Robust adaptive control for robot manipulators: Support vector regression-based command filtered adaptive backstepping approach
| Autor: | Achille Melingui, Bernard Z. Essimbi, Jean Bosco Mbede, Joseph Jean-Baptiste Mvogo Ahanda |
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| Rok vydání: | 2017 |
| Předmět: |
Lyapunov function
0209 industrial biotechnology Adaptive control Computer science General Mathematics Stability (learning theory) Control engineering 02 engineering and technology Computer Science Applications Support vector machine symbols.namesake 020901 industrial engineering & automation Control and Systems Engineering Approximation error Control theory Backstepping Bounded function 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Robust control Software |
| Zdroj: | Robotica. 36:516-534 |
| ISSN: | 1469-8668 0263-5747 |
| DOI: | 10.1017/s0263574717000534 |
| Popis: | SUMMARYThis study derives a robust adaptive control of electrically driven robot manipulators using a support vector regression (SVR)-based command filtered adaptive backstepping approach. The robot system is supposed to be subject to model uncertainties, neglected dynamics, and external disturbances. The command filtered backstepping algorithm is extended to the case of the robot manipulators. A robust term is added to the common adaptive SVR algorithm, to mitigate the effects of the SVR approximation error in the path tracking performance. The stability analysis of the closed loop system using the Lyapunov theory permits to highlight adaptation laws and to prove that all the signals in the closed loop system are bounded. Simulations show the effectiveness of the proposed control strategy. |
| Databáze: | OpenAIRE |
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