Adaptive Finite-Time Neural Network Control of Nonlinear Systems With Multiple Objective Constraints and Application to Electromechanical System
This article investigates an adaptive finite-time neural control for a class of strict feedback nonlinear systems with multiple objective constraints. In order to solve the main challenges brought by the state constraints and the emergence of finite-time stability, a new barrier Lyapunov function is...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2021-12, Vol.32 (12), p.5416-5426 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Liu, Lei Zhao, Wei Liu, Yan-Jun Tong, Shaocheng Wang, Yue-Ying |
| description | This article investigates an adaptive finite-time neural control for a class of strict feedback nonlinear systems with multiple objective constraints. In order to solve the main challenges brought by the state constraints and the emergence of finite-time stability, a new barrier Lyapunov function is proposed for the first time, not only can it solve multiobjective constraints effectively but also ensure that all states are always within the constraint intervals. Second, by combining the command filter method and backstepping control, the adaptive controller is designed. What is more, the proposed controller has the ability to avoid the "singularity" problem. The compensation mechanism is introduced to neutralize the error appearing in the filtering process. Furthermore, the neural network is used to approximate the unknown function in the design process. It is shown that the proposed finite-time neural adaptive control scheme achieves a good tracking effect. And each objective function does not violate the constraint bound. Finally, a simulation example of electromechanical dynamic system is given to prove the effectiveness of the proposed finite-time control strategy. |
| doi_str_mv | 10.1109/TNNLS.2020.3027689 |
| format | Article |
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In order to solve the main challenges brought by the state constraints and the emergence of finite-time stability, a new barrier Lyapunov function is proposed for the first time, not only can it solve multiobjective constraints effectively but also ensure that all states are always within the constraint intervals. Second, by combining the command filter method and backstepping control, the adaptive controller is designed. What is more, the proposed controller has the ability to avoid the "singularity" problem. The compensation mechanism is introduced to neutralize the error appearing in the filtering process. Furthermore, the neural network is used to approximate the unknown function in the design process. It is shown that the proposed finite-time neural adaptive control scheme achieves a good tracking effect. And each objective function does not violate the constraint bound. Finally, a simulation example of electromechanical dynamic system is given to prove the effectiveness of the proposed finite-time control strategy.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2020.3027689</identifier><identifier>PMID: 33064656</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Adaptive control ; Artificial neural networks ; Backstepping ; barrier Lyapunov functions (BLFs) ; command filter backstepping ; Constraint modelling ; Control systems ; Control systems design ; Controllers ; finite-time control ; Liapunov functions ; Multiple objective analysis ; Network control ; neural network (NN) ; Neural networks ; Nonlinear control ; Nonlinear systems ; Objective function ; Stability analysis</subject><ispartof>IEEE transaction on neural networks and learning systems, 2021-12, Vol.32 (12), p.5416-5426</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Finally, a simulation example of electromechanical dynamic system is given to prove the effectiveness of the proposed finite-time control strategy.</description><subject>Adaptive control</subject><subject>Artificial neural networks</subject><subject>Backstepping</subject><subject>barrier Lyapunov functions (BLFs)</subject><subject>command filter backstepping</subject><subject>Constraint modelling</subject><subject>Control systems</subject><subject>Control systems design</subject><subject>Controllers</subject><subject>finite-time control</subject><subject>Liapunov functions</subject><subject>Multiple objective analysis</subject><subject>Network control</subject><subject>neural network (NN)</subject><subject>Neural networks</subject><subject>Nonlinear control</subject><subject>Nonlinear systems</subject><subject>Objective function</subject><subject>Stability analysis</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkUFv0zAYhiMEYtPYHwAJWeLCJcX-Ejv2sao2QCrdYUVwi9zki-bi2MF2QPsX-8nz1tIDvnyW_byvLD9F8ZbRBWNUfdpuNuvbBVCgi4pCI6R6UZwDE1BCJeXL0775eVZcxrineQnKRa1eF2dVRUUtuDgvHpa9npL5g-TaOJOw3JoRyQbnoG0e6a8Pv8jKuxS8JX4gG--scagDub2PCcdIfph0R77NNpnJIrnZ7bF77suhmII2LkWiXU-W02RNp5PxjiRPrmzmgh-xu9Mun9tj4Zvi1aBtxMvjvCi-X19tV1_K9c3nr6vluuwqzlLZ1U0j2a7ud7xCVJTWODTIQTd9JwX0AwxccMZ6xSSDTgw74AJrCgNyWYOsLoqPh94p-N8zxtSOJnZorXbo59hCzZnkTf60jH74D937Obj8uhYErRVTQtFMwYHqgo8x4NBOwYw63LeMtk_K2mdl7ZOy9qgsh94fq-fdiP0p8k9QBt4dAIOIp2sFIKWC6hGxvpwo</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Liu, Lei</creator><creator>Zhao, Wei</creator><creator>Liu, Yan-Jun</creator><creator>Tong, Shaocheng</creator><creator>Wang, Yue-Ying</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Adaptive control Artificial neural networks Backstepping barrier Lyapunov functions (BLFs) command filter backstepping Constraint modelling Control systems Control systems design Controllers finite-time control Liapunov functions Multiple objective analysis Network control neural network (NN) Neural networks Nonlinear control Nonlinear systems Objective function Stability analysis |
| title | Adaptive Finite-Time Neural Network Control of Nonlinear Systems With Multiple Objective Constraints and Application to Electromechanical System |
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