Multivariable control of nonlinear process using soft computing techniques

In this paper, Particle Swarm Optimization (PSO) is utilized to optimize the coefficients of a decentralized PID controller for a nonlinear process by minimizing the Integral Square Error (ISE).The controller is tuned at chosen operating points, which are selected to cover the nonlinear range of the process. The optimal PID controller parameters are gain scheduled using a Fuzzy Gain scheduler. The effectiveness of the proposed control scheme has been demonstrated by conducting simulation studies on a Continuous Stirred Tank Reactor (CSTR) process which exhibits dynamic nonlinearity. It is shown that the proposed controller provides better set point tracking and load disturbance rejection than the Internal Model Control (IMC) based conventional control scheme.