A minimum variance predictive controller for nonlinear systems

The paper presents a minimum variance predictive controller (MVPC) using a modified neural network (MNN) in order to learn the characteristics of a dynamic system. The MVPC can adapt parameter variation and uncertainty in the controlled plant through online learning. The learning algorithm is considerably faster because of the introduction of a recursive least squares (RLS) algorithm. Simulation results show that the proposed approach is effective for adaptive control of nonlinear systems.