Neural network-based event-triggered data-driven control of disturbed nonlinear systems with quantized input
This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is...
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| Published in | Neural networks Vol. 156; pp. 152 - 159 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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01.12.2022
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| Abstract | This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example. |
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| AbstractList | This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example. This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example.This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example. |
| Author | Wang, Xianming Karimi, Hamid Reza Shi, Jiantao Li, Li-Wei Shen, Mouquan Liu, Dan |
| Author_xml | – sequence: 1 givenname: Xianming surname: Wang fullname: Wang, Xianming organization: School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China – sequence: 2 givenname: Hamid Reza surname: Karimi fullname: Karimi, Hamid Reza email: hamidreza.karimi@polimi.it organization: Department of Mechanical Engineering, Politecnico di Milano, Milan, 20156, Italy – sequence: 3 givenname: Mouquan orcidid: 0000-0001-6448-4866 surname: Shen fullname: Shen, Mouquan email: shenmouquan@njtech.edu.cn organization: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211816, China – sequence: 4 givenname: Dan surname: Liu fullname: Liu, Dan organization: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211816, China – sequence: 5 givenname: Li-Wei surname: Li fullname: Li, Li-Wei organization: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211816, China – sequence: 6 givenname: Jiantao surname: Shi fullname: Shi, Jiantao organization: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211816, China |
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| Cites_doi | 10.1002/rnc.5107 10.1109/TCYB.2020.3015746 10.1016/j.neunet.2019.08.011 10.1109/9.983352 10.1109/TAC.2017.2753460 10.1049/iet-cta.2019.0051 10.1016/j.neucom.2017.07.008 10.1016/j.neunet.2020.01.024 10.1109/TIE.2014.2308161 10.1109/TAC.2019.2959924 10.1109/TCST.2010.2093136 10.1109/TSMC.2018.2866909 10.1109/TIE.2016.2636126 10.1109/TCST.2003.821967 10.23919/ECC51009.2020.9143691 10.1002/rnc.5661 10.1109/TNNLS.2013.2291792 10.1016/j.ins.2020.03.031 10.1109/TNNLS.2021.3053269 10.1016/j.neunet.2019.09.032 10.1109/TNNLS.2019.2919441 |
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| References | Xu, Jiang, Shi (b20) 2014; 61 Pham, H. A., & Soffker, D. (2020). Improved model-free adaptive control method using recursive least-squares estimation algorithm. In Ding, Wang (b8) 2020; 125 (pp. 47–52). Bu, Zhu, Yu, Hou, Liang (b4) 2020; 30 Li, Wang, Zhang (b13) 2019; 13 Hou, Chi, Gao (b11) 2016; 64 Yu, Hou, Bu, Yu (b23) 2019; 31 Deng, Che, Wu (b7) 2020; 51 Hjalmarsson, H., Gunnarsson, S., & Gevers, M. (2002). A convergent iterative restricted complexity control design scheme. In Hou, Jin (b12) 2010; 19 Hou (b10) 1994 Cao, Wang, Guo, Huang, Wen (b5) 2019; 119 Bu, Yu, Yu, Hou, Yang (b3) 2021 Liu, Yang (b14) 2018; 272 (pp. 1734–1740). Zhu, Hou (b25) 2013; 25 Yang, Niu, Karimi (b21) 2022; 32 Rosolia, Borrelli (b17) 2017; 63 Silva, Datta, Bhattacharyya (b18) 2002; 47 Yang, Yu, Sun, Karimi (b22) 2019; 524 Previdi, Schauer, Savaresi, Hunt (b16) 2004; 12 Bu, Hou, Yu, Fu (b1) 2012; 14 Bu, Hou, Yu, Yang (b2) 2018; 50 Zhang, Su, Zhang, Tian (b24) 2021; 32 De Persis, Tesi (b6) 2019; 65 Wang, Wang, Song, Shen, Huang (b19) 2020; 121 Hou (10.1016/j.neunet.2022.09.021_b11) 2016; 64 Wang (10.1016/j.neunet.2022.09.021_b19) 2020; 121 Yang (10.1016/j.neunet.2022.09.021_b21) 2022; 32 Rosolia (10.1016/j.neunet.2022.09.021_b17) 2017; 63 Zhang (10.1016/j.neunet.2022.09.021_b24) 2021; 32 Xu (10.1016/j.neunet.2022.09.021_b20) 2014; 61 Hou (10.1016/j.neunet.2022.09.021_b10) 1994 Yu (10.1016/j.neunet.2022.09.021_b23) 2019; 31 Cao (10.1016/j.neunet.2022.09.021_b5) 2019; 119 Yang (10.1016/j.neunet.2022.09.021_b22) 2019; 524 Bu (10.1016/j.neunet.2022.09.021_b4) 2020; 30 Li (10.1016/j.neunet.2022.09.021_b13) 2019; 13 Deng (10.1016/j.neunet.2022.09.021_b7) 2020; 51 Previdi (10.1016/j.neunet.2022.09.021_b16) 2004; 12 Ding (10.1016/j.neunet.2022.09.021_b8) 2020; 125 Silva (10.1016/j.neunet.2022.09.021_b18) 2002; 47 Zhu (10.1016/j.neunet.2022.09.021_b25) 2013; 25 Bu (10.1016/j.neunet.2022.09.021_b3) 2021 Bu (10.1016/j.neunet.2022.09.021_b2) 2018; 50 Hou (10.1016/j.neunet.2022.09.021_b12) 2010; 19 De Persis (10.1016/j.neunet.2022.09.021_b6) 2019; 65 Bu (10.1016/j.neunet.2022.09.021_b1) 2012; 14 10.1016/j.neunet.2022.09.021_b9 10.1016/j.neunet.2022.09.021_b15 Liu (10.1016/j.neunet.2022.09.021_b14) 2018; 272 |
| References_xml | – volume: 32 start-page: 2518 year: 2022 end-page: 2535 ident: b21 article-title: Dynamic learning control design for interval type-2 fuzzy singularly perturbed systems: A component-based event-triggering protocol publication-title: International Journal of Robust and Nonlinear Control – volume: 524 start-page: 136 year: 2019 end-page: 147 ident: b22 article-title: Event-based networked predictive control for networked control systems subject to two-channel delays publication-title: Information Sciences – reference: Hjalmarsson, H., Gunnarsson, S., & Gevers, M. (2002). A convergent iterative restricted complexity control design scheme. In – volume: 19 start-page: 1549 year: 2010 end-page: 1558 ident: b12 article-title: A novel data-driven control approach for a class of discrete-time nonlinear systems publication-title: IEEE Transactions on Control Systems Technology – volume: 65 start-page: 909 year: 2019 end-page: 924 ident: b6 article-title: Formulas for data-driven control: Stabilization, optimality, and robustness publication-title: IEEE Transactions on Automatic Control – volume: 63 start-page: 1883 year: 2017 end-page: 1896 ident: b17 article-title: Learning model predictive control for iterative tasks. A data-driven control framework publication-title: IEEE Transactions on Automatic Control – year: 2021 ident: b3 article-title: Event-triggered model-free adaptive iterative learning control for a class of nonlinear systems over fading channels publication-title: IEEE Transactions on Cybernetics – volume: 32 start-page: 5502 year: 2021 end-page: 5511 ident: b24 article-title: Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 272 start-page: 356 year: 2018 end-page: 364 ident: b14 article-title: Neural network-based event-triggered MFAC for nonlinear discrete-time processes publication-title: Neurocomputing – volume: 61 start-page: 6391 year: 2014 end-page: 6398 ident: b20 article-title: A novel model-free adaptive control design for multivariable industrial processes publication-title: IEEE Transactions on Industrial Electronics – reference: (pp. 47–52). – volume: 13 start-page: 2197 year: 2019 end-page: 2206 ident: b13 article-title: Data-driven-based event-triggered tracking control for non-linear systems with unknown disturbance publication-title: IET Control Theory & Applications – volume: 31 start-page: 1170 year: 2019 end-page: 1182 ident: b23 article-title: RBFNN-based data-driven predictive iterative learning control for nonaffine nonlinear systems publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 50 start-page: 5119 year: 2018 end-page: 5129 ident: b2 article-title: Quantized data driven iterative learning control for a class of nonlinear systems with sensor saturation publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – reference: Pham, H. A., & Soffker, D. (2020). Improved model-free adaptive control method using recursive least-squares estimation algorithm. In – volume: 51 start-page: 1812 year: 2020 end-page: 1821 ident: b7 article-title: A dynamic periodic event-triggered approach to consensus of heterogeneous linear multiagent systems with time-varying communication delays publication-title: IEEE Transactions on Cybernetics – volume: 14 start-page: 42 year: 2012 end-page: 49 ident: b1 article-title: Model free adaptive control with disturbance observer publication-title: Journal of Control Engineering and Applied Informatics – volume: 125 start-page: 31 year: 2020 end-page: 40 ident: b8 article-title: Event-triggered synchronization of discrete-time neural networks: A switching approach publication-title: Neural Networks – volume: 119 start-page: 178 year: 2019 end-page: 189 ident: b5 article-title: Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control publication-title: Neural Networks – volume: 30 start-page: 6383 year: 2020 end-page: 6398 ident: b4 article-title: Model-free adaptive control for a class of nonlinear systems with uniform quantizer publication-title: International Journal of Robust and Nonlinear Control – year: 1994 ident: b10 article-title: The parameter identification, adaptive control and model free learning adaptive control for nonlinear systems – volume: 12 start-page: 176 year: 2004 end-page: 182 ident: b16 article-title: Data-driven control design for neuroprotheses: A virtual reference feedback tuning (VRFT) approach publication-title: IEEE Transactions on Control Systems Technology – reference: (pp. 1734–1740). – volume: 47 start-page: 241 year: 2002 end-page: 252 ident: b18 article-title: New results on the synthesis of PID controllers publication-title: IEEE Transactions on Automatic Control – volume: 64 start-page: 4076 year: 2016 end-page: 4090 ident: b11 article-title: An overview of dynamic-linearization-based data-driven control and applications publication-title: IEEE Transactions on Industrial Electronics – volume: 121 start-page: 329 year: 2020 end-page: 338 ident: b19 article-title: A waiting-time-based event-triggered scheme for stabilization of complex-valued neural networks publication-title: Neural Networks – volume: 25 start-page: 1013 year: 2013 end-page: 1020 ident: b25 article-title: Data-driven MFAC for a class of discrete-time nonlinear systems with RBFNN publication-title: IEEE Transactions on Neural Networks and Learning Systems – ident: 10.1016/j.neunet.2022.09.021_b9 – volume: 30 start-page: 6383 year: 2020 ident: 10.1016/j.neunet.2022.09.021_b4 article-title: Model-free adaptive control for a class of nonlinear systems with uniform quantizer publication-title: International Journal of Robust and Nonlinear Control doi: 10.1002/rnc.5107 – volume: 51 start-page: 1812 year: 2020 ident: 10.1016/j.neunet.2022.09.021_b7 article-title: A dynamic periodic event-triggered approach to consensus of heterogeneous linear multiagent systems with time-varying communication delays publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2020.3015746 – volume: 119 start-page: 178 year: 2019 ident: 10.1016/j.neunet.2022.09.021_b5 article-title: Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control publication-title: Neural Networks doi: 10.1016/j.neunet.2019.08.011 – volume: 47 start-page: 241 year: 2002 ident: 10.1016/j.neunet.2022.09.021_b18 article-title: New results on the synthesis of PID controllers publication-title: IEEE Transactions on Automatic Control doi: 10.1109/9.983352 – volume: 63 start-page: 1883 year: 2017 ident: 10.1016/j.neunet.2022.09.021_b17 article-title: Learning model predictive control for iterative tasks. A data-driven control framework publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2017.2753460 – volume: 13 start-page: 2197 year: 2019 ident: 10.1016/j.neunet.2022.09.021_b13 article-title: Data-driven-based event-triggered tracking control for non-linear systems with unknown disturbance publication-title: IET Control Theory & Applications doi: 10.1049/iet-cta.2019.0051 – year: 1994 ident: 10.1016/j.neunet.2022.09.021_b10 – volume: 272 start-page: 356 year: 2018 ident: 10.1016/j.neunet.2022.09.021_b14 article-title: Neural network-based event-triggered MFAC for nonlinear discrete-time processes publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.07.008 – volume: 125 start-page: 31 year: 2020 ident: 10.1016/j.neunet.2022.09.021_b8 article-title: Event-triggered synchronization of discrete-time neural networks: A switching approach publication-title: Neural Networks doi: 10.1016/j.neunet.2020.01.024 – volume: 61 start-page: 6391 year: 2014 ident: 10.1016/j.neunet.2022.09.021_b20 article-title: A novel model-free adaptive control design for multivariable industrial processes publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2014.2308161 – volume: 65 start-page: 909 year: 2019 ident: 10.1016/j.neunet.2022.09.021_b6 article-title: Formulas for data-driven control: Stabilization, optimality, and robustness publication-title: IEEE Transactions on Automatic Control doi: 10.1109/TAC.2019.2959924 – volume: 19 start-page: 1549 year: 2010 ident: 10.1016/j.neunet.2022.09.021_b12 article-title: A novel data-driven control approach for a class of discrete-time nonlinear systems publication-title: IEEE Transactions on Control Systems Technology doi: 10.1109/TCST.2010.2093136 – volume: 14 start-page: 42 year: 2012 ident: 10.1016/j.neunet.2022.09.021_b1 article-title: Model free adaptive control with disturbance observer publication-title: Journal of Control Engineering and Applied Informatics – year: 2021 ident: 10.1016/j.neunet.2022.09.021_b3 article-title: Event-triggered model-free adaptive iterative learning control for a class of nonlinear systems over fading channels publication-title: IEEE Transactions on Cybernetics – volume: 50 start-page: 5119 year: 2018 ident: 10.1016/j.neunet.2022.09.021_b2 article-title: Quantized data driven iterative learning control for a class of nonlinear systems with sensor saturation publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems doi: 10.1109/TSMC.2018.2866909 – volume: 64 start-page: 4076 year: 2016 ident: 10.1016/j.neunet.2022.09.021_b11 article-title: An overview of dynamic-linearization-based data-driven control and applications publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2016.2636126 – volume: 12 start-page: 176 year: 2004 ident: 10.1016/j.neunet.2022.09.021_b16 article-title: Data-driven control design for neuroprotheses: A virtual reference feedback tuning (VRFT) approach publication-title: IEEE Transactions on Control Systems Technology doi: 10.1109/TCST.2003.821967 – ident: 10.1016/j.neunet.2022.09.021_b15 doi: 10.23919/ECC51009.2020.9143691 – volume: 32 start-page: 2518 year: 2022 ident: 10.1016/j.neunet.2022.09.021_b21 article-title: Dynamic learning control design for interval type-2 fuzzy singularly perturbed systems: A component-based event-triggering protocol publication-title: International Journal of Robust and Nonlinear Control doi: 10.1002/rnc.5661 – volume: 25 start-page: 1013 year: 2013 ident: 10.1016/j.neunet.2022.09.021_b25 article-title: Data-driven MFAC for a class of discrete-time nonlinear systems with RBFNN publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2013.2291792 – volume: 524 start-page: 136 year: 2019 ident: 10.1016/j.neunet.2022.09.021_b22 article-title: Event-based networked predictive control for networked control systems subject to two-channel delays publication-title: Information Sciences doi: 10.1016/j.ins.2020.03.031 – volume: 32 start-page: 5502 year: 2021 ident: 10.1016/j.neunet.2022.09.021_b24 article-title: Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2021.3053269 – volume: 121 start-page: 329 year: 2020 ident: 10.1016/j.neunet.2022.09.021_b19 article-title: A waiting-time-based event-triggered scheme for stabilization of complex-valued neural networks publication-title: Neural Networks doi: 10.1016/j.neunet.2019.09.032 – volume: 31 start-page: 1170 year: 2019 ident: 10.1016/j.neunet.2022.09.021_b23 article-title: RBFNN-based data-driven predictive iterative learning control for nonaffine nonlinear systems publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2019.2919441 |
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