An indirect computational procedure for receding horizon hybrid optimal control
For researchers in hybrid systems and model predictive control, this work offers a computationally efficient indirect method for solving finite horizon hybrid optimal control problems.
The paper proposes a branch and bound algorithm for receding horizon hybrid optimal control that reduces the problem to solving algebraic equations for affine hybrid systems, and demonstrates efficiency through a case study comparison with existing methods.
In this work, solution of the finite horizon hybrid optimal control problem as the central element of the receding horizon optimal control (model predictive control) is investigated based on the indirect approach. The response of a hybrid system within the prediction horizon is composed of both discrete-valued sequences and continuous-valued time-trajectories. Given a cost functional, the optimal continuous trajectories can be calculated given the discrete sequences by the means of the recent results on the hybrid maximum principle. It is shown that these calculations reduce to solving a system of algebraic equations in the case of affine hybrid systems. Then, a branch and bound algorithm is proposed which determines both the discrete and continuous control inputs by iterating on the discrete sequences. It is shown that the algorithm finds the correct solution in a finite number of steps if the selected cost functional satisfies certain conditions. Efficiency of the proposed method is demonstrated during a case study through comparisons with the main existing method.