NUMERICALLY APPROXIMATED RECEDING HORIZON CONTROL FOR UNCERTAIN PURSUIT-EVASION GAMES

A robust technique for handling parameter and strategy uncertainty in a pursuit evasion framework is developed. The method uses a receding horizon controller designed for singularly perturbed trajectories. The controller approximates the optimal feedback solution with small loss in optimality while remaining robust to incorrect information about an opposing player's dynamics or strategy. A simple analytic pursuit-evasion game motivates the method by demonstrating that the receding horizon solution closely approximates the optimal solution and may be solved much faster. Simulations of a nonlinear game show that the receding horizon controller is especially useful when it is unknown whether the opposing player is enacting an active or passive maneuver. In several cases, the receding horizon controller is shown to become more effective than a game-optimal controller acting with an incorrect strategy estimate. The major limitation of the technique for a nonlinear system is the expensive solution time; therefore, the optimal control problem is transformed to a nonlinear programming problem and the test cases are repeated to validate the method for real-time hardware operation.