Hybrid optimal control with mixed-integer Lagrangian methods

Models involving hybrid systems are versatile in their application, but difficult to handle and optimize efficiently due to their combinatorial nature. This work presents a method to cope with hybrid optimal control problems which, in contrast to decomposition techniques, does not require relaxing the integrality constraints. Based on the discretize-then-optimize approach, our scheme addresses mixed-integer nonlinear problems under mild assumptions. The proposed numerical algorithm builds upon the augmented Lagrangian framework, whose subproblems are handled using successive mixed-integer linearizations with trust regions. We validate the performance of the numerical routine with extensive investigations using several hybrid optimal control problems from different fields of application. Promising preliminary results are presented for a motion planning task with hysteresis and drag, a Lotka-Volterra fishing problem, and a facility location design problem.