microgrid (MG). In comparison to random variables and 1D chaotic sequences, the 2D chaotic sequences are more ergodic and possess a wider chaotic range, thereby enhancing the global convergence speed and search capability of an algorithm. Initially, the proposed 2D-SLCSCA is tested on eight classical benchmark test functions and its performance is compared with 1D Logistic map based chaotic SCA (1D-LCSCA), 1D Sine map based chaotic SCA (1D-SCSCA), and the SCA incorporating random variables. Test results reveal that the proposed algorithm exhibits better convergence characteristics, statistics, and execution time. Finally, the proposed 2D-SLCSCA is implemented for the LFC analysis of the islanded MG. To establish the competence of the proposed algorithm in this regard, its performance is compared with 2D Hénon map based chaotic SCA, 2D Lozi map based chaotic SCA, improved salp swarm algorithm (ISSA), SCA, grey wolf optimizer (GWO), and particle swarm optimization (PSO) algorithm considering diverse load disturbance patterns in the MG. Simulation results affirm that the proposed control scheme augments the frequency response of the MG exhibiting a maximum percentage improvement of 78.89%, 78.86%, and 96.51% in peak overshoot (O S p e a k ), peak undershoot (U S p e a k ) and objective function (O F I T S E ) value, respectively as compared to the other algorithms. Furthermore, sensitivity of the proposed 2D-SLCSCA is validated considering ± 30% variation in the MG parameters. • Maiden application of a 2D-SLCSCA based PID controller for LFC analysis of a MG. • Competence establishment of the 2D-SLCSCA with 1D Sine and 1D Logistic maps. • Eight classical benchmark test functions considered for the competence analysis. • Performance comparison of the proposed 2D-SLCSCA with other algorithms of repute. • RLD, SLD and PLD patterns considered for the performance comparison. • Sensitivity analysis considering ± 30% variation in the various MG parameters. • Exceptional robust performance of the proposed control scheme demonstrated. • Proposed LFC scheme could be put into practice with ease. [ABSTRACT FROM AUTHOR]

In this work, an efficient constrained nonlinear predictive control algorithm, based on the meta-heuristic optimization strategy, is proposed. The aim is to develop a predictive control algorithm having a simple design and implementation procedure on the one hand, and that can give satisfactory performance with a large class of nonlinear systems on the other hand. To achieve this goal, a feedforward multilayer neural network is used to predict the future outputs of the system, and the optimization problem of predictive control is resolved using teaching–learning-based optimization strategy. Due to their interesting proprieties, feedforward neural networks are widely used in nonlinear systems identification and control. Indeed, they have a simple structure and can accurately approximate any nonlinear mapping. The teaching–learning-based optimization is used in a large number of applications in different fields of engineering and has gained wide acceptance among the optimization researchers community. Unlike other metaheuristic algorithms, this algorithm requires only common controlling parameters like population size and number of generations for its working; it does not require any algorithm-specific parameters. To assess the effectiveness of the proposed control algorithm, the control of the model of the continuous stirred tank rector and the 2-DOF manipulator robot model is considered. A comparative study, using the conventional PID controller, the fuzzy logic control, the computed torque control and the neural network-based model predictive control using particle swarm optimization, is carried out. To further demonstrate the effectiveness of the proposed controller, the control algorithm is numerically implemented and applied to a system with fast dynamics, namely the induction motor. The obtained results, through the simulation and the experimental study, indicate that the proposed controller presents better control performance than the other controllers. Furthermore, the experimental study shows that the developed control strategy can be effectively used to control, in real time, systems with fast dynamics. [ABSTRACT FROM AUTHOR]

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the "swarm learning process" (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems. [ABSTRACT FROM AUTHOR]

Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic community and industry society throughout the world. To improve the performance of particle swarm optimization (PSO), we herein propose a chaotic proportional integral derivative (PID) controlling PSO algorithm by the hybridization of chaotic logistic dynamics and hierarchical inertia weight. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles’ search space. Moreover, the chaotic logistic map is not only used in the substitution of the two random parameters affecting the convergence behavior, but also used in the chaotic local search for the global best position so as to easily avoid the particles’ premature behaviors via the whole search space. Thereafter, the convergent analysis of chaotic PID controlling PSO is under deep investigation. Empirical simulation results demonstrate that compared with other several chaotic PSO algorithms like chaotic PSO with the logistic map, chaotic PSO with the tent map and chaotic catfish PSO with the logistic map, chaotic PID controlling PSO exhibits much better search efficiency and quality when solving the optimization problems. Additionally, the parameter estimation of a nonlinear dynamic system also further clarifies its superiority to chaotic catfish PSO, genetic algorithm (GA) and PSO. [ABSTRACT FROM AUTHOR]

Automatic Generation Control (AGC) delivers a high quality electrical energy to energy consumers using efficient and intelligent control systems ensuring nominal operating frequency and organized tie-line power deviation. Subsequently, for the AGC analysis of a two-area interconnected hydro-gas-thermal-wind generating unit, a novel Fractional Order Integral-Tilt Derivative (FOI-TD) controller is proposed that is fine-tuned by a powerful meta-heuristic optimization technique referred as Improved-Fitness Dependent Optimizer (I-FDO) algorithm. For more realistic analysis, various constraints, such as Boiler Dynamics (BD), Time Delay (TD), Generation Rate Constraint (GRC), and Governor Dead Zone (GDZ) having non-linear features are incorporated in the specified system model. Moreover, a comparative analysis of I-FDO algorithm is performed with state-of-the-art approaches, such as FDO, teaching learning based optimization, and particle swarm optimization algorithms. Further, the proposed I-FDO tuned controller is compared with Fractional Order Tilt Integral Derivative (FOTID), PID, and Integral-Tilt Derivative (I-TD) controllers. The performance analysis demonstrates that proposed FOI-TD controller provides better performance and show strong robustness by changing system parameters and load condition in the range of  ± 50%, compared to other controllers. [ABSTRACT FROM AUTHOR]

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results. [ABSTRACT FROM AUTHOR]

The PID (proportional–integral–derivative) controller is the most widely used control method in modern engineering control because it has the characteristics of a simple algorithm structure and easy implementation. The traditional PID controller, in the face of complex control objects, has been unable to meet the expected requirements. The emergence of the intelligent algorithm makes intelligent control widely usable. The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a new evolutionary algorithm. Compared with other intelligent algorithms, the QUATRE algorithm has a strong global search ability. To improve the accuracy of the algorithm, the adaptive mechanism of online adjusting control parameters was introduced and the linear population reduction strategy was adopted to improve the performance of the algorithm. The standard QUATRE algorithm, particle swarm optimization algorithm and improved QUATRE algorithm were tested by the test function. The experimental results verify the advantages of the improved QUATRE algorithm. The improved QUATRE algorithm was combined with PID parameters, and the simulation results were compared with the PID parameter tuning method based on the particle swarm optimization algorithm and standard QUATRE algorithm. From the experimental results, the control effect of the improved QUATRE algorithm is more effective. [ABSTRACT FROM AUTHOR]