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首页> 外文会议>2011 International Conference on Advanced Mechatronic Systems >An observer-based adaptive type-2 fuzzy-neural controller for a class of MIMO systems with uncertainties
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An observer-based adaptive type-2 fuzzy-neural controller for a class of MIMO systems with uncertainties

机译：一类不确定性MIMO系统的基于观测器的自适应2型模糊神经控制器

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摘要

An observer-based adaptive controller based on a type-2 fuzzy neural network (type-2 FNN) is developed for a class of multi-input multi-output (MIMO) nonaffine nonlinear system. The interval type-2 fuzzy system is proposed in this paper as an alternative solution when a MIMO system has a large amount of uncertainties or the training data is corrupted by noise. By using implicit function theorem and Lyapunov theorem, the observer-based control law and the weight update law of the adaptive type-2 FNN controller are derived. Based on the design of the type-2 fuzzy neural network, the observer-based adaptive controller can improve its robustness to noise. In this paper, we prove that the proposed observer-based adaptive controller can guarantee that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Simulations results are reported to show the performance of the proposed control system mode and algorithms.
机译：针对一类多输入多输出（MIMO）非仿射非线性系统，开发了一种基于观察者的基于类型2模糊神经网络（类型2 FNN）的自适应控制器。当MIMO系统存在大量不确定性或训练数据被噪声破坏时，本文提出了区间2型模糊系统作为一种替代解决方案。利用隐函数定理和Lyapunov定理，推导了自适应2型FNN控制器的基于观测器的控制律和权重更新律。基于2型模糊神经网络的设计，基于观察者的自适应控制器可以提高其对噪声的鲁棒性。在本文中，我们证明了所提出的基于观测器的自适应控制器可以保证所涉及的所有信号都受到限制，并且闭环系统的输出渐近跟踪所需的输出轨迹。仿真结果被报道以显示所提出的控制系统模式和算法的性能。

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