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 constrai nts is proposed. To enable the bounded nonlinear controller to learn to con trol a changing plant by merely observing the process output errors, a simp le learning algorithm for parameter updating is derived based on the Lyapun ov stability theorem. The learning scheme is easy to implement, and does no t require any a priori process knowledge except the system output response direction. For demonstrating the effectiveness and applicability of the lea rning control strategy, the control of a once-through boiler, as well as an open-loop unstable continuously stirred tank reactor (CSTR), were investig ated. Furthermore, extensive comparisons of the proposed scheme with the co nventional PI controller and with some existing model-free intelligent cont rollers were also performed. Due to significant features of simple structur e, efficient algorithm and good performance, the proposed learning control strategy appears to be a promising and practical approach to the intelligen t control of process systems subject to hard input constraints. (C) 1999 El sevier Science Ltd. All rights reserved.