Model Reduction for Control-based Continuation of Complex Nonlinear Structures

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The constant drive to improve the performance of engineering structures is leading to new designs which are increasingly lighter and much more flexible. The presence of distributed and localised nonlinearities in such new designs leads to a wide range of complicated dynamic phenomena which are extremely difficult to predict and can lead to catastrophic failures. Control-based Continuation (CBC) is a testing technique that combines sensors, actuators and algorithms to intelligently probe the dynamic behaviour of a physical system. The data collected using CBC proved extremely valuable in understanding nonlinear system responses and developing more accurate mathematical models. While the principles of CBC are extremely general, the method remains limited to simple academic applications due to the simplistic feedback control algorithm currently used. Through this project, the performance of CBC will be improved and its applicability extended to a broader range of systems, including complex engineering structures. In particular, the project will develop model predictive control (MPC) algorithms that are suitable for CBC and exploit state-of-the-art nonlinear model reduction techniques.