Neural Network-Based Model Predictive Control: Fault Tolerance and Stability

Abstract

This brief deals with nonlinear model predictive control designed for a tank unit. The predictive controller is realized by means of a recurrent neural network, which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. An important issue in control theory is stability of the control system. In this brief, this problem is investigated by showing that a cost function is monotonically decreasing with respect to time. The derived stability conditions are then used to redefine a constrained optimization problem in order to calculate a control signal. As the automatic control system can prevent faults from being observed, the control system is equipped with a fault diagnosis block. It is realized by means of a multivalued diagnostic matrix, which is determined on the basis of residuals calculated using a set of partial models. Each partial model is designed in the form of a recurrent neural network. This brief proposes also a methodology of compensating sensor, actuator, as well as process faults. When a sensor fault is isolated, the system estimates its size and, based on this information, the controller is fed with a determined, close to real, tank level value. Actuator and process faults can be compensated due to application of an unmeasured disturbance model.