Predictive dual control for nonlinear stochastic systems modelled by neural networks

A predictive dual control for a nonlinear system with functional uncertainty based on the bicriterial approach is proposed and discussed. The nonlinear functions of the system are approximated by multi-layered perceptron neural networks where the unknown parameters are found in real time without a necessity of any off-line training process. These nonlinear predictors based on the affine structure in inputs together with the certainty equivalence principle utilization allow to obtain an analytical solution to the predictive control. Behavior of the system based on the certainty equivalence assumption can negatively be affected, especially in a presence of disturbances and functional uncertainties. For that, the obtained predictive control is enhanced about dual property based on the bicriterial approach that uses two separate criteria to introduce one of the opposing aspects between estimation and control. The quality of the proposed predictive dual controller is illustrated in a numerical example.