针对直驱式电液伺服系统的非线性不确定负载因数学模型无法准确描述而难以补偿问题，提出一种多滑模神经网络自适应控制策略。该控制策略结合了RBF神经网络对复杂非线性系统的强大映射能力以及响应速度快和滑模变结构控制的强抗干扰性2种控制算法优点，并利用Backstepping方法设计控制器。通过将传统滑模变结构控制中的非连续切换函数优化成连续可倒的切换函数，有效避免了传统滑模变结构控制的抖振现象。运用Lyapunov稳定性定理，理论证明了控制算法的稳定性，并通过仿真分析，验证了理论分析的正确性和控制策略的有效性。
A multiple sliding mode neural network adaptive control scheme, which combines the strong ability to map the complicated nonlinear system and fast responsibility of RBF neural network with anti-jamming of the sliding mode variable structure control, is proposed for the uncertain nonlinear load of direct drive electro-hydraulic servo system. Using the Backstepping method and substituting the discontinuous switching function of traditional sliding mode variable structure control for the continuous derivable function, a controller is designed, which avoids the chattering of the traditional sliding mode variable structure control effectively. The stability of the control algorithm is proved by employing the Lyapunov stability theorem. Simulation results demonstrate the correctness and effectivenness of the control strategy.
2018,40(5): 79-84 收稿日期：2017-01-03
DOI：10.3404/j.issn.1672-7649.2018.05.014
分类号：TP273
作者简介：徐荣武(1989-),男,博士研究生,主要研究方向为振动与噪声控制
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