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Control and Optimization

Control and Optimization

Starting with a dynamic model, we develop optimal controllers for automotive systems and human movements. Symbolic computing facilitates sensitivity analysis and the optimization used in our model-based controllers, which incorporate Pontryagin’s minimum principle, nonlinear model-predictive control, and other optimal control theories. Homotopy optimization has proven to be well-suited for model parameter identification from noisy or incomplete experimental data, and graph theory has been exploited in our topology optimization of mechanisms and hybrid vehicles.