Automatic Tuning Of Proportional-Integral-Derivative (Pid) Controller Using Particle Swarm Optimization (Pso) Algorithm

Abstract

The proportionalintegralderivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal proportionalintegral derivative (PID) controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. ZieglerNichols, tuning method was applied in the PID tuning and results were compared with the PSOBased PID for optimum control. Simulation results are presented to show that the PSOBased optimized PID controller is capable of providing an improved closedloop performance over the ZieglerNichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of ZieglerNichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steadystates error; rise time, settling time and maximum overshoot in speed control of DC motor.