Imperial College London > Talks@ee.imperial > Control and Power Seminars > Adaptive Horizon Model Predictive Control

Adaptive Horizon Model Predictive Control

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If you have a question about this talk, please contact Alessandro Astolfi.

Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying as needed the horizon length of Model Predictive Control (MPC). The goal is to achieve stabilization with horizons as small as possible so that MPC can be used on faster and/or more complicated dynamic processes. A standard requirement of MPC is a terminal cost that is a control Lyapunov function on the terminal set. AHMPC also requires a terminal feedback that turns the control Lyapunov function into a standard Lyapunov function in some domain around the operating point. But this domain need not be known explicitly. MPC does not compute off-line the optimal cost and the optimal feedback over a large domain instead it computes these quantities on-line when and where they are needed. AHMPC does not compute off-line the domain on which the terminal cost is a control Lyapunov function instead it computes on-line when a state is in this domain

This talk is part of the Control and Power Seminars series.

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