Adaptive fuzzy control of switched objective functions in pursuit-evasion scenarios

In recent efforts, the authors have derived simple switched control schemes that qualitatively yield an attractive performance in two player pursuit-evasion games. A drawback of these methods is that detailed knowledge of an opponent´s dynamics and strategy is required to implement the switching controller. Furthermore, an objective evaluated over a finite horizon may not guide an agent to the target set. To circumvent this potential shortcoming, a switching scheme is proposed where an adaptive fuzzy controller chooses the best objective function from a predefined library to increase the agent´s reachability. The methodology we present builds on the common approximate dynamic programming reinforcement learning technique. We give conditions for showing when the controller is applicable and give an implementation example with the Homicidal Chauffeur problem.