Fuzzy-Neural Sliding Mode Control of Uncertain Systems: A Lyapunov Theory Approach

不确定系统的模糊神经滑模控制：李雅普诺夫理论方法

• 批准号: 9819310
• 负责人: Stanislaw Zak
• 金额: $ 19.76万
• 依托单位: Purdue Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-09-15 至 2003-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9819310&HistoricalAwards=false
• 关键词: Fuzzy; Neural; Sliding; Mode; Control

The objective of the proposed research is to incorporate neural networks, fuzzy systems and genetic algorithms into the design of sliding mode controllers, sliding mode state estimators and sliding mode identifiers of uncertain or nonlinear dynamical systems. A paradigm for fuzzy modeling will be provided that allows for systematic construction of fuzzy models for the purpose of the controllers and state estimators' design. The controllers and state estimators' stability and their guaranteed performance will be analyzed and then tested on a simulation model of a ground vehicle. Optimization of the controllers and estimators' parameters will be achieved using genetic algorithms. In practice, the controller as well as the plant are subject to various nonlinear constraints like hard bounds on gains, limited energy, or finite switching speeds that must be taken into account in a realistic controller design. In addition, due to lack of knowledge of parameter values or inaccuracies in the modeling process, the designer must cope with uncertainties in the plant model. In this project, a deterministic approach to the control, identification and state estimation of uncertain dynamical systems is taken. Adaptation algorithms for continuous-time sliding mode neural identifiers will be studied and novel variable structure sliding mode fuzzy controllers and state estimators will be developed. Then, the proposed structures will be integrated into self-organizing fuzzy-neural sliding mode tracking controllers. Neural network and fuzzy logic controllers have been used with considerable success in closed-loop applications. However, these applications, though very successful, have no proofs of guaranteed stability for uncertain systems with control variables limited in amplitude. In the proposed research, the direct method of Lyapunov, Hahn's extensions of the Lyapunov method and LaSalle's Invariance Principle will be used in the stability and guaranteed performance analyses of fuzzy-neural sliding mode control and identification structures. The proposed controllers, estimators and identifiers will be tested on the recently developed ground vehicle model that includes lateral weight transfer and tires' models. This model is suitable for the evaluation of the vehicle dynamical behavior in real-time. The results of the proposed research will contribute to the basic control theory as well as to the intelligent vehicle control systems.

该研究的目的是将神经网络、模糊系统和遗传算法结合到不确定或非线性动态系统的滑模控制器、滑模状态估值器和滑模辨识器的设计中。将提供一种模糊建模的范例，以便为控制器和状态估计器的设计而系统地构建模糊模型。控制器和状态估值器的稳定性及其保证性能将被分析，然后在地面车辆的仿真模型上进行测试。控制器和估值器参数的优化将使用遗传算法来实现。在实践中，控制器和对象都受到各种非线性约束，如增益的硬边界、有限的能量或有限的切换速度，这些都是现实控制器设计中必须考虑的因素。此外，由于缺乏参数值的知识或建模过程中的不准确，设计者必须处理对象模型中的不确定性。在这个项目中，对不确定动态系统的控制、辨识和状态估计采取了确定性的方法。研究连续时间滑模神经辨识器的自适应算法，开发新型变结构滑模模糊控制器和状态估值器。然后，将所提出的结构集成到自组织模糊神经滑模跟踪控制器中。神经网络和模糊逻辑控制器已经在闭环系统中获得了相当大的成功。然而，这些应用虽然非常成功，但对于控制变量幅值有限的不确定系统，并没有保证稳定性的证明。在所提出的研究中，将使用Lyapunov直接方法、Hahn对Lyapunov方法的推广和LaSalle不变性原理来分析模糊神经滑模控制和辨识结构的稳定性和保性能。拟议的控制器、估计器和识别器将在最近开发的地面车辆模型上进行测试，其中包括横向重量转移和轮胎模型。该模型适用于车辆动力学行为的实时评价。本文的研究成果将为车辆控制的基础理论和智能车辆控制系统的研究奠定基础。