Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances
In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/20/7073 |
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| author | Roxana Recio-Colmenares Kelly Joel Gurubel-Tun Virgilio Zúñiga-Grajeda |
| author_facet | Roxana Recio-Colmenares Kelly Joel Gurubel-Tun Virgilio Zúñiga-Grajeda |
| author_sort | Roxana Recio-Colmenares |
| collection | DOAJ |
| description | In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty. |
| first_indexed | 2024-03-10T15:42:39Z |
| format | Article |
| id | doaj.art-ab039d447d104c54b4e0fd2ff2b6cafc |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T15:42:39Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ab039d447d104c54b4e0fd2ff2b6cafc2023-11-20T16:42:34ZengMDPI AGApplied Sciences2076-34172020-10-011020707310.3390/app10207073Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with DisturbancesRoxana Recio-Colmenares0Kelly Joel Gurubel-Tun1Virgilio Zúñiga-Grajeda2School of Engineering and Technological Innovation, University of Guadalajara, Campus Tonalá, Jalisco 45425, MexicoSchool of Engineering and Technological Innovation, University of Guadalajara, Campus Tonalá, Jalisco 45425, MexicoSchool of Engineering and Technological Innovation, University of Guadalajara, Campus Tonalá, Jalisco 45425, MexicoIn this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty.https://www.mdpi.com/2076-3417/10/20/7073optimal controlartificial neural networkmetaheuristic optimizationnonlinear systems |
| spellingShingle | Roxana Recio-Colmenares Kelly Joel Gurubel-Tun Virgilio Zúñiga-Grajeda Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances Applied Sciences optimal control artificial neural network metaheuristic optimization nonlinear systems |
| title | Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances |
| title_full | Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances |
| title_fullStr | Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances |
| title_full_unstemmed | Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances |
| title_short | Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances |
| title_sort | optimal neural tracking control with metaheuristic parameter identification for uncertain nonlinear systems with disturbances |
| topic | optimal control artificial neural network metaheuristic optimization nonlinear systems |
| url | https://www.mdpi.com/2076-3417/10/20/7073 |
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