Abstract
This paper studies the cooperative control problem for second-order stochastic strict-feedback nonlinear multi-agent systems. The virtual and actual control items are the power functions with positive odd integers. By using the adding a power integrator technique, the obstacle of the high powers of control items is overcome. Moreover, an adaptive neural networks compensation control approach is applied to tackle the problems of sensor faults, and the neural networks are employed to estimate unknown nonlinear functions. Based on the stochastic Lyapunov functional method, it is proved that the consensus tracking errors can converge to a small neighborhood of the origin, and all signals of the closed-loop system are semi-globally uniformly ultimately bounded in probability. Finally, a simulation example is proposed to verify the availability of the control strategy.
Published Version
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