Wavelet based PID controller using GA optimization and scheduling for feedback systems

Abstract

A feedback system is very important where the system output is taken in to consideration to achieve its desired performance. A very traditional approach for feedback control is proportional, integral and derivative (PID) controller. But this approach fails when nonlinearity or uncertainty creep in to the system. PID controller performance can be enhanced by implementing Multi resolution PID controller (MRPID) by providing better transient and steady state characteristics. Performance of MRPID controller can be improved further by using soft computing optimization techniques as they have gained importance in recent times due to availability of better computing capability at lower cost. Hence, in this paper Genetic algorithm (GA) soft computing technique has been proposed to optimize parameters of a MRPID controller. Then its performance is compared with a MRPID controller without optimized parameters and a conventional PID controller. Feedback loop model of a thermal system has been taken as an illustrative example which is nonlinear in nature. The error signal (Difference between actual and reference temperature) is decomposed using wavelet at different scales. Multi resolution property of wavelet has been utilized to resolve control issue, remove noise and disturbances present in the system. Finally, comparison of the proposed optimized MRPID controller with PID and un optimized MRPID shows that its time domain characteristics are better as compared to each one of them.