Top PDF Evolutionary algorithms based tuning of PID controller for an AVR system

Evolutionary algorithms based tuning of PID controller  for an AVR system

Evolutionary algorithms based tuning of PID controller for an AVR system

In recent literature many evolutionary optimization algorithms are proposed for tuning PID controller in the AVR system such as Anarchic Society Optimization [16], reinforcement learning automata optimization approach [17], real coded GA with fuzzy logic technique [18], Choatic ant swarm algorithm [19], Artificial Bee Colony algorithm [20], Hybrid GA-Bacterial Foraging (BF) algorithm [21] and local unimodal sampling algorithm [22]. GA and Ant Colony Optimization techniques are proposed to tune the parameters of FOPID controller in controlling of AVR system. In some of the research papers novel performance criteria has been proposed for optimal tuning of PID and FOPID controller in AVR control system. A novel performance criterion comprises of overshoot, settling time, steady state error and mean of time weighted integral absolute error has been proposed for optimal tuning of PID controller in AVR system using cuckoo search algorithm [23]. A. Sikander et. al, 2018 has proposed a cuckoo search algorithm based fractional order PID controller for AVR system with performance criterion which was proposed by Gaing et. al in 2004 [24]. In this research work, Cuckoo search (CS) and particle swarm optimization (PSO) algorithms are proposed to find the optimal parameters of PID controller in the control of automatic voltage regulator (AVR) system with new performance criterion comprises of Integral absolute error, rise time, settling time and peak overshoot. The performance of this new proposed performance criterion is compared with performance of other performance criterion such as ITAE, ITSE, ISE, MSE and IAE. The paper is mainly organized such that section two describes about the Automatic Voltage Regulator (AVR) system; section three examines the Cuckoo search (CS) algorithm and particle swarm optimization (PSO) algorithms; section four and five concentrate on the application of CS-PID, PSO-PID and conventional tuning method (Ziegler-Nichols) in optimal tuning PID controller for both servo and regulatory control of AVR system. Additionally, section six describes conclusions of the study.
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Implementation of Fractional Order PID Controller for An AVR System Using GA Optimization Technique

Implementation of Fractional Order PID Controller for An AVR System Using GA Optimization Technique

Abstract: In recent years, the research and studies on fractional order (FO) modelling of dynamic systems and controllers are quite advancing. To improve the stability and dynamic response of automatic voltage regulation (AVR) system, we presented fractional Order PID (FOPID) controller by employing Genetic Algorithm (GA) technique. We used fractional order tool kit a masked fractional order PID block in MATLAB programming to implement the P ⋋ controller for any type of system. Comparisons are made with a PID controller from standpoints of transient response. It is shown that the proposed FOPID controller can improve the performance of the AVR in all the aspects.
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Optimal PID Controller Design for AVR System

Optimal PID Controller Design for AVR System

In this paper, a real-valued genetic algorithm (RGA) and a particle swarm optimization (PSO) algorithm with a new fitness function method are proposed to design a PID controller for the Automatic Voltage Regulator (AVR) system. The proposed fitness function can let the RGA and PSO algorithm search a high-quality solution effectively and improve the transient response of the controlled system. The proposed algorithms are applied in the PID controller design for the AVR system. Some simulation and comparison results are presented. We can see that the proposed RGA and PSO algorithm with this new fitness function can find a PID control parameter set effectively so that the controlled AVR system has a better control performance.
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Performance Analysis of ACO Based PID Controller in AVR System

Performance Analysis of ACO Based PID Controller in AVR System

the proposed method generates a solution very near to the global optimum solution. Ying-Tung Hsiao presents an optimum approach for designing of PID controllers using ACO to minimize the integral absolute control error. The experiment results demonstrate that better control performance can be achieved in comparison with conventional PID method.[6] Duan Hai-bin presented a parameter optimization strategy for PID controller using ACO Algorithm. The algorithm has been applied to the combinatorial optimization problem, and the results indicate high precision of control and quick response. Mohd. Rozely Kalil, Ismail Musirin proposed Ant Colony Optimization (ACO) technique for searching the optimal point of maximum loadability point at a load bus.. Hamid Boubertakh, Mohamed Tadjine, Pierre-Yves Glorennec and Salim Labiod has proposed theory that although conventional PID controllers are the most used in the industrial process, their performance is often limited when it is poorly tuned and/or used for controlling highly complex processes with nonlinearities, complex dynamic behaviors.[3] Ing-Tung Hsiao proposed a solution algorithm based on the ant colony optimization technique to determine the parameters of the PID controller for getting a well performance for a given plant. Simulation results demonstrate that better control performance can be achieved in comparison with known methods.[4] Kiarash , Mehrdad Abedi,(2011) Shuffled frog leaping and particle swarm optimization this two algoritm are used to determine optimal PID controller in AVR system and also shows that for tuning PID controller using various optimization technique reduces complexity and find more realistic result than trial and error method.[4] Hany M. Hasanien (2013) propose optimization of PID controller in AVR system shows that minimize the maximum percentage overshoot, the rise time, the settling time and oscillation and step response of AVR system can be changed. Richa Singh (IEEE 2016)-ACO is popular technique which shows behavior of real ant colonies to find solutions to discrete optimization problems.[13]
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Compare the results of Tuning of PID controller by using PSO and GA Technique for AVR system

Compare the results of Tuning of PID controller by using PSO and GA Technique for AVR system

The task of an AVR system is to hold the terminal voltage magnitude of a synchronous generator at a specified level. Thus, the stability of the AVR system would seriously affect the security of the power system. A simpler AVR system contains five basic components such as amplifier, exciter, generator, sensor and comparator. The real model of such a system is shown in fig1. A unit step response of this system without control has some oscillations which reduce the performance of the regulation (1). Thus, a control technique must be applied to the AVR system. For this reason, the PID block is connected to amplifier seriously. The A small signal model of this system including PID controller which is constituted through the transfer functions of these components is depicted in Fig, and the limits of the parameters used in these transfer functions are presented(2) . PID controllers have been widely used for speed and position control of various system.
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Design of Intelligent PID Controller for AVR System Using an Adaptive Neuro Fuzzy Inference System

Design of Intelligent PID Controller for AVR System Using an Adaptive Neuro Fuzzy Inference System

engineers, and easiness to design and implement. The PID and its variations (P, PI, PD) still are widely applied in the motion control because of its simple structure and robust performance in a wide range of operating conditions. Unfortunately, it has been quite difficult to tune properly the gains of PID controllers because many industrial plants are often burdened with problems such as high order, time delays, and nonlinearities. Therefore, when the search space complexity increases the exact algorithms can be slow to find global optimum. Linear and nonlinear programming, brute force or exhaustive search and divide and conquer methods are some of the exact optimization methods. Over the years, several heuristic methods have been proposed for the tuning of PID controllers. These methods have several advantages compared to other algorithms as follows: (a) Heuristic algorithms are generally easy to implement; (b) They can be used efficiently in a multiprocessor environment; (c) They do not require the problem definition function to be continuous; (d) They generally can find optimal or near-optimal solutions. Particle swarm optimization (PSO) is an efficient and well known stochastic algorithm which has found many successful applications in engineering problems [1-4]. Signal to noise ratio algorithm doesn’t require a wide solution space, and the large number of searching and iterations were susceptible to related control parameters. On the other hand, this method has an effective appliance and better result for uncertainties conditions and different operation points. SNR has a responsible result in the nonlinear systems optimization. The integral performance criteria in frequency domain were often used to evaluate the controller performance, but these criteria have their own advantages and disadvantages [5-6]. In this study a novel design method for determining the optimal signal to noise ratio algorithm and particle swarm optimization (SNR-PSO) parameters for design the optimal proportional-integral-derivative (PID) controller of an automatic voltage regulator (AVR) system using the hybrid SNRPSO approach such that the controlled system could obtain a good step response output for some event and case that may be happen in the power system. After that we use ANFIS for training and obtained the fuzzy membership function (MF) for fuzzy inference system with result of our optimization. In this paper a fuzzy inference system models which takes K G and  g as inputs and K p , K i and K d as output. Therefore
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Continuous firefly algorithm for optimal tuning of PID controller in AVR system

Continuous firefly algorithm for optimal tuning of PID controller in AVR system

This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.
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Optimal Tuning of PID Controller by Firefly Algorithm in AVR System

Optimal Tuning of PID Controller by Firefly Algorithm in AVR System

In this paper an efficient meta-heuristics algorithm is proposed for the practical higher order AVR system with PID controller to investigate the performance of the proposed method. Firefly Algorithm (FA) is one of the nature inspired computing algorithm. It has been found to robust in solving continuous non-linear optimization problems. In the PID controller design, the FFA algorithm is applied to search an optimal PID control parameters.

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A PID and PIDA Controller Design for an AVR System using Frequency Response Matching

A PID and PIDA Controller Design for an AVR System using Frequency Response Matching

Mukherjee and Ghoshal [12] present fuzzy PID controller design for an AVR system and the parameter of PID controller is optimized using intelligent particle swarm algorithm. Fuzzy P plus Fuzzy I plus Fuzzy D (FP+FI+FD) controller has been designed for an AVR system based on Fuzzy logic and its controller gain parameter is optimized using hybrid of GA and PSO (HGAPSO) algorithm by Shayeghi et al. [13]. A PID controller design has been developed for an AVR system based on a combined GA, radial basis function neural network (RBF-NN) and Sugeno fuzzy logic technique by Gizi et al. [14]. Ribeiro et al. [15] presents sliding mode control technique for an AVR and this technique is able to maintain stability and terminal voltage of synchronous generator.
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A novel optimal PID plus second order derivative controller for AVR system

A novel optimal PID plus second order derivative controller for AVR system

In power generation systems, automatic voltage regulator (AVR) is utilized to maintain the terminal voltage of a synchronous generator at a speci fi ed level. The AVR controls the consistency of the terminal voltage by varying the exciter voltage of the generator [1]. Due to the high inductance of the generator fi eld windings and load variation, stable and fast response of the regulator is dif fi cult to achieve. Therefore, it is important to improve the AVR performance and ensure stable and ef fi cient response to transient changes in terminal voltage. Various control structures have been proposed for the AVR system, however, among these controllers the proportional plus integral plus derivative (PID) was the most preferable controller. The PID controller is distinguished by its robust perfor- mance over a wide range of operating conditions and simplicity of structure design [2]. The design of the PID controller involves the determination of three parameters which are the proportional, integral, and derivative gains. In recent years, many intelligent optimization algorithms were proposed to tune the PID gains of the AVR system. Such algorithms include Particle Swarm Optimization (PSO) [3], Genetic algorithm (GA) [3,4], Craziness based particle
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Intelligent PID Controller Tuning Using PSO for Linear System

Intelligent PID Controller Tuning Using PSO for Linear System

In this paper, a simple performance criterion in time domain is proposed for evaluating the performance of a PSO-PID controller that was applied to the complex control system. The generator excitation system maintains generator voltage and controls the reactive power flow using an automatic voltage regulator (AVR). The role of an AVR is to hold the terminal voltage magnitude of a synchronous generator at a specified level. Hence, the stability of the AVR system would seriously affect the security of the power system. In this paper, a practical high-order AVR system with a PID controller is adopted to test the performance of the proposed PSO-PID controller. In this paper, besides demonstrating how to employ the PSO method to obtain the optimal PID controller parameters of an AVR system, many performance estimation schemes are performed to examine whether the proposed method has better performance than the real-value GA method in solving the optimal.
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Hybrid Differential Evolution Based Tuning Of PID Controller for Automatic Voltage Regulation System

Hybrid Differential Evolution Based Tuning Of PID Controller for Automatic Voltage Regulation System

ABSTRACT: This paper presents the implementation of HDE algorithm for PID controller tuning for automatic voltage regulator systems so that more expeditious settling to rated voltage is ascertained and AVR is closed loop system compensated with a PID controller. In this work two AVR examples are considered. In first plant saturation non-linearity is neglected and second plant saturation non-linearity is considered. Hybrid Differential Evolutions are acclimated to tune the parameters of PID controller, to procure optimal solution. Optimal control parameters are obtained by minimizing the objective function ITAE (integral time absolute error). Simulations are done to show the performance of PID controlled AVR system tuned utilizing z-n method, differential evolution and hybrid differential evolutions are utilized.
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Optimal Tuning of PID Controller for DC Motor using Bio Inspired Algorithms

Optimal Tuning of PID Controller for DC Motor using Bio Inspired Algorithms

In this paper, a Simulink model of a DC motor has been designed and for controlling the speed, a PID controller based system has been designed; followed by the estimation of PID parameters of Kp Ki and Kd using Ziegler-Nichols method. Since the ZN-PID controllers when implemented for the systems, presents an oscillatory response, so the optimization of the PID gains is carried out by various soft- computing techniques like Genetic Algorithms, Multi- Objective Genetic Algorithm and Stimulated Annealing. The parameters obtained by Ziegler-Nichols have been used as boundary limits for the PID controller optimization populations to provide, faster convergence.
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Optimal Tuning Of UPFC Damping Controller Using Single And Multi-Objective Evolutionary Algorithms

Optimal Tuning Of UPFC Damping Controller Using Single And Multi-Objective Evolutionary Algorithms

concepts like elitism, fast non-dominated sorting approach and diversity maintenance along the Pareto-optimal front. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been successfully applied to various multi-objective engineering optimization problems [19, 20, 22 & 23-29]. Multi-objective algorithm NSGA-II has been used for the tuning of TCSC based damping controller by considering speed deviation and control signal as objectives [24]. The NSGA-II still falls short in maintaining lateral diversity and obtaining Pareto-front with high uniformity. To overcome this shortcoming, controlled elitism concept, which can maintain the diversity of non-dominated front laterally, has been proposed [19]. Also to obtain Pareto-front with high uniformity, Luo et al. have proposed DCD based diversity maintenance strategy [25]. Jeyadevi et al. have suggested Modified NSGA-II by incorporating control elitism and DCD features to ensure better convergence and diversity for solving multi-objective optimal reactive power dispatch problem [23]. Lakshminarasimman et al. have applied MNSGA-II for the optimal placement of mobile antenna [24]. Rajkumar et al. have also applied MNSGA-II for Combined Economic and Emission Dispatch with Valve-point loading of Thermal Generators [26]. Piraisoodi et al. have applied MNSGA-II for optimal nonlinear controller design in boiler turbine system [31]. In the present proposed work, MNSGA- II has been considered for the optimal design of UPFC damping controller (  E ) . Minimizing ISE of the error signal and input control signal (u) gives the optimum performance of the proposed UPFC controller under nominal, light and heavy loading conditions compared to other optimization techniques [32,33. 34]. For the purpose of understanding the benefits of multi-objective algorithms, the UPFC damping controller is also tuned with single objective algorithm PSO along with Integral Squared Error
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A Novel Method for Tuning PID Controller

A Novel Method for Tuning PID Controller

that is impossible to describe the real plant exactly, in case of unbalancing behavioural between controller and plant system. It is necessary to expand the abilities of PID controllers to include new features. As some techniques are better than others for any given application, each method has its advantages and disadvantages [7, 8, 12-14]. Several researchers focused on these drawbacks by developing tuning algorithms. Current trends show that there has been a drastic improvement in tuning using evolutionary algorithms or intelligent techniques, which gives a better result after every iteration [9,13,15,16]. Few computational algorithms frequently used these days, for instance Internal Model Control (IMC), which uses to reduce the error by predicting the output, besides adjusting the controller gain to achieved the desired closed loop response with sophisticated overshoot [10,17]. There is another high-performance tuning, such as Particle Swarm Optimization (PSO) which uses to minimize Overshoot [7, 18-20], and Genetic Algorithm (GA) which has a capability to optimize proportional gains and to overcome the limitations of the nonlinear PID controller [2-4, 21-24]. A considerable amount of research work has also been carried out to develop better tuning techniques, such as artificial neural networks which adapt based on the behavior of a system’s input and output [2,15]. Fuzzy logic techniques usually implement a control strategy derived from linguistic rules [11,25,26]. These controllers can conquer drawbacks with minor changes in parameters despite variable loading. Adaptive controller is another intelligent technique, but there is no guarantee to remain globally stable with large changes in system’s parameters [12,27]. Several researchers have tried to use two sets of evolutionary techniques (heuristic algorithms), for example, Differential
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PSO and PSO-BFO Based Tuning of PID Controller: A Comparative Evaluation

PSO and PSO-BFO Based Tuning of PID Controller: A Comparative Evaluation

But the calculated gains of PID controller may or mayn’t be optimal for practical purposes. Therefore, researchers have used evolutionary algorithms [3] and swarm optimization based techniques [4-6] for PID tuning. Out of many swarm optimization algorithms hybrid particle swarm optimization is one of the emerging algorithms because of its faster convergence and optimal results. This paper presents a comparative performance analysis of PSO and BF-PSO algorithms for optimal PID tuning of shell and tube heat exchanger system. The simulation results show that the hybrid (BF-PSO) algorithm is performs better than particle swarm optimization algorithm (PSO) algorithm in terms of time and frequency domain specifications.
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MIMO PID Controller Tuning Method for Quadrotor Based on LQR/LQG Theory

MIMO PID Controller Tuning Method for Quadrotor Based on LQR/LQG Theory

Several methods have been published to control the fly of SUAV [1,2,5,9–11,13,14,28–35]. In the present work, we propose a control law structure based on a combination of PID and LQR/LQG algorithms but, in contrast to those exposed in others works [5,30–35], a modified LQR/LQG controller is used to obtain the optimal pre-tuning parameters of four PID controllers, commonly employed for attitude and altitude control in multirotor systems (hover maneuver). Additionally, the uncertainty affecting the measurements are taken into account through the errors and noise modeling of a LIDAR, an optical flow camera, and a MEMS type MARG (Magnetic, Angular Rate, Gravity) sensor. The attitude of the vehicle is estimated using the sensor fusion algorithm proposed in [36], whereas the altitude and the vertical velocity are estimated by means of a Kalman filter. Thus, this study considers the effects of measurement errors, including the relatively large bias and noise errors inherent to the MEMS sensors technology, and includes all the required estimation and compensation algorithms necessary for implementation in a real system. A horizontal controller is also included to analyze the influence of an external control loop over the proposed modified LQR/LQG. Next, we study the performance and the robustness of the proposed pre-tuning method through numeric simulations, considering the non-linear model, particularized for the DJI F-450 quadrotor, as well as the rotors dynamics. The influence of uncertainty and bounded perturbations on the robustness of the method is also tested adding parametric uncertainty and significant external disturbances.
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Genetic Algorithms Based PID Controller for Temperature Control of Shell and tube Heat Exchanger System

Genetic Algorithms Based PID Controller for Temperature Control of Shell and tube Heat Exchanger System

This paper has proposed a PID controller tuned using GAs for shell and tube heat exchanger. From simulation results it is concluded GAs based PID tuning method gives superior performance than that of conventional ZN tuning method. Rise time, settling time and peak overshoot are three main parameters for analyzing the performance of PID controller. The objective of PID controller is to regulate the temperature of outlet fluid of shell and tube heat exchanger in shortest possible time and small or no overshoot. ZN based PID controller shows 16.7598% overshoot and 5.3019 sec. settling time whereas GAs based PID controller shows 0% overshoot and 0.1752 sec. settling time. GAs provides 100% improvement in overshoot.
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A Comparative Study of Tuning of PID Controller using Evolutionary Algorithms

A Comparative Study of Tuning of PID Controller using Evolutionary Algorithms

A controller is a device which monitors and affects the operational conditions of a given dynamical system. The operational conditions are typically referred to as output variables of the system which can be affected by adjusting certain input variables. Controllers are required for controlling the given plant so that the plant can perform according to our requirements. The PID controllers are widely used in industrial applications to provide optimal and robust performance for stable, unstable and nonlinear processes [1]. Controllers must be simple and of low order so that easy analysis and required change in the system can be done. The control system performs poor in characteristics and even it becomes unstable, if improper values of the controller tuning constants are used [2]. So it becomes necessary to tune the controller parameters to achieve good control performance with the proper choice of tuning constants. The basic function of controller is to use a proposed tuning algorithm and to maintain the output in the desired or required range of output. Setting of the proportional, integral and derivative values of a controller to get the best possible control for a process using a tuning algorithm is called tuning of a PID controller [3].
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Offline Auto-Tuning of a PID Controller Using Extended Classifier System (XCS) Algorithm

Offline Auto-Tuning of a PID Controller Using Extended Classifier System (XCS) Algorithm

There are several methods in the literature for tuning the PID parameters, which include some modern techniques. Ang et al presented a modern overview of functionalities and tuning methods, software packages and commercial hardware modules [6]. Pyung et al presented asystematic method to select gains of a discrete PID controller [7]. Coelho et al used a chaotic optimization approach based on Lozi map for tuning the PID parameters [8]. He et al presented a new optimal PI/PID controller tuning algorithms via LQR approach [9].Awouda et al demonstrated an efficient method of tuning the PID controller parameters using the optimization rule for ITAE performance criteria [10].Bagis presented an efficient and fast tuning method based on a modified generic algorithm structure to find the optimal parameters of the PID controller [11]. And Stephen et al formulate multi-input multi- output proportional integral derivative controller design as an optimization problem [12].
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