Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis
Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis
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DOI：
10.1109/tnnls.2013.2283879
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发表时间：
2014-05
影响因子：
10.4
通讯作者：
Huanqing Wang;Bing Chen;Kefu Liu;Xiaoping Liu;Chong Lin
中科院分区：
文献类型：
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作者：
Huanqing Wang;Bing Chen;Kefu Liu;Xiaoping Liu;Chong Lin
研究了一类具有未知类反冲滞环非线性的非严格反馈随机非线性系统的自适应神经网络控制问题。为克服非严格反馈结构的设计困难，采用变量分离技术将所有状态变量的未知函数分解为各误差动态的光滑函数之和。将径向基函数神经网络的普适逼近能力与自适应反推技术相结合，提出了一种自适应神经网络控制算法。结果表明，所设计的控制器保证了闭环系统中所有的信号是四阶矩半全局一致最终有界的，并且跟踪误差最终收敛到原点的一个四次均值意义下的小邻域内.仿真结果进一步验证了所提控制方案的有效性。
This paper considers the problem of adaptive neural control of stochastic nonlinear systems in nonstrict-feedback form with unknown backlash-like hysteresis nonlinearities. To overcome the design difficulty of nonstrict-feedback structure, variable separation technique is used to decompose the unknown functions of all state variables into a sum of smooth functions of each error dynamic. By combining radial basis function neural networks' universal approximation capability with an adaptive backstepping technique, an adaptive neural control algorithm is proposed. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are four-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results further show the effectiveness of the presented control scheme.