Near-optimal strategies for nonlinear networked control systems using optimistic planning

We consider the scenario where a controller communicates with a general nonlinear plant via a network, and must optimize a performance index. The problem is modeled in discrete time and the admissible control inputs are constrained to belong to a finite set. Exploiting a recent optimistic planning algorithm from the artificial intelligence field, we propose two control strategies that take into account communication constraints induced by the use of the network. Both resulting algorithms have guaranteed near-optimality. In the first strategy, input sequences are transmitted to the plant at a fixed period, and we show bounded computation. In the second strategy, the algorithm decides the next transmission instant according to the last state measurement (leading to a self-triggered policy), working within a fixed computation budget. For this case, we guarantee long transmission intervals. Examples and simulation experiments are provided throughout the paper to illustrate the results.