Optimistic planning with an adaptive number of action switches for near-optimal nonlinear control

We consider infinite-horizon optimal control of nonlinear systems where the control actions are discrete, and focus on optimistic planning algorithms from artificial intelligence, which can handle general nonlinear systems with nonquadratic costs. With the main goal of reducing computations, we introduce two such algorithms that only search for constrained action sequences. The constraint prevents the sequences from switching between different actions more than a limited number of times. We call the first method optimistic switch-limited planning (OSP), and develop analysis showing that its fixed number of switches S leads to polynomial complexity in the search horizon, in contrast to the exponential complexity of the existing OP algorithm for deterministic systems; and to a correspondingly faster convergence towards optimality. Since tuning S is difficult, we introduce an adaptive variant called OASP that automatically adjusts S so as to limit computations while ensuring that near-optimal solutions keep being explored. OSP and OASP are analytically evaluated in representative special cases, and numerically illustrated in simulations of a rotational pendulum. To show that the algorithms also work in challenging applications, OSP is used to control the pendulum in real time, while OASP is applied for trajectory control of a simulated quadrotor.