Neuroadaptive asymptotic consensus tracking control for a class of uncertain nonlinear multiagent systems with sensor faults. (English) Zbl 1532.93188
Summary: This paper proposes an adaptive neural consensus tracking control approach for a class of leader-follower uncertain multiagent systems with sensor faults. Based on backstepping technique, a new direct adaptive neural control scheme is proposed to adaptively approximate the sensor faults. In order to improve the stability of the system and transient performance, a series of smooth functions are incorporated into control design and Lyapunov analysis. In addition, a class of reduced-order smooth functions is introduced to achieve a simpler virtual controller implementation. It is proved that the closed-loop signals are bounded and the synchronization errors can converge to a preset interval. Besides the asymptotic performance, a tunable \(L_2\)-norm transient performance is achieved. Finally, numerical and physical example are presented to validate the effectiveness of the proposed control scheme.
MSC:
| 93C40 | Adaptive control/observation systems |
| 93D50 | Consensus |
| 93A16 | Multi-agent systems |
| 93C10 | Nonlinear systems in control theory |
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