Fuzzy Model-Based Output Constraint Satisfaction Mechanism for Controllers of Nonlinear Processes

This paper introduces a novel output constraint satisfaction mechanism that can be used to supplement controllers employing a control law. This mechanism is dedicated to control systems of nonlinear processes, with this additional feature. It utilizes an easy-to-obtain fuzzy model composed of step responses, which includes values of the operating points at which these step responses were obtained. The mechanism is based on a prediction approach from Model Predictive Control (MPC) algorithms. Despite this, it can be used with relatively simple controllers (e.g., fuzzy analytical MPC, PID, or Internal Model Control ones). The mechanism involves skillfully modifying the control signal generated by the controller. It is designed in such a way that, under favorable circumstances, the output constraints are not violated, but in less favorable circumstances, the constraint violation can be minimized. The performance and advantages of our mechanism are demonstrated in the simulated control system of an example nonlinear control plant.