An Adaptive PID Controller Based on Bayesian Theory

Abstract

One of the most popular trajectory-tracking controllers used in industry is the PID controller. The PID controller utilizes three types of gains and the tracking error in order to provide a control gain to a system. The PID gains may be tuned manually or using a number of different techniques. Under most operating conditions, only one set of PID gains are used. However, techniques exist to compensate for dynamic systems such as gain scheduling or basic timevarying functions. In this paper, an adaptive PID controller is presented based on Bayesian theory. The interacting multiple model (IMM) method, which utilizes Bayes' theorem and likelihood functions, is implemented on the PID controller to present an adaptive control strategy. The strategy is applied to a simulated electromechanical system, and the results of the proposed controller are compared with the standard PID method. Future work is also considered.