Event-Triggered Adaptive Optimal Control With Output Feedback: An Adaptive Dynamic Programming Approach

被引：45

作者：

Zhao, Fuyu ^{[1
]}

Gao, Weinan ^{[2
]}

Jiang, Zhong-Ping ^{[3
]}

Liu, Tengfei ^{[1
]}

机构：

[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China

[2] Florida Inst Technol, Coll Engn & Sci, Dept Mech & Civil Engn, Melbourne, FL 32901 USA

[3] NYU, Dept Elect & Comp Engn, Brooklyn, NY 11201 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2021年 / 32卷 / 11期

基金：

美国国家科学基金会; 中国国家自然科学基金;

关键词：

Optimal control; Adaptive systems; Dynamic programming; Linear systems; Mathematical model; Symmetric matrices; Learning systems; Adaptive dynamic programming (ADP); event-triggered control; output feedback; ZERO-SUM GAMES; NONLINEAR-SYSTEMS; GAIN; DESIGN; STATE;

D O I：

10.1109/TNNLS.2020.3027301

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article presents an event-triggered output-feedback adaptive optimal control method for continuous-time linear systems. First, it is shown that the unmeasurable states can be reconstructed by using the measured input and output data. An event-based feedback strategy is then proposed to reduce the number of controller updates and save communication resources. The discrete-time algebraic Riccati equation is iteratively solved through event-triggered adaptive dynamic programming based on both policy iteration (PI) and value iteration (VI) methods. The convergence of the proposed algorithm and the closed-loop stability is carried out by using the Lyapunov techniques. Two numerical examples are employed to verify the effectiveness of the design methodology.

引用

页码：5208 / 5221

页数：14

共 60 条

[1] Data-based optimal control [J].

Aangenent, W ;

Kostic, D ;

de Jager, B ;

van de Molengraft, R ;

Steinbuch, M .

ACC: PROCEEDINGS OF THE 2005 AMERICAN CONTROL CONFERENCE, VOLS 1-7, 2005, :1460-1465

[2]

Agarwal R. P., 2000, Difference equations and inequalities: Theory, methods and applications, V2nd, DOI DOI 10.1201/9781420027020

[3] Model-free Q-learning designs for linear discrete-time zero-sum games with application to H-infinity control [J].

Al-Tamimi, Asma ;

Lewis, Frank L. ;

Abu-Khalaf, Murad .

AUTOMATICA, 2007, 43 (03) :473-481

[4]

Arzen K.-E., 1999, Proceedings of the 14th World Congress. International Federation of Automatic Control, P423

[5]

Astrom K.J., 2008, Analysis and design of nonlinear control systems: In honor of Alberto Isidori, P127, DOI DOI 10.1007/978-3-540-74358-3_9

[6]

Åström KJ, 2002, IEEE DECIS CONTR P, P2011, DOI 10.1109/CDC.2002.1184824

[7] CONTINUOUS-TIME ROBUST DYNAMIC PROGRAMMING [J].

Bian, Tao ;

Jiang, Zhong-Ping .

SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 2019, 57 (06) :4150-4174

[8] Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design [J].

Bian, Tao ;

Jiang, Zhong-Ping .

AUTOMATICA, 2016, 71 :348-360

[9] Adaptive dynamic programming and optimal control of nonlinear nonaffine systems [J].

Bian, Tao ;

Jiang, Yu ;

Jiang, Zhong-Ping .

AUTOMATICA, 2014, 50 (10) :2624-2632

[10]

Chen T., 1995, Optimal Sampled-Data Control Systems, DOI 10.1007/978-1-4471-3037-6

← 1 2 3 4 5 6 →