Adaptive event-triggered control for MIMO nonlinear systems with asymmetric state constraints based on unified barrier functions
Sanxia Wang
School of Mathematics Science, Liaocheng University, Liaocheng, China
Search for more papers by this authorCorresponding Author
Jianwei Xia
School of Mathematics Science, Liaocheng University, Liaocheng, China
Correspondence Jianwei Xia, School of Mathematics Science, Liaocheng University, Liaocheng 252000, China.
Ju H. Park, Department of Electrical Engineering, Yeungnam University, Kyongsan, 38541, Republic of Korea.
Search for more papers by this authorCorresponding Author
Ju H. Park
Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea
Correspondence Jianwei Xia, School of Mathematics Science, Liaocheng University, Liaocheng 252000, China.
Ju H. Park, Department of Electrical Engineering, Yeungnam University, Kyongsan, 38541, Republic of Korea.
Search for more papers by this authorHao Shen
School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, China
Search for more papers by this authorGuoliang Chen
School of Mathematics Science, Liaocheng University, Liaocheng, China
Search for more papers by this authorSanxia Wang
School of Mathematics Science, Liaocheng University, Liaocheng, China
Search for more papers by this authorCorresponding Author
Jianwei Xia
School of Mathematics Science, Liaocheng University, Liaocheng, China
Correspondence Jianwei Xia, School of Mathematics Science, Liaocheng University, Liaocheng 252000, China.
Ju H. Park, Department of Electrical Engineering, Yeungnam University, Kyongsan, 38541, Republic of Korea.
Search for more papers by this authorCorresponding Author
Ju H. Park
Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea
Correspondence Jianwei Xia, School of Mathematics Science, Liaocheng University, Liaocheng 252000, China.
Ju H. Park, Department of Electrical Engineering, Yeungnam University, Kyongsan, 38541, Republic of Korea.
Search for more papers by this authorHao Shen
School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, China
Search for more papers by this authorGuoliang Chen
School of Mathematics Science, Liaocheng University, Liaocheng, China
Search for more papers by this authorAbstract
The topic of adaptive event-triggered control for multi-input-multi-output nonlinear systems with asymmetric state constraints is considered in this article. First, by introducing unified barrier functions method, the initial system is transformed to a non-constraints system, which brings that the requirement of feasibility conditions could be eliminated and the constraint functions could be relaxed effectively. Then, an adaptive tracking controller is designed by combining some excellent technology, where command filter is utilized to overcome the explosion of complexity, neural network is introduced to approximate unknown nonlinear function, and event-triggered mechanism is proposed to save communication resources. The designed control scheme can make the outputs of the system to track the target trajectories within a small bounded error range, all signals in the closed-loop system are bounded, and all states do not escape the state constraints. Finally, practical and comparative examples are given to verify the effectiveness of the method.
CONFLICT OF INTEREST
The authors confirm that there is no conflict of interest for this article.
Open Research
DATA AVAILABILITY STATEMENT
All data generated or analysed during this study are included in this published article.
REFERENCES
- 1Ma J, Xu S, Zhuang G, Wei Y, Zhang Z. Adaptive neural network tracking control for uncertain nonlinear systems with input delay and saturation. Int J Robust Nonlinear Control. 2020; 30: 2593-2610.
- 2Tong S, Li Y. Adaptive fuzzy output feedback control of MIMO nonlinear systems with unknown dead-zone inputs. IEEE Trans Fuzzy Syst. 2013; 21(1): 134-146.
- 3Tong S, Li Y, Feng G, Li T. Observer-based adaptive fuzzy backstepping dynamic surface control for a class of MIMO nonlinear systems. IEEE Trans Syst Man Cybern Syst. 2011; 41: 1124-1135.
- 4Chen B, Liu X, Liu K, Lin C. Adaptive fuzzy tracking control of nonlinear MIMO systems with time-varying delays. Fuzzy Sets Syst. 2013; 217: 1-21.
- 5Wang H, Chen B, Liu K, Liu X, Lin C. Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis. IEEE Trans Neural Netw Learn Syst. 2014; 25: 947-958.
- 6Bai W, Zhang B, Zhou Q, Lu R. Multigradient recursive reinforcement learning NN control for affine nonlinear systems with unmodeled dynamics. Int J Robust Nonlinear Control. 2020; 30: 1643-1663.
- 7Li Z, Li T, Feng G. Adaptive neural control for a class of stochastic nonlinear time-delay systems with unknown dead zone using dynamic surface technique. Int J Robust Nonlinear Control. 2016; 26: 759-781.
- 8
Lin G, Li H, Ma H, Yao D, Lu R. Human-in-the-loop consensus control for nonlinear multi-agent systems with actuator faults. IEEE/CAA J Automat Sin. 2020. https://doi.org/10.1109/JAS.2020.1003596
10.1109/JAS.2020.1003596 Google Scholar
- 9Li H, Wu Y, Chen M. Adaptive fault-tolerant tracking control for discrete-time multi-agent systems via reinforcement learning algorithm. IEEE Trans Cybern. 2021; 51(3): 1163-1174.
- 10Xia J, Feng J, Wang Z, Zhuang G. Command filter-based adaptive fuzzy control for nonlinear systems with unknown control directions. IEEE Trans Syst Man Cybern Syst. 2021; 51(3): 1945-1953.
- 11Sun W, Su S, Xia J, Wu Y. Adaptive tracking control of wheeled inverted pendulums with periodic disturbances. IEEE Trans Cybern. 2020; 50: 1867-1876.
- 12Chen B, Zhang HG, Liu X, Lin C. Neural observer and adaptive neural control design for a class of nonlinear systems. IEEE Trans Neural Netw Learn Syst. 2018; 29(9): 4261-4271.
- 13 Bai W, Li T, Tong S. NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems. IEEE Trans Cybern. 2020; 50(11): 4573-4584. https://doi.org/10.1109/TCYB.2020.2963849
- 14K. Tee, S. Ge, “Control of nonlinear systems with full state constraint using a Barrier-Lyapunov function”, Proceedings of the 48th IEEE Conference on Decision and Control, vol. 28, pp. 8618-8623, 2009.
- 15He W, Chen Y, Yin Z. Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans Cybern. 2016; 46: 620-629.
- 16Liu Y, Li J, Tong S, Chen C. Neural network control based adaptive learning design for nonlinear systems with full-state constraints. IEEE Trans Neural Netw Learn Syst. 2016; 27: 1562-1571.
- 17Liu Y, Lu S, Tong S, Chen X, Chen C, Li D. Adaptive control-based Barrier Lyapunov functions for a class of stochastic nonlinear systems with full state constraints. Automatica. 2018; 87: 83-93.
- 18Liu Y, Tong S. Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems. Automatica. 2017; 76: 143-152.
- 19Liu Y, Gong M, Liu L, et al. Fuzzy observer constraint based on adaptive control for uncertain nonlinear MIMO systems with time-varying state constraints. IEEE Trans Cybern. 2021; 51: 1380-1389.
- 20Jin X. Adaptive fault tolerant control for a class of input and state constrained MIMO nonlinear systems. Int J Robust Nonlinear Control. 2016; 26: 286-302.
- 21Sun W, Su S, Wu Y, Xia J, Nguyen V. Adaptive fuzzy control with high-order barrier Lyapunov functions for high-order uncertain nonlinear systems with full-state constraints. IEEE Trans Cybern. 2020; 50(8): 3424-3432.
- 22Xia J, Zhang J, Sun W, Zhang B, Wang Z. Finite-time adaptive fuzzy control for nonlinear systems with full state constraints. IEEE Trans Syst Man Cybern Syst. 2019; 49(7): 1541-1548.
- 23Tang Z, Ge S, Tee K, He W. Robust adaptive neural tracking control for a class of perturbed uncertain nonlinear systems with state constraints. IEEE Trans Syst Man Cybern Syst. 2016; 46: 1618-1629.
- 24Tee K, Ge S. Control of state-constrained nonlinear systems using integral Barrier-Lyapunov functionals. Proceedings of the 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). IEEE; 2012: 3239-3244.
10.1109/CDC.2012.6426196 Google Scholar
- 25Zhang T, Xia M, Yi Y. Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics. Automatica. 2017; 81: 232-239.
- 26Qiu J, Sun K, Rudas I, Gao H. Command filter-based adaptive NN control for MIMO nonlinear systems with full-state constraints and actuator hysteresis. IEEE Trans Cybern. 2020; 50: 2905-2915.
- 27Yang B, Xiao W, Yin H, et al. Adaptive neural control for multiagent systems with asymmetric time-varying state constraints and input saturation. Int J Robust Nonlinear Control. 2020; 30(12): 4764-4778.
- 28 Wang C, Wu Y, Wang F, et al. TABLF-based adaptive control for uncertain nonlinear systems with time-varying asymmetric full state constraints. Int J Robust Nonlinear Control. 2021; 94(5): 1238-1246. https://doi.org/10.1080/00207179.2019.1639825
- 29 Liu Y, Zhang H, Wang Y, et al. Adaptive fuzzy control for nonstrict-feedback systems under asymmetric time-varying full state constraints without feasibility condition. IEEE Trans Fuzzy Syst. 2020; 1.
- 30Zhao K, Song Y, Chen C, Chen L. Control of nonlinear systems under dynamic constraints: a unified barrier function-based approach. Automatica. 2020; 119: 109102.
- 31Su X, Wen Y, Shi P, Lam H. Event-triggered fuzzy filtering for nonlinear dynamic systems via reduced-order approach. IEEE Trans Fuzzy Syst. 2019; 27(6): 1215-1225.
- 32Ma H, Li H, Liang H, Dong G. Adaptive fuzzy event-triggered control for stochastic nonlinear systems with full state constraints and actuator faults. IEEE Trans Fuzzy Syst. 2019; 27(11): 2242-2254.
- 33Zhang Y, Su X, Liu Z, Chen C. Event-triggered adaptive fuzzy tracking control with guaranteed transient performance for MIMO nonlinear uncertain systems. IEEE Trans Cybern. 2021; 51(2): 736-749. https://doi.org/10.1109/TCYB.2019.2894343
- 34 Cao L, Ren H, Meng W, Li H, Lu R. Distributed event triggering control for six-rotor UAV systems with asymmetric time-varying output constraints. Sci China Inf Sci. 2021; 64: 172213:1-172213:6. https://doi.org/10.1007/s11432-020-3128-2
- 35 Xia J, Li B, Su S, Sun W, Shen H. Finite-time command filtered event-triggered adaptive fuzzy tracking control for stochastic nonlinear systems. IEEE Trans Fuzzy Syst. 2020. https://doi.org/10.1109/TFUZZ.2020.2985638
- 36He M, Li J. Event-triggered adaptive tracking control for a class of uncertain stochastic nonlinear systems with Markov jumping parameters. Int J Adapt Control Signal Process. 2018; 32(12): 1655-1674.
- 37Cao L, Li H, Zhou Q. Adaptive intelligent control for nonlinear strict-feedback systems with virtual control coefficients and uncertain disturbances based on event-triggered mechanism. IEEE Trans Cybern. 2018; 48(12): 3390-3402.
- 38Choi Y, Yoo S. Event-triggered output-feedback tracking of a class of nonlinear systems with unknown time delays. Nonlinear Dyn. 2019; 96(2): 959-973.
- 39Wang C, Guo L, Qiao J. Event-triggered adaptive fault-tolerant control for nonlinear systems fusing static and dynamic information. J Franklin Inst. 2019; 356(1): 248-267.
- 40Li Y, Yang G. Model-based adaptive event-triggered control of strict-feedback nonlinear systems. IEEE Trans Neural Netw Learn Syst. 2018; 29(4): 1033-1045.
- 41Xing L, Wen C, Liu Z, Su H, Cai J. Event-triggered adaptive control for a class of uncertain nonlinear systems. IEEE Trans Automat Contr. 2017; 62(4): 2071-2076.
- 42Lu K, Liu Z, Lai G, Chen C, Zhang Y. Adaptive fuzzy output feedback control for nonlinear systems based on event-triggered mechanism. Inf Sci. 2019; 486: 419-433.
- 43Xing L, Wen C, Liu Z, Su H, Cai J. Event-triggered output feedback control for a class of uncertain nonlinear systems. IEEE Trans Automat Contr. 2019; 61(1): 290-297.
- 44Farrell J, Polycarpou W, Sharma M, Dong W. Command filtered backstepping. IEEE Trans Automat Contr. 2009; 54: 1391-1395.
