A Multiple Particle Swarm Optimization Algorithm And Its Application In PID Parameters Tuning

In the process industry, controlling the process is the main issue. It is very important to keep the process working probably and safely in the industry, for environmental issues and for the quality of the product being processed. From small industry to high technology industry, most of processes are controlled using PID controllers. PID controller is a robust easily understood algorithm that can provide good control performance despite the varied dynamic characteristics of process.However, in order for the PID controller to work probably it has to be tuned which means that a selection of the PID controller parameters has to be made. Hence, how do we optimize the PID controller parameters? Do we still search optimal PID controller parameters using the traditional technique proposed by Ziegler-Nichols? Or do we need to use new approaches to tune the PID in a stochastic manner?In this project, we proposed to tune PID controller parameters using a multiple particle swarm optimization (MPSO) algorithm which is a population metaheuristic approach.Considered as high level strategies for exploring search spaces by using different methods, metaheuristics are a new family of stochastic algorithms used to solve intractable optimization problems. They are divided into two categories:single-solution metaheuristics where a single solution is considered at a time and population metaheuristics where a multiple of solutions evolve concurrently. Used to solve various applicative problems, many researchers have proved the efficiency of these techniques on a large amount of optimization problems.Among that family of stochastic algorithms, particle swarm optimization (PSO) algorithm is the most powerful metaheuristic that has been developed in last decade and used to find the global optimization problems in search space. It is inspired from the social behavior of animals living in swarm, such as bird flocking or fish schooling. The particles of the swarm use a direct way of communication in order to build a solution to the considered problem, based on their collective experience.PSO algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. It is very effective in multi dimensional, linear and nonlinear problems.Recently, researches have revealed that PSO algorithm is even successfully applied to solve the PID parameters optimization problem.For this study, the aim was to improve the searching capability of PSO algorithm for determining the optimal PID controller parameters by implementing a new metaheuristic that combines multiple swarms. The proposed method puts together a number of swarms containing particles that exchange information about the best position in order to build a solution to the considered problem, based on their collective experience.Thus, The PID controller using PSO algorithm has been designed and after, the same model has been redesigned using the MPSO algorithm. The implemented controllers have been called PSO-PID controller and MPSO-PID controller respectively.The simulation results of both designs with matlab programming language have shown that our method performs an efficient search for the optimal PID controller parameters. Comparing with standard PSO-PID controller, the proposed controller was more efficient in improving the step response characteristics such as, reducing the steady-state error; rise time. settling time and overshoot.