AI in control system design using a new paradigm for design representation

A new design representation paradigm different from traditional control system design is proposed. This representation of the control system design problem necessitates an Artificial Intelligence (Al) based search strategy to arrive at solutions. The search is performed by a multicriteria Genetic Algorithm (GA) to achieve Pareto optimal design solutions. The new design representation paradigm is used to implement both linear and non-linear state feedback. We also demonstrate with experimental results how non-linear state feedback expands the search space for the design. As an illustrative example an application of this new representation paradigm to control system design is presented.