Recurrent Model Predictive Control
This is an incremental improvement for control systems, offering a more efficient method for optimal control problems.
The paper tackles the problem of solving general nonlinear finite-horizon optimal control by proposing Recurrent Model Predictive Control (RMPC), which adaptively selects prediction horizons and converges to optimal policies, as demonstrated with numerical examples.
This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems. Unlike traditional Model Predictive Control (MPC) algorithms, it can make full use of the current computing resources and adaptively select the longest model prediction horizon. Our algorithm employs a recurrent function to approximate the optimal policy, which maps the system states and reference values directly to the control inputs. The number of prediction steps is equal to the number of recurrent cycles of the learned policy function. With an arbitrary initial policy function, the proposed RMPC algorithm can converge to the optimal policy by directly minimizing the designed loss function. We further prove the convergence and optimality of the RMPC algorithm thorough Bellman optimality principle, and demonstrate its generality and efficiency using two numerical examples.