Optimal Tuning of PID Controllers Using Iterative Learning Approach

Abstract

The proportional-integral-derivative (PID) controller is the predominant industrial controller that constitutesmore than 90% of feedback loops. Time-domain performance of PID, including peak overshoot, settling time and rise time, is directly dependent on the PID parameters. In this work we propose an iterative learning tuning method (ILT) – an optimal tuning method for PID parameters by means of iterative learning. PID parameters are updated whenever the same control task is repeated. The first novel property of the new tuning method is that the time-domain performance or requirements can be incorporated directly into the objective function to be minimized. The second novel property is that the optimal tuning does not require as much plant-model knowledge as other PID tuning methods. The new tuning method is essentially applicable to any plants that are stabilizable by PID controllers. The third novel property is that any existing PID auto-tuning methods can be used to provide the initial setting of PID parameters, and the iterative learning process can achieve a better PID controller. The fourth novel property is that the iterative learning of PID parameters can be applied straightforwardly to discretetime or sampled-data systems, in contrast to existing PID auto-tuning methods that are dedicated to continuous-time plants. In this chapter, we further exploit efficient search methods for the optimal tuning of PID parameters. Through theoretical analysis, comprehensive investigations on benchmarking examples, and real-time experiments on the level control of a coupled-tank system, the effectiveness of the proposed method is validated.