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Adaptive Leader-Following and Leaderless Consensus of a Class of Nonlinear Systems Using Neural Networks | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 13، دوره 48، شماره 2، بهمن 2016، صفحه 123-138 اصل مقاله (1.91 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2016.837 | ||
| نویسندگان | ||
| Bahram Karimi* 1؛ Hassan Ghiti Sarand2 | ||
| 1Associated Professor, Department of Electrical Engineering, Malek-e Ashtar University of Technology | ||
| 2Assistant Professor, Department of Electrical Engineering, Malek-e Ashtar University of Technology | ||
| چکیده | ||
| This paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (MIMO) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. The agents may have different dynamics and communicate together under a directed graph. A distributed adaptive method is designed for both cases. The structures of the controllers simplify their implementation and reduce computational cost. Unknown nonlinearities are estimated by a radial basis function neural network (RBFNN). The ultimate boundness of the closed-loop system is guaranteed through Lyapunov stability analysis by introducing a suitably driven adaptive rule. Finally, the simulation results verify performance of the proposed control method. | ||
| کلیدواژه‌ها | ||
| Adaptive control؛ Consensus؛ MIMO systems؛ neural networks؛ multi-agent systems | ||
| عنوان مقاله [English] | ||
| Adaptive Leader-Following and Leaderless Consensus of a Class of Nonlinear Systems Using Neural Networks | ||
| نویسندگان [English] | ||
| Bahram Karimi1؛ Hassan Ghiti Sarand2 | ||
| 1Malek ashtar University of Technology | ||
| 2Malek Ashtar University of Technology | ||
| چکیده [English] | ||
| This paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (MIMO) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. The agents may have different dynamics and communicate together under a directed graph. A distributed adaptive method is designed for both cases. The structures of the controllers simplify their implementation and reduce computational cost. Unknown nonlinearities are estimated by a radial basis function neural network (RBFNN). The ultimate boundness of the closed-loop system is guaranteed through Lyapunov stability analysis by introducing a suitably driven adaptive rule. Finally, the simulation results verify performance of the proposed control method. | ||
| کلیدواژه‌ها [English] | ||
| Adaptive control, Consensus, MIMO systems, neural networks, multi-agent systems | ||
| مراجع | ||
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