Optimization of PD-PI Controller Using Swarm Intelligence (original) (raw)
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This paper presents stochastic search techniques, including Particle Swarm Optimization (PSO), Constriction Coefficient Particle Swarm Optimization (CPSO) and Differential Evolution (DE) algorithms for determining optimal Proportional-Integral-Derivative (PID) controller parameters attached to the Rotational Inverted Pendulum (RIP) system. This paper demonstrates in detail how to employ these proposed algorithms to optimize the performance index for balancing the pendulum in vertical-upright position. The efficiency of these intelligent strategies to tune PID gains is compared and evaluated based on the time response performance. The simulation results clearly demonstrate superior features of proposed tuning approaches, including easy implementation, and good computational efficiency. The overall results have validated that CPSO method yields better performance in control action compared to PSO and DE. The proposed approaches could generally be considered as an encouraging way for control of nonlinear industrial plants.
Optimal Tuning of PID Controller Using Genetic Algorithm and Swarm Techniques
In order to control the systems, a few control strategies must deal with the effects of non-linearties or uncertainties. As earlier utilized control techniques based on mathematical models have been primarily concentrated on stability robustness against the ill-effects of control mechanism, they are limited in their ability to amend the transient responses. These conventional techniques failed to tune the non-linear and non-minimum phase systems. Therefore, a few modern algorithms have been introduced here such as; Bacteria Foraging Optimization, Particle Swarm Optimization and Genetic Algorithm which have been proved an appropriate aid to improve the transient responses of systems perturbed by non-linearties or unknown mathematical characteristics. This Paper presents designing a PID controller by selection of PID parameters using Bacterial Foraging Optimization, Particle Swarm Optimization (PSO) and Genetic Algorithm. Here, the closed loop step response of the PID controller has been compared for the above mentioned three optimization algorithms.
2018
Linear control systems can be easily tuned using conventional tuning techniques such as the Ziegler-Nichols and Cohen-Coon tuning formulae. Empirical studies have found that these conventional tuning methods result in an unsatisfactory control performance when they are used for industrial processes. It is for this reason that control practitioners often prefer to tune most nonlinear systems using trial and error tuning, or intuitive tuning. A need therefore exists for the development of a suitable automatic tuning technique that is applicable for a wide range of control processes that do not respond satisfactorily to conventional tuning. The balancing of an inverted pendulum by moving a cart along a horizontal track is a classic problem in the area of control. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the PSO when compared with the performance of PID and Ziegler-Nichols (Z-N) makes PSO-PID a good addition to solving PID Controller tuning problems using metaheuristic techniques as will reduce the time and cost of tuning these parameters and improve the overall system performance.
Performance of Pid Controller of Nonlinear System Using Swarm Intelligence Techniques
ICTACT Journal on Soft Computing, 2016
In this paper swarm intelligence based PID controller tuning is proposed for a nonlinear ball and hoop system. Particle swarm optimization (PSO), Artificial bee colony (ABC), Bacterial foraging optimization (BFO) is some example of swarm intelligence techniques which are focused for PID controller tuning. These algorithms are also tested on perturbed ball and hoop model. Integral square error (ISE) based performance index is used for finding the best possible value of controller parameters. Matlab software is used for designing the ball and hoop model. It is found that these swarm intelligence techniques have easy implementation & lesser settling & rise time compare to conventional methods.
Optimal Tuning of PI Controller using Swarm Intelligence for a Nonlinear Process
2013
This article deals with an application of Particle Swarm Optimization (PSO) to obtain a optimum PI controller settings for a non linear process. In this work, a conical tank level process is identified as first order plus dead time model (FOPTD). Control of level in conical tank is a complex issue but in the presence of gravity discharge flow conical tanks are used in industrial unit. PSO algorithm is used to tune the parameters of PI controller to control the level of the liquid in the conical tank. Efficacy of proposed method has been validated through a comparative study with gain scheduling method. The proposed method has excellent features of high computational efficiency. In this paper optimum values of PI controller settings are found and it is proved that the PSO based tuned PI values gives better results than the gain scheduling method. Hence the results demonstrates that the tuning of PI controller using PSO technique gives minimum rise time, minimum settling time than Gai...
Evaluations of Optimum Value of PID Controller Gains Using Hybrid Bacterial Swarm Optimization
In the control system problems it is challenging to find the PID parameters in the initial stage, and there fine tuning during the system run condition. In this paper we have test the application of hybrid bacterial foraging and particle swarm optimization algorithm named as bacterial swarm optimization BSO) based PID parameter tuning of close loop controller. The objective is dependent on globally minimal error squared error integral criteria of the step response of second order and higher order plants cascaded with PID controller by our proposed method. In this algorithm parameters are found by evolutionary methods with consideration of the globally optimal solution for control applications. The Kp, Ki and Kd gains are calculated by the PSO and (BSO) methods for plants with all the poles of the transfer function located in the left half of the s-plane. The performance of both algorithms is analyzed on transfer functions of a second order and higher order plants.
IJERT-Evaluations of Optimum Value of PID Controller Gains Using Hybrid Bacterial Swarm Optimization
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/evaluations-of-optimum-value-of-pid-controller-gains-using-hybrid-bacterial-swarm-optimization https://www.ijert.org/research/evaluations-of-optimum-value-of-pid-controller-gains-using-hybrid-bacterial-swarm-optimization-IJERTV2IS110994.pdf In the control system problems it is challenging to find the PID parameters in the initial stage, and there fine tuning during the system run condition. In this paper we have test the application of hybrid bacterial foraging and particle swarm optimization algorithm named as bacterial swarm optimization (BSO) based PID parameter tuning of close loop controller. The objective is dependent on globally minimal error squared error integral criteria of the step response of second order and higher order plants cascaded with PID controller by our proposed method. In this algorithm parameters are found by evolutionary methods with consideration of the globally optimal solution for control applications. The Kp, Ki and Kd gains are calculated by the PSO and (BSO) methods for plants with all the poles of the transfer function located in the left half of the s-plane. The performance of both algorithms is analyzed on transfer functions of a second order and higher order plants.
Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller
Energies, 2021
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...
A Particle Swarm Optimization approach for optimum design of PID controller for nonlinear systems
2013
In this paper,a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters for Takagi-Sugeno fuzzy model using the particle swarm optimization (PSO) algorithm is presented. In order to assist estimating the performance of the proposed PSO-PID controller,a new timedomain performance criterion function has been used. The proposed approach yields better solution in term of rise time, settling time,maximum overshoot and steady state error condition of the system.the proposed method was indeed more efficient and robust in improving the step response.
Application of Swarm Intelligence Computation Techniques in PID Controller Tuning: A Review
Advances in Intelligent and Soft Computing, 2012
Swarm Intelligence Computation technique is one of the recent and advanced research topic in the field of Artificial Intelligence. This natureinspired, global optimization technique is used rapidly in various fields , specially it has become one of the most useful method for efficiency improvement of control and distributed optimization aspects. A review study on tuning of PID controller with effective and satisfactory performance analysis via different swarm intelligence computation techniques is presented in this paper. Tuning of PID via traditional methods and genetic algorithm and their limitations in proper tuning, different structure of PID controllers with the objectives for PID tuning and an efficient intelligent PID controller design is presented in the beginning of this paper. Then a brief literature review on PID tuning with different Swarm Intelligence(SI) techniques i.e. Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO), and Bacterial Foraging Optimization Algorithm(BFOA) as well as their advantages and disadvantages in proper tuning is presented in the afterwards . And finally a performance comparison with simulation results of PID tuning via ZN, GA, PSO, BFOA are experimented on four set of system transfer functions and are studied for effective analysis.