Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming

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 (N...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 49; no. 12; pp. 4206 - 4218
Main Authors: Dong, Lu, Yan, Jun, Yuan, Xin, He, Haibo, Sun, Changyin
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Adaptive control
Adaptive dynamic programming (ADP)
Algorithms
Artificial neural networks
Computer simulation
Discrete time systems
Dynamic programming
Economic models
Feedback control
Heuristic algorithms
Horizon
Mathematical model
Neural networks
neural networks (NNs)
Nonlinear control
nonlinear model predictive control (NMPC)
Nonlinear systems
Optimal control
Predictive control
System dynamics
Terminal constraints
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2018.2859801