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针对一类控制增益函数及符号均未知的不确定非线性系统,基于反推滑模设计方法,提出一种鲁棒自适应神经网络控制方案.结合Nussbaum增益设计技术和神经网络逼近能力,取消了控制增益函数及符号已知的条件,应用积分型Lyapunov函数避免了控制器奇异性问题,并通过引入神经网络逼近误差和不确定干扰上界的自适应补偿项消除了建模误差和不确定干扰的影响.理论分析证明了闭环系统所有信号半全局一致终结有界,仿真结果验证了该方法的有效性.
Aiming at a class of uncertain nonlinear systems with unknown control gain function and unknown sign, a robust adaptive neural network control scheme is proposed based on the backstepping sliding mode design method. Combining with Nussbaum gain design technique and neural network approximation ability, Control the gain function and the known conditions of the symbol, the integral Lyapunov function is used to avoid the singularity problem of the controller, and the adaptive compensation term of the approximation error and uncertain upper bound of the disturbance is introduced to eliminate the modeling error and the uncertain interference The theoretical analysis proves that all signals in the closed-loop system are semi-globally consistent and end-bound, and the simulation results verify the effectiveness of the proposed method.