Data-Driven Constrained PID Controller Design for CSTR

Since most chemical processes exhibit severe nonlinear and time-varying behavior, the control of such processes is challenging. In this paper, we propose data-driven constrained controller design method based on lazy learning for chemical processes. Using a lazy learning algorithm, a local valid linear model denoting the current state of system is automatically exacted for adjusting the PID controller parameters based on input/output data. This scheme can adjust the constrained PID parameters in an online manner even if the system has nonlinear properties. At same time, in order to solve the control input saturation problem, a dynamic anti-windup compensator is proposed for accommodating the reference. The simulation results on the dynamic model of Continuous Stirred Tank Reactor (CSTR) are provided to demonstrate the effectiveness of the proposed new control techniques.