Learning control of process systems with hard input constraints

In this paper, a novel and simple learning control strategy based on using a bounded nonlinear controller for process systems with hard input constraints is proposed. To enable the bounded nonlinear controller to learn to control a changing plant by merely observing the process output errors, a simple learning algorithm for parameter updating is derived based on the Lyapunov stability theorem. The learning scheme is easy to implement, and does not require any a priori process knowledge except the system output response direction. For demonstrating the e€ectiveness and applicability of the learning control strategy, the control of a once- through boiler, as well as an open-loop unstable continuously stirred tank reactor (CSTR), were investigated. Furthermore, exten- sive comparisons of the proposed scheme with the conventional PI controller and with some existing model-free intelligent con- trollers were also performed. Due to signi®cant features of simple structure, ecient algorithm and good performance, the proposed learning control strategy appears to be a promising and practical approach to the intelligent control of process systems subject to hard input constraints.