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CLC number: TP273

On-line Access: 2021-07-20

Received: 2020-04-03

Revision Accepted: 2020-10-15

Crosschecked: 2021-06-08

Cited: 0

Clicked: 5902

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xuerao Wang

https://orcid.org/0000-0002-5693-7527

Changyin Sun

https://orcid.org/0000-0001-9269-334X

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Frontiers of Information Technology & Electronic Engineering 

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Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance


Author(s):  Xuerao Wang, Qingling Wang, Changyin Sun

Affiliation(s):  School of Automation, Southeast University, Nanjing 210096, China; more

Corresponding email(s):  wangxuerao@seu.edu.cn, qlwang@seu.edu.cn, cysun@seu.edu.cn

Key Words:  Adaptive tracking control, Prescribed performance, Input saturation, Disturbance observer, Neural network


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Xuerao Wang, Qingling Wang, Changyin Sun. Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000145

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Abstract: 
In this paper, an observer-based adaptive prescribed performance tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without input saturation. A novel finite-time neural network disturbance observer is constructed to estimate the system uncertainties and external disturbances. To guarantee the prescribed performance, an error transformation is applied to transfer the time-varying constraints into a constant constraint. Then, by employing a barrier Lyapunov function and the backstepping technique, an observer-based tracking control strategy is presented. It is proven that using the proposed algorithm, all the closed-loop signals are bounded, and the tracking errors satisfy the predefined time-varying performance requirements. Finally, simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

带有预设性能的高阶多输入多输出非线性系统自适应跟踪控制

王雪娆1,2,王庆领1,2,孙长银1,2
1东南大学自动化学院,中国南京市,210096
2东南大学复杂工程系统测量与控制教育部重点实验室,中国南京市,210096
摘要:本文针对一类不确定多输入多输出非线性系统提出一种基于观测器的自适应预设性能跟踪控制策略,同时考虑了系统中可能存在的不确定性。为估计被控系统中的不确定性以及外部扰动,本文构建了一类新颖的有限时间神经网络干扰观测器。此外,为保证系统可以达到预设性能,采用一类误差转换方法,可以将时变约束转换为一种等价的非时变约束。随后,基于障碍李雅普诺夫函数以及反步方法,提出一种基于观测器的跟踪控制策略。经证明,本文所设计的控制方法可以使闭环系统所有信号实现有界,跟踪误差满足预设的时变性能指标。最后,无人机系统数值仿真结果验证了所提控制策略的有效性。

关键词组:自适应跟踪控制;预设性能;输入饱和;干扰观测器;神经网络

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bechlioulis CP, Rovithakis GA, 2008. Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance. IEEE Trans Autom Contr, 53(9):2090-2099.

[2]Bechlioulis CP, Rovithakis GA, 2009. Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems. Automatica, 45(2):532-538.

[3]Bechlioulis CP, Rovithakis GA, 2010. Prescribed performance adaptive control for multi-input multi-output affine in the control nonlinear systems. IEEE Trans Autom Contr, 55(5):1220-1226.

[4]Bu XW, 2018. Guaranteeing prescribed output tracking performance for air-breathing hypersonic vehicles via non-affine back-stepping control design. Nonl Dynam, 91(1):525-538.

[5]Chen M, Ge SS, Ren BB, 2011. Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints. Automatica, 47(3):452-465.

[6]Chen M, Mei R, Jiang B, 2013. Sliding mode control for a class of uncertain MIMO nonlinear systems with application to near-space vehicles. Math Probl Eng, 2013:180589.

[7]Chen WH, 2004. Disturbance observer based control for nonlinear systems. IEEE/ASME Trans Mech, 9(4):706-710.

[8]Chen WH, Yang J, Guo L, et al., 2015. Disturbance-observer-based control and related methods—an overview. IEEE Trans Ind Electron, 63(2):1083-1095.

[9]Chen XS, Yang J, Li SH, et al., 2009. Disturbance observer based multi-variable control of ball mill grinding circuits. J Process Contr, 19(7):1205-1213.

[10]Fan B, Yang QM, Jagannathan S, et al., 2017. Asymptotic tracking controller design for nonlinear systems with guaranteed performance. IEEE Trans Cybern, 48(7):2001-2011.

[11]Fu J, Ma RC, Chai TY, 2017. Adaptive finite-time stabilization of a class of uncertain nonlinear systems via logic-based switchings. IEEE Trans Autom Contr, 62(11):5998-6003.

[12]Guo L, Chen WH, 2005. Disturbance attenuation and rejection for systems with nonlinearity via DOBC approach. Int J Robust Nonl Contr, 15(3):109-125.

[13]Guo XX, Yan WS, Cui RX, 2019. Integral reinforcement learning-based adaptive NN control for continuous-time nonlinear MIMO systems with unknown control directions. IEEE Trans Syst Man Cybern Syst, 50(11):4068-4077.

[14]Han SI, Lee JM, 2013. Improved prescribed performance constraint control for a strict feedback non-linear dynamic system. IET Contr Theory Appl, 7(14):1818-1827.

[15]He W, Dong YT, Sun CY, 2015a. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Trans Syst Man Cybern Syst, 46(3):334-344.

[16]He W, David AO, Yin Z, et al., 2015b. Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Trans Syst Man Cybern Syst, 46(6):759-770.

[17]Hu QL, Li B, Qi JT, 2014. Disturbance observer based finite-time attitude control for rigid spacecraft under input saturation. Aerosp Sci Technol, 39:13-21.

[18]Hu QL, Shao XD, Guo L, 2017. Adaptive fault-tolerant attitude tracking control of spacecraft with prescribed performance. IEEE/ASME Trans Mech, 23(1):331-341.

[19]Jin X, 2018. Adaptive decentralized finite-time output tracking control for MIMO interconnected nonlinear systems with output constraints and actuator faults. Int J Robust Nonl Contr, 28(5):1808-1829.

[20]Lin LG, Xin M, 2020. Nonlinear control of two-wheeled robot based on novel analysis and design of SDRE scheme. IEEE Trans Contr Syst Technol, 28(3):1140-1148.

[21]Lin XB, Yu Y, Sun CY, 2019a. A decoupling control for quadrotor UAV using dynamic surface control and sliding mode disturbance observer. Nonl Dynam, 97(1):781-795.

[22]Lin XB, Yu Y, Sun CY, 2019b. Supplementary reinforcement learning controller designed for quadrotor UAVs. IEEE Access, 7:26422-26431.

[23]Liu H, Xi JX, Zhong YS, 2017. Robust attitude stabilization for nonlinear quadrotor systems with uncertainties and delays. IEEE Trans Ind Electron, 64(7):5585-5594.

[24]Liu J, Zhang YL, Yu Y, et al., 2019. Fixed-time event-triggered consensus for nonlinear multiagent systems without continuous communications. IEEE Trans Syst Man Cybern Syst, 49(11):2221-2229.

[25]Liu J, Zhang YL, Yu Y, et al., 2020. Fixed-time leader–follower consensus of networked nonlinear systems via event/self-triggered control. IEEE Trans Neur Netw Learn Syst, 31(11):5029-5037.

[26]Liu J, Yu Y, He HB, et al., 2021. Team-triggered practical fixed-time consensus of double-integrator agents with uncertain disturbance. IEEE Trans Cybern, 51(6):3263-3272.

[27]Ouyang YC, Dong L, Xue L, et al., 2019. Adaptive control based on neural networks for an uncertain 2-DOF helicopter system with input deadzone and output constraints. IEEE/CAA J Autom Sin, 6(3):807-815.

[28]Ouyang YC, Dong L, Sun CY, 2020. Critic learning-based control for robotic manipulators with prescribed constraints. IEEE Trans Cybern, online.

[29]Peng JZ, Ding S, Yang ZQ, et al., 2020. Adaptive neural impedance control for electrically driven robotic systems based on a neuro-adaptive observer. Nonl Dynam, 100(2):1359-1378.

[30]Ren BB, Zhong QC, Chen JH, 2015. Robust control for a class of nonaffine nonlinear systems based on the uncertainty and disturbance estimator. IEEE Trans Ind Electron, 62(9):5881-5888.

[31]Song YD, Huang XC, Wen CY, 2017. Robust adaptive fault-tolerant PID control of MIMO nonlinear systems with unknown control direction. IEEE Trans Ind Electron, 64(6):4876-4884.

[32]Sui S, Tong SC, Li YM, 2015. Observer-based fuzzy adaptive prescribed performance tracking control for nonlinear stochastic systems with input saturation. Neurocomputing, 158:100-108.

[33]Sun HB, Guo L, 2016. Neural network-based DOBC for a class of nonlinear systems with unmatched disturbances. IEEE Trans Neur Netw Learn Syst, 28(2):482-489.

[34]Tee KP, Ge SS, Tay EH, 2009. Barrier Lyapunov functions for the control of output-constrained nonlinear systems. Automatica, 45(4):918-927.

[35]Tong SC, Li YM, Shi P, 2012. Observer-based adaptive fuzzy backstepping output feedback control of uncertain MIMO pure-feedback nonlinear systems. IEEE Trans Fuzzy Syst, 20(4):771-785.

[36]Wang CC, Yang GH, 2018. Observer-based adaptive prescribed performance tracking control for nonlinear systems with unknown control direction and input saturation. Neurocomputing, 284:17-26.

[37]Wang DD, Zong Q, Tian BL, et al., 2018. Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters. ISA Trans, 73:208-226.

[38]Wang LY, Chai TY, Zhai LF, 2009. Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics. IEEE Trans Ind Electron, 56(9):3296-3304.

[39]Wang QL, Sun CY, 2020. Adaptive consensus of multiagent systems with unknown high-frequency gain signs under directed graphs. IEEE Trans Syst Man Cybern Syst, 50(6):2181-2186.

[40]Wang XJ, Yin XH, Wu QH, et al., 2018. Disturbance observer based adaptive neural control of uncertain MIMO nonlinear systems with unmodeled dynamics. Neurocomputing, 313:247-258.

[41]Wang XR, Sun CY, Lin XB, et al., 2018. Adaptive neural network control of a quadrotor with input delay. Proc Chinese Automation Congress, p.4095-4100.

[42]Zhao Y, Yu SH, Lian J, 2020a. Anti-disturbance bumpless transfer control for switched systems with its application to switched circuit model. IEEE Trans Circ Syst II Expr Briefs, 67(12):3177-3181.

[43]Zhao Y, Zhao J, Fu J, et al., 2020b. Rate bumpless transfer control for switched linear systems with stability and its application to aero-engine control design. IEEE Trans Ind Electron, 67(6):4900-4910.

[44]Zheng ZW, Feroskhan M, 2017. Path following of a surface vessel with prescribed performance in the presence of input saturation and external disturbances. IEEE/ASME Trans Mech, 22(6):2564-2575.

[45]Zhou Q, Shi P, Tian Y, et al., 2014. Approximation-based adaptive tracking control for MIMO nonlinear systems with input saturation. IEEE Trans Cybern, 45(10):2119-2128.

[46]Zhu Z, Xia YQ, Fu MY, 2011. Attitude stabilization of rigid spacecraft with finite-time convergence. Int J Robust Nonl Contr, 21(6):686-702.

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