Intelligent adaptive control using multiple models

We develop a new intelligent adaptive control algorithm that is applicable to systems with large parameters variations and multiple operating modes. It uses a set of models, called the dynamic model bank, that guides the adaptation process. The dynamic model bank summarizes the parameters of the models that successfully approximate the plant. The model bank is automatically created and updated and does not call for an initial set of models. It uses a soft switching mechanism that provides a smooth transition from an interpolative to a pure "hard" switching scheme between the models in the bank. We also demonstrate the advantage of using this approach on several examples considering the control of systems with large parameter variations.