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首页> 外文期刊>Industrial Electronics, IEEE Transactions on >Observer-Based Adaptive Sliding Mode Control for Nonlinear Stochastic Markov Jump Systems via T–S Fuzzy Modeling: Applications to Robot Arm Model
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Observer-Based Adaptive Sliding Mode Control for Nonlinear Stochastic Markov Jump Systems via T–S Fuzzy Modeling: Applications to Robot Arm Model

机译：基于Observer的自适应滑模控制非线性随机马尔可夫跳跃系统，通过T-S模糊建模：机器人臂模型的应用

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摘要

In this article, the issue of sliding mode control for nonlinear stochastic Markovian jump systems with uncertain time-varying delay is investigated. Considering the system state measurements and the state-dependent disturbances are not available for feedback purposes, an observer-based adaptive control strategy is proposed. Based on the decomposition of the input matrices, the state-space representation of the system is turned into a regular form with the aid of T–S fuzzy models first. Then, a fuzzy observer system is constructed, which could be transformed into two lower order subsystems. By choosing a common linear switching surface, on which it also obtains linear sliding mode dynamics in a simple form. Further, an adaptive controller is synthesized relying on the bounded system delay information to ensure the estimated states driven on the predefined sliding surface and remain the sliding motion. Also, the stochastic stability analysis of the sliding mode dynamics is undertaken with two types of transition rates, and an interesting result reveals that the stability for the dynamics with type of uncertain transition rates may cover the completely known type. Finally, a single-link robot arm model is provided to verify the validity of the proposed method.
机译：在本文中，研究了具有不确定时变延迟的非线性随机马尔可夫跳跃系统的滑模控制问题。考虑到系统状态测量和状态依赖性干扰不可用于反馈目的，提出了一种基于观察者的自适应控制策略。基于输入矩阵的分解，系统的状态表示借助于T-S模糊模型将其变成常规形式。然后，构造模糊观察者系统，可以将其转换为两个下订单子系统。通过选择公共线性切换表面，在其上以简单的形式获得线性滑动模式动态。此外，合成自适应控制器依赖于有界系统延迟信息，以确保在预定的滑动表面上驱动的估计状态并保持滑动运动。而且，通过两种类型的转变速率进行了滑动模式动态的随机稳定性分析，并且有趣的结果表明，具有不确定转变率的动态的稳定性可以覆盖完全已知的类型。最后，提供了一种单链路机器人臂模型来验证所提出的方法的有效性。

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