This paper presents a tuning approach based on Continuousfireflyalgorithm (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 PIDcontroller 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 AVRsystem. 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.
Abstract: In this Paper, a novel meta-heuristics algorithm, namely the FireflyAlgorithm (FA) is applied to the Proportional Integral Derivative (PID) Controller parameter tuning for Automatic Voltage Regulator System (AVR). The main goal is to increase the time domain characteristics and reduce the transient response of AVR systems. This paper described in details how to employ FireflyAlgorithm to determine the optimalPIDcontroller parameters of an AVRsystem. The proposed algorithm can improve the dynamic performance of AVRsystem. Compared with Ziegler Nichols (Z-N), Particle Swarm Optimization (PSO) methods, it has better control system performance in terms of time domain specification.
In recent literature many evolutionary optimization algorithms are proposed for tuningPIDcontroller in the AVRsystem such as Anarchic Society Optimization , reinforcement learning automata optimization approach , real coded GA with fuzzy logic technique , Choatic ant swarm algorithm , Artificial Bee Colony algorithm , Hybrid GA-Bacterial Foraging (BF) algorithm  and local unimodal sampling algorithm . GA and Ant Colony Optimization techniques are proposed to tune the parameters of FOPID controller in controlling of AVRsystem. In some of the research papers novel performance criteria has been proposed for optimaltuning 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 optimaltuning of PIDcontroller in AVRsystem using cuckoo search algorithm . A. Sikander et. al, 2018 has proposed a cuckoo search algorithm based fractional order PIDcontroller for AVRsystem with performance criterion which was proposed by Gaing et. al in 2004 . In this research work, Cuckoo search (CS) and particle swarm optimization (PSO) algorithms are proposed to find the optimal parameters of PIDcontroller 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 optimaltuningPIDcontroller for both servo and regulatory control of AVRsystem. Additionally, section six describes conclusions of the study.
This paper presents a SNR-PSO PIDcontroller for searching the optimalcontroller parameters of AVR. In this section, a PIDcontroller using the SNRPSO algorithm was developed to improve the step transient response of an AVRsystem. Signal-to-Noise Ratio (SNR) algorithm are used in this paper to evaluate existence possibility of optimal value in PID parameters. This algorithm does not 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. Signal-to-Noise Ratio algorithm has a responsible result in the nonlinear systems optimization. Signal-to-Noise Ratio (SNR) is a measure of the variation within a trial when noise factors present. It looks like a response which consolidates repetitions and reflects noise levels into one data point. SNR consolidates several repetitions into one value that reflects the amount of variation present. There SNR are defined depending on the type of characteristic desired, higher is better (HB), lower is better (LB) and nominal is best (NB). The equations for calculating S/N ratios for HB, LB or NB characteristics are given as follows :
Electrical motor servo systems are indispensable in modern industries. Servo motors are used in a variety of applications in industrial electronics and robotics that includes precision positioning as well as speed control . Servomotors use feedback controller to control the speed or the position, or both. The basic continuous feedback controller is PIDcontroller which possesses good performance. However is adaptive enough only with flexible tuning. Although many advanced control techniques such as self-tuning control, model reference adaptive control, sliding mode control and fuzzy control have been proposed to improve system performances, the conventional PI/PID controllers are still dominant in majority of real-world servo systems .
Particle Swarm Optimization (PSO) technique, proposed by Kennedy and Eberhart is an evolutionary - type global optimization technique developed due to the inspiration of social act ivities in flock of birds and school of fish and is widely applied in various engineering problems due to its high computational efficiency . It has been proved to be an effective optimum tool in system identification and PIDcontrollertuning for a class of processes. This techniques is used to minimize the maximum overshoot, minimize the rise time, minimize speed tracking error, minimize the steady state error, and minimize the settling time, optimization solution results are set of near optimal trade-off value which are called the Pareto front or optimally surfaces. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarms. The components of PSO are Swarm Size, Velocity, position components and maximum no of iteration. The structure of the PIDcontroller with PSO algorithm is shown in Fig.3.
GA is an optimization technique inspired by the mechanisms of natural selection.GA starts with an initial population containing a number of chromosomes where each one represents a solution of the problem in which its performance is evaluated based on a fitness function. Based on the fitness of individual and defined probability, a group of chromosomes is selected to undergo three common stages: selection, crossover and mutation. The application of these three basic operations will allow the creation of new individuals to yield better solutions than the parents, leading to the optimal solution. The features of GA illustrated in the work by considering the problem of designing a control system for a plant of a first order system with time delay and obtaining the possible results. The future scope of this work is aimed at providing a self-tuningPIDcontroller with proposed algorithm (Particle Swarm Optimization - PSO) so as to solve the complex issues for real time problems.
Many PIDtuning methods are introduced. The Ziegler-Nichols method is widely used for ControllerTuning. One of the disadvantage of this method is prior knowledge regarding plant model. Once tuned the controller by Ziegler Nichols method, a good but not optimum system response will be reached. The Transient response can be even worse if the plant dynamics change. To assure an environmentally independent good performance, the controller must be able to adapt the changes of the plant dynamic characteristics. For these reasons, it is highly desirable to increase the capabilities of PID controllers by adding new features. Many random search methods, such as Genetic Algorithm (GA) have received much interest for achieving high efficiency and searching global optimal solution in the problem space.
Sahu et al.  have outlined around the design and style as well as effectiveness evaluation regarding Differential Evolution (DE) algorithm based parallel 2-Degree Freedom of Proportional-Integral-Derivative (2-DOF PID) controller for Load Frequency Control (LFC) of interconnected power system process. The planning issue has been formulated as an optimization issue and DE has been currently employed to look for optimalcontroller parameters. Standard as well as improved aim features have been used for the planning goal. Standard aim features currently employed, which were Integral of Time multiplied by Squared Error (ITSE) and Integral of Squared Error (ISE). To be able to additionally raise the effec- tiveness in the controller, some sort of improved aim operate is derived making use of Integral Time multiply Absolute Error (ITAE), damping ratio of dominant eigenvalues, settling times of frequency and peak overshoots with appropriate weight coefﬁcients. The particular ﬁneness in the recommended tech- nique has become conﬁrmed by simply contrasting the results with a lately published strategy, i.e. Craziness based Particle Swarm Optimization (CPSO) for the similar interconnected electric power process. Further, level of sensitivity evaluation has been executed by simply varying the machine details as well as managing load conditions off their nominal valuations. It is really observed which the recommended controllers are quite powerful for many the system parameters as well as man- aging load conditions off their nominal valuations.
Abstract: AVR (Automatic Voltage Regulator) plays a key role in generating stations. To maintain voltage stability of the generator the terminal voltage should remain constant all the times. In a large interconnected system manual regulation is much complicated and therefore automatic generation and voltage regulation is necessary. So, to maintain a constant voltage level, Automatic voltage regulators are used at each generating station. This paper presents the MATLAB simulation based on Ant Colony Optimization (ACO) technique used for the tuning of the PID(Proportional, Integral, Derivative) controllers which are used for AVRsystem. Ant Colony Optimization technique is recognized from the behavior of real ants within the colony to find optimum gain value in a shortest time period. In this paper, an attempt has been made to find out an optimal gain values for tuning of PIDcontroller based on ACO algorithm to improve the overall transient performance of an AVRsystem for the control of terminal voltage following disturbance through MATLAB simulation.
The PIDController is an example of a control loop feedback technique that is used in engineering, industrial and process control systems. The PID works optimally in electrical, electronic or electromechanical systems that has precise and distinguishable mathematical model. The PIDController, basically, calculates three separate system parameters: proportional, integral and derivative coefficients. The proportional component of PID computes the value of the very current error. Similarly, the integral component calculates the result of the sum of recent errors while the derivative component is involved with the determination of the system’s reaction based on the rate dynamism of the errors. The weighted sum of the functions of the three PID components is then imported into the control system to regulate the AVR. Some of the different AVR models with input PID controllers are presented in Figures1-4.
Initially, the design of PIDA controller parameters have been determined using analytical method. In this method two characteristic equations; one formed with desired root loca- tions with specifications based on the design criterion, and another one with the nominal control structure were equated to deduce the parameters of PIDA. It have been considered to design PIDA controller for third order systems and extended for the control of an AC motor system . The application of PIDA were considered for a servo motor driving a load through a long shaft or transmission system in . Sambariya and Paliwal, 2016 have presented the optimaltuning of PIDA controller parameters using harmony search algorithm in .
In the design of PID controllers, Ziegler-Nichols settings give an oscillatory response of the system; hence the parameters found by this method cannot be implemented in the designs as computed. However, it can be used with the genetic algorithm to form possible interval for the controller parameter set. Since determining precise intervals for these gains have not been developed yet, it is wise to use these gains as the first step towards tuning the controller. For example, the lower and upper bounds for the design parameters can be formed around the values found from Ziegler-Nichols method, such as one-third for lower bound and three-fold for the upper bound
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 . Pyung et al presented asystematic method to select gains of a discrete PIDcontroller . Coelho et al used a chaotic optimization approach based on Lozi map for tuning the PID parameters . He et al presented a new optimal PI/PIDcontrollertuning algorithms via LQR approach .Awouda et al demonstrated an efficient method of tuning the PIDcontroller parameters using the optimization rule for ITAE performance criteria .Bagis presented an efficient and fast tuning method based on a modified generic algorithm structure to find the optimal parameters of the PIDcontroller . And Stephen et al formulate multi-input multi- output proportional integral derivative controller design as an optimization problem .
We extended the benefits of P ⋋ controller for AVRsystem. In Particular, a masked P ⋋ controller is developed in matlab by programming to optimize the parameters based on the recently developed optimization techniques instead of using toolbox which was restrained for using advanced optimization techniques. Genetic Algorithm (GA) technique has already been used to determine optimal solution to several power engineering problems and we employed these algorithms to design an FOPID controller for Automatic voltage regulator (AVR) problem. The proposed controller is simulated within various scenarios and its performance is compared with those of an optimally-designed PIDcontroller. Transient response and performance robustness characteristics of both controllers are studied and superiority of the proposed controller in all two respects is illustrated.
In this paper, a Simulink model of a DC motor has been designed and for controlling the speed, a PIDcontroller 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 PIDcontroller optimization populations to provide, faster convergence.
controllertuning methods may require complex computations to identify the controller parameters. With the evolution of soft computing methods these classical control methods were eliminated. This paper focus on design method for determining the optimal proportional-integral-derivative (PID) controller parameters of linear system using the particle swarm optimization (PSO) algorithm. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. At first genetic algorithm (GA) of automatic voltage regulator(AVR) setting is designed and compared with PSO based PIDcontroller settings. The proposed method has more robust stability and efficiency, and can solve the searching and tuning problems of PIDcontroller parameters more easily and quickly than the GA method.
solving continuous nonlinear optimization problems. The PSO technique can generate a high-quality solution within shorter calculation time and stable convergence characteristic than other stochastic methods [4, 6]. Bacterial Foraging Optimization (BFO) is a population-based numerical optimization algorithm. Until date, BFO has been applied successfully to some engineering problems, such as optimal control, harmonic estimation, transmission loss reduction and machine learning [7,8]. However, experimentation with complex optimization problems reveals that the original BFO algorithm possesses a poor convergence behaviour compared to other nature-inspired algorithms and its performance also heavily decrease with the growth of the search space dimensionality. BF-PSO algorithm combines both BFO and PSO. The aim is to make PSO ability to exchange social information and BF ability in finding new solution by elimination and dispersal, a unit length direction of tumble behaviour is randomly generated.
feedback controller analyzed for temperature control of continuous stirred tank reactors (CSTRs) which have strong non linearities has been done in .The control objective in his simulation-based work is to maintain the CSTR at steady state operating point. Methodologies to learn and optimize fuzzy logic controller parameters based on neural network and genetic algorithm has been developed in .Designing of PIDcontroller has been done in . Ziegler et al  has proposed a system of units for measuring the control effects, which are now in common use. Wang-Xiao Kan et al  introduces a design method of fuzzy self-tuningPIDcontroller and make use of MATLAB fuzzy toolbox to design fuzzy controller, organically combine fuzzy PIDcontroller with Simulink. Development of a Linear Quadratic Regulator (LQR) for a set of time-varying hyperbolic PDEs coupled with a set of time-varying ODEs through the boundary has been discussed in . Qingsi Zhanget al  uses Fuzzy logic controller with self-tuningPID parameter. CSTR system has been discussed in .
Noise corrected improved algorithm settle down in very short duration of time and overshoot approximately equal to less than 1% (Except 3-phase Induction Motor). In the noisy environment improved algorithm with a noise, the correction method is suggested. Thus, the new tuningalgorithm tuned PIDcontroller for faster response and better noise rejection property so that this algorithm is robust in the noisy environment. The roots of r1 and r2 should be real only and left the side of root locus for better stability and performance of the algorithm. The three-phase induction motor control by using SAM-PIDcontroller is more stable and efficient. The auto tune algorithm is robust and demonstrates the performance in noisy environment. Thus, it can be used in various ways. To improve the system performance SAM-PIDcontroller also has the feature of adaptive and optimal controlling.