  In control applications there is often a compromise that needs to be made
with regards to the complexity and performance of the controller and the
computational resources that are available. For instance, the typical hardware
platform in embedded control applications is a microcontroller with limited
memory and processing power, and for battery powered applications the control
system can account for a significant portion of the energy consumption. We
propose a controller architecture in which the computational cost is explicitly
optimized along with the control objective. This is achieved by a three-part
architecture where a high-level, computationally expensive controller generates
plans, which a computationally simpler controller executes by compensating for
prediction errors, while a recomputation policy decides when the plan should be
recomputed. In this paper, we employ model predictive control (MPC) as the
high-level plan-generating controller, a linear state feedback controller as
the simpler compensating controller, and reinforcement learning (RL) to learn
the recomputation policy. Simulation results for two examples showcase the
architecture's ability to improve upon the MPC approach and find reasonable
compromises weighing the performance on the control objective and the
computational resources expended.
