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首页> 外文期刊>Mathematical Problems in Engineering >Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks
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Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks

机译：一类不确定非线性系统的改进自适应滑模控制。

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摘要

The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzyeural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach.
机译：针对一类具有未知模型动力学输入非线性的非线性系统，提出了一种基于模糊神经网络的改进的自适应滑模控制方法。控制方案包括改进的自适应控制器和补偿控制器。改进的自适应控制器在线估计未知的模型动力学和输入非线性，然后构造滑模控制律，而补偿控制器考虑到近似误差并保持系统的鲁棒性。基于李雅普诺夫稳定性定理，该方法可以保证跟踪误差为零的渐近收敛，并为闭环系统提供鲁棒的稳定性。另外，由于控制器设计的修改，完全消除了基于模糊/神经近似的间接自适应控制技术中通常出现的奇异性问题。最后，在倒立摆系统上进行的仿真结果证明了所提出的自适应控制方法的先进功能和可行性。

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