A new approach to adaptive control of multi-input multi-output systems using multiple models

This paper proposes a novel method as second level adaptation using multiple models to identify and control of a class of multi-input multi-output (MIMO) systems. Different uncertain environments change the system parameters and create multiple operating conditions. These conditions are designed as multiple identification models in a model bank using adaptive laws. These models are evaluated using some estimated weighting factors based on the errors between each of the models and the actual plant. The evaluated models are effectively used in identification and control process. Bounded signals, proper closed-loop tracking performance, and rapid and accurate parameter convergence to their real values are achieved through simulation results.