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Parametric uncertainty associated with unmodeled disturbance always exist in physical electrical–optical gyro-stabilized platform systems, and poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. By constructing a smooth dead-zone inverse, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback is proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded (GUUB) tracking stability of servo systems recently. An asymptotic tracking performance in the presence of unknown dead-zone, parametric uncertainties and various disturbances, which is vital for high accuracy tracking, is achieved by the proposed robust adaptive backstepping controller. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy. 相似文献
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Neural adaptive global stability control for robot manipulators with time‐varying output constraints
In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time‐varying parameter that installed in the fore‐end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time‐varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time‐varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods. 相似文献
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基于L2增益稳定的航天器鲁棒姿态控制 总被引:1,自引:0,他引:1
研究了有界干扰输入下的航天器姿态调节控制问题,设计了鲁棒姿态控制算法,使得从干扰力矩输入到系统某一指定的性能输出的L2增益小于任意给定的正常数,从而实现了对干扰力矩的抑制.避开了直接求解使闭环系统L2增益小于给定值的某个相应的Hamilton-Jacobi-Isaacs偏微分方程或不等式的困难.通过选取恰当的Lyapunov函数,分析证明了前述L2增益是小于任意给定的正值,保证了闭环系统的状态轨迹在有界干扰作用下是全局最终一致有界的.仿真结果验证了所提出的鲁棒控制方案的有效性. 相似文献
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