PSO-Based PID Controller Design for a Class of Stable and Unstable Systems

Nonlinear processes are very common in process industries, and designing a stabilizing controller is always preferred to maximize the production rate. In this paper, tuning of PID controller for a class of time delayed stable and unstable process models using Particle Swarm Optimization (PSO) algorithm is discussed. The dimension of the search space is only three ( , , and ); hence, a fixed weight is assigned for the inertia parameter. A comparative study is presented between various inertia weights such as 0.5, 0.75, and 1. From the result, it is evident that the proposed method helps to attain better controller settings with reduced iteration number. The efficacy of the proposed scheme has been validated through a comparative study with classical controller tuning methods and heuristic methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Finally, a real-time implementation of the proposed method is carried on a nonlinear spherical tank system. From the simulation and real-time results, it is evident that the PSO algorithm performs well on the stable and unstable process models considered in this work. The PSO tuned controller offers enhanced process characteristics such as better time domain specifications, smooth reference tracking, supply disturbance rejection, and error minimization. 1. Introduction In process industries, many important real-time processing units such as Continuous Stirred Tank Reactor (CSTR), biochemical reactor, and spherical tank system are highly nonlinear in nature. Tuning of controllers to stabilize these nonlinear chemical process loops and impart adequate disturbance rejection is critical because of their complex nature. Based on the operating regions, most of the chemical loops exhibit stable and/or unstable steady states. Controller tuning is an essential preliminary procedure in almost all industrial process control systems. In control literature, a number of controller structures are available to stabilize stable, unstable, and nonlinear processes [1–5]. Designing controller for process with stable operating region is quite simple. For unstable systems, there exist minimum and maximum values of controller gain, and the average of this limiting value is considered to design the controller to stabilize the system. The increase in time delay in the process narrows down the limiting value and restricts the performance of closed loop system under control. In addition, these systems show unusual overshoot or inverse response due to the presence of positive zeros [4]. Despite the significant developments