A probabilistically constrained model predictive controller

We propose a novel control algorithm, probabilistically constrained predictive control, to deal with the uncertainties of system disturbances. The output is to be controlled in the constrained range with a desired probability. Under the assumption of a linear system, the formulated joint probabilistically constrained problem is convex. Thus, it can be solved with a nonlinear programming solver. The probabilities and gradients of the constraints, composed of disturbance sequences with multivariate normal distribution, are computed using an efficient simulation approach. The results of a test problem show the effectiveness of the proposed algorithm. (C) 2002 Elsevier Science Ltd. All rights reserved.