Abstract
In this paper, an event-triggered neural intelligent control for uncertain nonlinear systems with specified-time guaranteed behaviors is proposed. To cope with constrained communication resources, an event-triggered mechanism using switched thresholds is devised without involving input-to-state stability assumption, such that a better design flexibility and freedom can be provided. In addition, a minimum-learning-parameter-based state observer is developed to online estimate the unavailable states and uncertainties at the same time, which effectively eliminates the issue of learning explosion without sacrificing the identification precision. Furthermore, in pursuit of making a compromise between sampling cost and tracking performance, a modified barrier Lyapunov function based on a time-varying finite-time behavior boundary is constructed in the controller design, which can guarantee that the tracking error converges to a predetermined region within a specified time. Then by introducing the Nussbaum gain technique to handle the unknown control direction, an event-triggered neural output feedback control strategy is synthesized within the framework of dynamic surface control. Meanwhile, with the aid of Lyapunov synthesis, all the signals involved in the closed-loop system are proved to be bounded while Zeno phenomena is circumvented, and system outputs are well within the predefined region. Finally, an application on control design for a micro-electro-mechanical system gyroscope is given to validate the efficiency and superiority of proposed intelligent control scheme.
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References
Yen VT, Nan WY, Cuong PV (2019) Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators. Neural Comput Appl 31(11):6945–6958
Ming Pi Y, Kang CX, Li G, Li Z (2019) Adaptive time-delay balance control of biped robots. IEEE Trans Ind Electron 67(4):2936–2944
Lee J, Chang PH, Jin M (2019) An adaptive gain dynamics for time delay control improves accuracy and robustness to significant payload changes for robots. IEEE Trans Ind Electron 67(4):3076–3085
Wang X, Guo J, Tang S, Qi S (2019) Fixed-time disturbance observer based fixed-time back-stepping control for an air-breathing hypersonic vehicle. ISA Trans 88:233–245
Shen H, Liu Y, Chen B, Yuping L (2018) Control-relevant modeling and performance limitation analysis for flexible air-breathing hypersonic vehicles. Aerosp Sci Technol 76:340–349
Shi Y, Shao X, Zhang W (2020) Quantized learning control for flexible air-breathing hypersonic vehicle with limited actuator bandwidth and prescribed performance. Aerosp Sci Technol 97:105629
Shao X, Shi Y (2020) Neural adaptive control for MEMS gyroscope with full-state constraints and quantized input. IEEE Trans Ind Inf 16(10):6444–6454
Fei J, Batur C (2009) A novel adaptive sliding mode control with application to MEMS gyroscope. ISA Trans 48(1):73–78
Sun L (2019) Adaptive fault-tolerant constrained control of cooperative spacecraft rendezvous and docking. IEEE Trans Ind Electron 67(4):3107–3115
Wang C, Guo L, Wen C, Qinglei H, Qiao J (2019) Event-triggered adaptive attitude tracking control for spacecraft with unknown actuator faults. IEEE Trans Ind Electron 67(3):2241–2250
Rigatos G, Zhu G, Yousef H, Boulkroune A (2016) Flatness-based adaptive fuzzy control of electrostatically actuated MEMS using output feedback. Fuzzy Sets Syst 290:138–157
Zheng Q, Dong L, Lee DH, Gao Z (2008) Active disturbance rejection control for MEMS gyroscopes. In: 2008 American control conference. IEEE, pp 4425–4430
Zheng M, Li L, Peng H, Xiao J, Yang Y, Zhao H (2018) Parameters estimation and synchronization of uncertain coupling recurrent dynamical neural networks with time-varying delays based on adaptive control. Neural Comput Appl 30(7):2217–2227
Shao X, Wang L, Li J, Liu J (2019) High-order ESO based output feedback dynamic surface control for quadrotors under position constraints and uncertainties. Aerosp Sci Technol 89:288–298
Chang E-C, Wu R-C, Ke Z, Chen G-Y (2018) Adaptive neuro-fuzzy inference system-based grey time-varying sliding mode control for power conditioning applications. Neural Comput Appl 30(3):699–707
Sadek U, Sarjas A, Chowdhury A (2017) Improved adaptive fuzzy backstepping control of a magnetic levitation system based on symbiotic organism search. Appl Soft Comput 56:19–33
Yin Q, Wang M, Jing H (2020) Stabilizing backstepping controller design for arbitrarily switched complex nonlinear system. Appl Math Comput 369:124789
Ma Z, Ma HJ (2019) Adaptive fuzzy backstepping dynamic surface control of strict-feedback fractional order uncertain nonlinear systems. IEEE Trans Fuzzy Syst 28(1):122–133
Chen Z, Huang F, Yang C, Yao B (2019) Adaptive fuzzy backstepping control for stable nonlinear bilateral teleoperation manipulators with enhanced transparency performance. IEEE Trans Ind Electron 67(1):746–756
Munoz-Vazquez AJ, Gaxiola F, Martinez-Reyes F (2019) A fuzzy fractional-order control of robotic manipulators with PID error manifolds. Appl Soft Comput 83:105646
Huang L, Li Y, Tong S (2017) Fuzzy adaptive output feedback control for MIMO switched nontriangular structure nonlinear systems with unknown control directions. IEEE Trans Syst Man Cybern Syst 50(2):550–564
Hang S, Zhang W (2018) Adaptive fuzzy control of stochastic nonlinear systems with fuzzy dead zones and unmodeled dynamics. IEEE Trans Cybern 50(2):587–599
Li T, Duan S, Liu J, Wang L (2018) An improved design of rbf neural network control algorithm based on spintronic memristor crossbar array. Neural Comput Appl 30(6):1939–1946
Xinghu Y, Wang T, Gao H (2020) Adaptive neural fault-tolerant control for a class of strict-feedback nonlinear systems with actuator and sensor faults. Neurocomputing 380:87–94
Lau JY, Liang W, Tan KK (2019) Motion control for piezoelectric-actuator-based surgical device using neural network and extended state observer. IEEE Trans Ind Electron 67(1):402–412
Xie S, Ren J (2019) Recurrent-neural-network-based predictive control of piezo actuators for trajectory tracking. IEEE/ASME Trans Mechatron 24(6):2885–2896
Shao X, Liu N, Wang Z, Zhang W, Yang W (2020) Neuroadaptive integral robust control of visual quadrotor for tracking a moving object. Mech Syst Signal Process 136:106513
Moawad NM, Elawady WM, Sarhan AM (2019) Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems. ISA Trans 87:200–216
Namadchian Z, Rouhani M (2018) Adaptive neural tracking control of switched stochastic pure-feedback nonlinear systems with unknown Bouc–Wen hysteresis input. IEEE Trans Neural Netw Learn Syst 29(12):5859–5869
Mohammadzadeh A, Zhang W (2019) Dynamic programming strategy based on a type-2 fuzzy wavelet neural network. Nonlinear Dyn 95(2):1661–1672
Mendel JM (2020) The interval weighted average and its importance to type-2 fuzzy sets and systems. In: Beyond traditional probabilistic data processing techniques: interval, fuzzy etc. Methods and their applications. Springer, pp 195–211
Tee KP, Ren B, Ge SS (2011) Control of nonlinear systems with time-varying output constraints. Automatica 47(11):2511–2516
Peng J, Dubay R (2019) Adaptive fuzzy backstepping control for a class of uncertain nonlinear strict-feedback systems based on dynamic surface control approach. Expert Syst Appl 120:239–252
Bechlioulis CP, Rovithakis GA (2008) Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance. IEEE Trans Autom Control 53(9):2090–2099
Bechlioulis CP, Rovithakis GA (2014) A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems. Automatica 50(4):1217–1226
Xia X, Zhang T (2018) Robust adaptive quantized DSC of uncertain pure-feedback nonlinear systems with time-varying output and state constraints. Int J Robust Nonlinear Control 28(10):3357–3375
Wang Y, Jianbo H, Li J, Liu B (2019) Improved prescribed performance control for nonaffine pure-feedback systems with input saturation. Int J Robust Nonlinear Control 29(6):1769–1788
Shi D, Xue J, Wang J, Huang Y (2018) A high-gain approach to event-triggered control with applications to motor systems. IEEE Trans Ind Electron 66(8):6281–6291
Girard A (2014) Dynamic triggering mechanisms for event-triggered control. IEEE Trans Autom Control 60(7):1992–1997
Xing L, Wen C, Guo F, Liu Z, Hongye S (2016) Event-based consensus for linear multiagent systems without continuous communication. IEEE Trans Cybern 47(8):2132–2142
Tallapragada P, Chopra N (2014) Decentralized event-triggering for control of nonlinear systems. IEEE Trans Autom Control 59(12):3312–3324
Xing L, Wen C, Liu Z, Hongye S, Cai J (2018) Event-triggered output feedback control for a class of uncertain nonlinear systems. IEEE Trans Autom Control 64(1):290–297
Amrr SM, Nabi MU, Iqbal A (2019) An event-triggered robust attitude control of flexible spacecraft with modified rodrigues parameters under limited communication. IEEE Access 7:93198–93211
Zhang J, Johansson KH, Lygeros J, Sastry S (2001) Zeno hybrid systems. Int J Robust Nonlinear Control IFAC Affil J 11(5):435–451
Shao X, Tian B, Yang W, Zhang W (2019) Estimator-based MLP neuroadaptive dynamic surface containment control with prescribed performance for multiple quadrotors. Aerosp Sci Technol 97:105620
Shao X, Liu J, Cao H, Shen C, Wang H (2018) Robust dynamic surface trajectory tracking control for a quadrotor UAV via extended state observer. Int J Robust Nonlinear Control 28(7):2700–2719
Xiru W, Wang Y, Dang X (2014) Robust adaptive sliding-mode control of condenser-cleaning mobile manipulator using fuzzy wavelet neural network. Fuzzy Sets Syst 235:62–82
Zhou J, Wen C, Wang W, Yang F (2019) Adaptive backstepping control of nonlinear uncertain systems with quantized states. IEEE Trans Autom Control 64(11):4756–4763
Xi C, Zhai D, Li X, Zhang Q (2017) Decentralized adaptive delay-dependent neural network control for a class of large-scale interconnected nonlinear systems. Appl Math Comput 311:148–163
Tian-Ping Zhang and Shuzhi Sam Ge (2008) Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form. Automatica 44(7):1895–1903
Zhipeng S et al (2020) MLP neural network-based recursive sliding mode dynamic surface control for trajectory tracking of fully actuated surface vessel subject to unknown dynamics and input saturation. Neurocomputing 377:103–112
Shi J, Lyu Y, Cao Y, Chen H, Xiaobo Q (2019) Minimum parameters learning-based dynamic surface control for advanced aircraft at high angle of attack. IEEE Access 7:149724–149735
Pan Y, Liu Y, Bin X, Haoyong Y (2016) Hybrid feedback feedforward: an efficient design of adaptive neural network control. Neural Netw 76:122–134
Peng G, Yang C, He W, Philip Chen CL (2019) Force sensorless admittance control with neural learning for robots with actuator saturation. IEEE Trans Ind Electron 67(4):3138–3148
Keighobadi J, Hosseini-Pishrobat M, Faraji J (2020) Adaptive neural dynamic surface control of mechanical systems using integral terminal sliding mode. Neurocomputing 379:141–151
Kumar R, Srivastava S, Gupta JRP (2018) Online modeling and adaptive control of robotic manipulators using gaussian radial basis function networks. Neural Comput Appl 29(11):1261–1271
Nussbaum RD (1983) Some remarks on a conjecture in parameter adaptive control. Syst Control Lett 3(5):243–246
Chen C, Liu Z, Xie K, Liu Y, Zhang Y, Philip Chen CL (2016) Adaptive fuzzy asymptotic control of MIMO systems with unknown input coefficients via a robust Nussbaum gain-based approach. IEEE Trans Fuzzy Syst 25(5):1252–1263
Yang Y, Feng G, Ren J (2004) A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems. IEEE Trans Syst Man Cybern Syst 34(3):406–420
Acknowledgements
In behalf of the co-authors, I would like to deliver sincere appreciation to the editors and reviewers for their valuable suggestions on improving the quality of this article. This research has been supported in part by National Natural Science Foundation of China under Grant 61803348, State Key Laboratory of Deep Buried Target Damage under Grant DXMBJJ2019-02, Shanxi Province Science Foundation for Youths under Grant 201701D221123, Youth Academic Leader Program of North University of China under Grant QX201803, Program for the Innovative Talents of Higher Education Institutions of Shanxi, and Shanxi 1331 Project Key Subjects Construction (1331KSC).
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The authors Shao Xingling, Si Haonan and Zhang Wendong declare that there is no potential conflict of interest with regard to this work.
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Shao, X., Si, H. & Zhang, W. Event-triggered neural intelligent control for uncertain nonlinear systems with specified-time guaranteed behaviors. Neural Comput & Applic 33, 5771–5791 (2021). https://doi.org/10.1007/s00521-020-05357-w
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DOI: https://doi.org/10.1007/s00521-020-05357-w