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Functional Nonlinear Model Predictive Control Based on Adaptive Dynamic Programming
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2019-12-01 , DOI: 10.1109/tcyb.2018.2859801
Lu Dong , Jun Yan , Xin Yuan , Haibo He , Changyin Sun

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.

中文翻译:

基于自适应动态规划的功能非线性模型预测控制

本文提出了一种基于自适应动态规划(ADP)算法的功能模型预测控制(MPC)方法,该方法具有处理非线性离散时间系统最优控制的控制约束和干扰的能力。在提出的基于ADP的非线性MPC(NMPC)结构中,首先建立了基于神经网络的识别,以重建未知的系统动力学。然后,采用批评者网络的行为者批评方案来估计指标绩效函数,并使用行动网络来近似最佳控制输入。同时,由于MPC策略可以通过解决有限水平开环最优控制问题而有效地确定电流控制,因此在该算法中,将无限水平分解为一系列有限水平以获得最优控制。在每个有限范围内,有限ADP算法都可解决受终端约束,控制约束和干扰影响的最优控制问题。Lyapunov方法验证了闭环系统的统一极限有界性。最后,在两种不同情况下进行了基于ADP的NMPC,仿真结果证明了该方法的快速响应性和强大的鲁棒性。
更新日期:2019-12-01
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