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首页> 外文期刊>Neural computing & applications >Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems
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Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems

机译：一类MIMO非线性非仿射不确定系统的基于输出递归小波神经网络的自适应反推控制器

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摘要

In this paper, an adaptive backstepping control problem is proposed for a class of multiple-input-multiple-output nonlinear non-affine uncertain systems. An output recurrent wavelet neural network (ORWNN) is used to approximate the unknown nonlinear functions to develop the proposed adaptive backstepping controller. The proposed ORWNN combines the advantages of wavelet-based neural network, fuzzy neural network, and output feedback layer to achieve higher approximation accuracy and faster convergence. According to the estimation of ORWNN, the control scheme is designed by backstepping approach such that the system outputs follow the desired trajectories. Based on the Lyapunov approach, our approach guarantees that the system outputs converge to a small neighborhood of the references signals, that is, all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results including double pendulums system and two inverted pendulums on carts system are shown to demonstrate the performance and effectiveness of our approach.
机译：针对一类多输入多输出非线性非仿射不确定系统，提出了一种自适应反步控制问题。使用输出递归小波神经网络（ORWNN）近似未知的非线性函数，以开发所提出的自适应反推控制器。提出的ORWNN结合了基于小波的神经网络，模糊神经网络和输出反馈层的优点，以实现更高的逼近精度和更快的收敛速度。根据ORWNN的估计，通过反推方法设计控制方案，以使系统输出遵循所需的轨迹。基于Lyapunov方法，我们的方法可确保系统输出收敛到参考信号的较小邻域，也就是说，闭环系统的所有信号最终都是半全局一致有界的。最后，仿真结果包括双摆系统和推车系统上的两个倒立摆系统，证明了该方法的性能和有效性。

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