Constrained Nonlinear Predictive Control Based on Input-Output Linearization Using a Neural Network

Abstract Affine neural network models can be used as good aproximators of the dynamics of a nonlinear process, and are easily included in a input-output feedback linearization (IOFL) scheme. This paper proposes a new solution for solving a constrained optimization problem using IOFL imbedded in a predictive control scheme. The linearization of the nonlinear feedback law over the entire prediction horizon, enables an optimal solution to be found by solving a general quadratic programming problem. The procedure here presented also guarantees convergenceto a feasible solution without constraint violation.