Abstract
This paper addresses a neuroadaptive inverse optimal consensus problem of uncertain nonlinear multiagent systems (MASs) subject to actuator and sensor faults simultaneously. Unlike traditional adaptive dynamic programming methods, the proposed control mechanism minimizes a loss function without solving the Hamilton-Jacobi-Bellman equation, which simplifies the computational workload. In addition, a compensation strategy for actuator and sensor faults is considered and a novel fault-tolerant adaptive inverse optimal protocol incorporating the Lyapunov design is constructed. It is demonstrated that the system is input-to-state stabilizable (ISS) under the designed inverse optimal controller and the tracking errors of the MASs can converge to a predefined range. A simulation example is presented to illustrate the effectiveness of the control design.
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