Pre-Stabilized Energy-Optimal Model Predictive Control

Xin Wang, Julian Stoev, and Jan Swevers


Motion control, Model based control, Energy optimal control, Embedded mechanical systems


This paper presents Pre-stabilized Energy-optimal Model Predictive Control which is developed based on the existing Energy-Optimal Model Predictive Control (EOMPC) approach. EOMPC is a control method to realize energy-optimal point-to-point motions within a required motion time. In order to obtain a sufficiently large prediction time horizon with a limited number of decision variables resulting in less computational load and solving the optimization problem within the chosen sampling time, non-equidistant time intervals are used over the prediction horizon. This approach is called blocking. However blocking yields a non-smooth optimal solution and as a result the energy-optimality is only approximately achieved. In order to overcome this drawback, this paper proposes a pre-stabilization strategy to reduce the computational load of EOMPC. Pre-stabilization uses deadbeat state feedback to modify the system models employed in the formulation of MPC and yields a much sparser optimization problem. The significant advantage of the pre-stabilization on computational speed of MPC optimization problems is clarified. The computational efficiency and performance of EOMPC with pre-stabilization is validated through numerical simulations.

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