Constrained Adaptive Dynamic Programming for Pid Controllers

In this paper, a model-free off-policy approximate constrained adaptive dynamic programming (ACADP) algorithm is developed for discrete-time general nonlinear systems. By using an adaptive learning rate, the controller update is constrained in a well-defined neighborhood to avoid performance degradation in case of high convergence uncertainty of the ACADP mechanism. The learning mechanism is used to tune a Proportional-Integrative-Derivative (PID) controller parameters in a model-free offline fashion. The Markovian assumption is proved for the closed-loop system with a general PID controller to show its applicability in Adaptive Dynamic Programming (ADP) like algorithms. The validation performed on a nonlinear process highlighting the stabilitypreserving attributes of the proposed method using the constrain mechanism.