Functional Adaptive Control for Multi-Input Multi-Output Systems

A functional adaptive control for nonlinear stochastic systems with Multi-Input Multi-Output is suggested. The systems are modelled using a multilayer perceptron networks. Parameters of the model are estimated by the Gaussian sum method which allows to determine conditional probability density functions of the network weights. Control design is based on bicriterial dual approach that use two separate criterions to introduce one of opposing aspects between estimation and control; caution and probing. The proposed approach is compared with two adaptive non-dual controllers. The quality of the proposed functional adaptive controller is illustrated in a numerical example.