Disturbance Observer-based Adaptive Fault-tolerant Dynamic Surface Control of Nonlinear System with Asymmetric Input Saturation

In this paper, a prescribed performance-based distributed neural adaptive fault-tolerant cooperative control (FTCC) scheme is proposed for multiple unmanned aerial vehicles (multi-UAVs). A distributed sliding-mode observer (SMO) technique is first utilized to estimate the leader UAV’s reference. Then, by transforming the tracking errors of follower UAVs with respect to the estimated references into a new set, a distributed neural adaptive FTCC protocol is developed based on the combination of dynamic surface control (DSC) and minimal learning parameters of neural network (MLPNN). Moreover, auxiliary dynamic systems are exploited to deal with input saturation. Furthermore, the proposed control scheme can guarantee that all signals of the closed-loop system are bounded, and tracking errors of follower UAVs with respect to the estimated references are confined within the prescribed bounds. Finally, comparative simulation results are presented to illustrate the effectiveness of the proposed distributed neural adaptive FTCC scheme.

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Neural observer-based path following control for underactuated unmanned surface vessels with input saturation and time-varying disturbance

International Journal of Advanced Robotic Systems ◽

10.1177/1729881419878071 ◽

2019 ◽

Vol 16 (5) ◽

pp. 172988141987807

Author(s):

Lei Wan ◽

Jiangfeng Zeng ◽

Yueming Li ◽

Hongde Qin ◽

Lei Zhang ◽

...

Keyword(s):

Disturbance Observer ◽

External Disturbance ◽

Input Saturation ◽

Path Following ◽

Dynamic Surface Control ◽

Dynamic Surface ◽

Surface Control ◽

Surface Vessels ◽

Path Following Control ◽

Control Scheme

In this study, a new neural observer-based dynamic surface control scheme is proposed for the path following of underactuated unmanned surface vessels in the presence of input saturation and time-varying external disturbance. The dynamic surface control technique is augmented by a robust adaptive radial basis function neural network and a nonlinear neural disturbance observer. Radial basis function neural network is employed to deal with system uncertainties, and the nonlinear neural disturbance observer is developed to compensate for the unknown compound disturbance that contains the input saturation approximation error and the external disturbance. Moreover, the stringent known boundary requirement of the unknown disturbance constraint is eliminated with the proposed nonlinear neural disturbance observer. Meanwhile, to deal with the non-smooth saturation nonlinearity, a new parametric hyperbolic tangent function approximation model with arbitrary prescribed precision is constructed, which results in the transient performance improvement for the path following control system. Stability analysis shows that all the signals in the closed-loop system are guaranteed to be ultimately bounded. Comparative simulation results further demonstrate the effectiveness of the proposed control scheme.

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