Functional nonlinear model predictive control based on adaptive dynamic programming
Date of Original Version
This paper presents a functional model predictive control (MPC) approach based on an adaptive dynamic programming (ADP) algorithm with the abilities of handling control constraints and disturbances for the optimal control of nonlinear discrete-time systems. In the proposed ADP-based nonlinear MPC (NMPC) structure, a neural-network-based identification is established first to reconstruct the unknown system dynamics. Then, the actor-critic scheme is adopted with a critic network to estimate the index performance function and an action network to approximate the optimal control input. Meanwhile, as the MPC strategy can effectively determine the current control by solving a finite horizon open-loop optimal control problem, in the proposed algorithm, the infinite horizon is decomposed into a series of finite horizons to obtain the optimal control. In each finite horizon, the finite ADP algorithm solves the optimal control problem subject to the terminal constraint, the control constraint, and the disturbance. The uniform ultimate boundedness of the closed-loop system is verified by the Lyapunov approach. Finally, the ADP-based NMPC is conducted on two different cases and the simulation results demonstrate the quick response and strong robustness of the proposed method.
Publication Title, e.g., Journal
IEEE Transactions on Cybernetics
Dong, Lu, Jun Yan, Xin Yuan, Haibo He, and Changyin Sun. "Functional nonlinear model predictive control based on adaptive dynamic programming." IEEE Transactions on Cybernetics 49, 12 (2019): 4206-4218. doi: 10.1109/TCYB.2018.2859801.