FSE-RBFNN-Based AILC of Finite Time Complete Tracking for a Class of Time-Varying NPNL Systems with Initial State Errors
The paper proposed an adaptive iterative learning control (AILC) strategy for the unmatched uncertain time-varying nonparameterized nonlinear systems (NPNL systems). Addressing the difficulty of nonlinear parameterization terms in system models, a new function approximator (FSE-RBFNN) which is combi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2024/3744735 |
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| _version_ | 1832542950285901824 |
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| author | Chunli Zhang Lei Yan Yangjie Gao |
| author_facet | Chunli Zhang Lei Yan Yangjie Gao |
| author_sort | Chunli Zhang |
| collection | DOAJ |
| description | The paper proposed an adaptive iterative learning control (AILC) strategy for the unmatched uncertain time-varying nonparameterized nonlinear systems (NPNL systems). Addressing the difficulty of nonlinear parameterization terms in system models, a new function approximator (FSE-RBFNN) which is combined with radial basis function neural network (RBFNN) and Fourier series expansion (FSE) is introduced to model each time-varying nonlinear parameterization function. Using adaptive backstepping method to design control laws and parameter adaptive laws. As the number of iterations increases, the maximum tracking error gradually decreases until it converges to zero on the entire given interval 0,T according to the Lyapunov-like synthesis. A updated time-varying boundary layer is introduced to eliminating the impact of initial state errors. Introducing a series to deal with the unknown error upper bounds. Finally, two simulation examples demonstrate the correctness of the proposed control method. |
| format | Article |
| id | doaj-art-39fd6980086e4bd3878dbe8d4c689ad6 |
| institution | Kabale University |
| issn | 1687-9139 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-39fd6980086e4bd3878dbe8d4c689ad62025-02-03T12:01:24ZengWileyAdvances in Mathematical Physics1687-91392024-01-01202410.1155/2024/3744735FSE-RBFNN-Based AILC of Finite Time Complete Tracking for a Class of Time-Varying NPNL Systems with Initial State ErrorsChunli Zhang0Lei Yan1Yangjie Gao2School of Automation and Information EngineeringSchool of Automation and Information EngineeringSchool of Automation and Information EngineeringThe paper proposed an adaptive iterative learning control (AILC) strategy for the unmatched uncertain time-varying nonparameterized nonlinear systems (NPNL systems). Addressing the difficulty of nonlinear parameterization terms in system models, a new function approximator (FSE-RBFNN) which is combined with radial basis function neural network (RBFNN) and Fourier series expansion (FSE) is introduced to model each time-varying nonlinear parameterization function. Using adaptive backstepping method to design control laws and parameter adaptive laws. As the number of iterations increases, the maximum tracking error gradually decreases until it converges to zero on the entire given interval 0,T according to the Lyapunov-like synthesis. A updated time-varying boundary layer is introduced to eliminating the impact of initial state errors. Introducing a series to deal with the unknown error upper bounds. Finally, two simulation examples demonstrate the correctness of the proposed control method.http://dx.doi.org/10.1155/2024/3744735 |
| spellingShingle | Chunli Zhang Lei Yan Yangjie Gao FSE-RBFNN-Based AILC of Finite Time Complete Tracking for a Class of Time-Varying NPNL Systems with Initial State Errors Advances in Mathematical Physics |
| title | FSE-RBFNN-Based AILC of Finite Time Complete Tracking for a Class of Time-Varying NPNL Systems with Initial State Errors |
| title_full | FSE-RBFNN-Based AILC of Finite Time Complete Tracking for a Class of Time-Varying NPNL Systems with Initial State Errors |
| title_fullStr | FSE-RBFNN-Based AILC of Finite Time Complete Tracking for a Class of Time-Varying NPNL Systems with Initial State Errors |
| title_full_unstemmed | FSE-RBFNN-Based AILC of Finite Time Complete Tracking for a Class of Time-Varying NPNL Systems with Initial State Errors |
| title_short | FSE-RBFNN-Based AILC of Finite Time Complete Tracking for a Class of Time-Varying NPNL Systems with Initial State Errors |
| title_sort | fse rbfnn based ailc of finite time complete tracking for a class of time varying npnl systems with initial state errors |
| url | http://dx.doi.org/10.1155/2024/3744735 |
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