Hybrid Optimal Control under Mode Switching Constraints with Applications to Pesticide Scheduling

This paper concerns optimal mode-scheduling in autonomous switched-mode hybrid dynamical systems, where the objective is to minimize a cost-performance functional defined on the state trajectory as a function of the schedule of modes. The controlled variable, namely the modes' schedule, consists of the sequence of modes and the switchover times between them. We propose a gradient-descent algorithm that adjusts a given mode-schedule by changing multiple modes over time-sets of positive Lebesgue measures, thereby avoiding the inefficiencies inherent in existing techniques that change the modes one at a time. The algorithm is based on steepest descent with Armijo step sizes along Gateaux differentials of the performance functional with respect to schedule-variations, which yields effective descent at each iteration. Since the space of modeschedules is infinite dimensional and incomplete, the algorithm's convergence is proved in the sense of Polak's framework of optimality functions and minimizing sequences. Simulation results are presented, and possible extensions to problems with dwell-time lower-bound constraints are discussed.