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.

拟议研究的目的是将神经网络，模糊系统和遗传算法纳入滑动模式控制器，滑动模式状态估计器以及不确定或非线性动力学系统的滑动模式标识符的设计。 将提供一个模糊建模的范式，以实现控制器和状态估计器设计的目的系统构建模糊模型。 控制器和州估计器的稳定性及其保证性能将进行分析，然后在地面车辆的仿真模型上进行测试。 使用遗传算法将实现控制器和估计值参数的优化。 实际上，控制器和工厂都受到各种非线性约束，例如在逼真的控制器设计中必须考虑到必须考虑到必须考虑到的强度，有限的能量或有限的开关速度。 此外，由于缺乏对建模过程中参数值或不准确性的了解，设计师必须应对植物模型中的不确定性。 在该项目中，采用了对控制，识别和状态估计不确定动态系统的确定性方法。 将研究连续滑动模式神经标识符的适应算法，并将开发新型可变结构滑动模式模糊控制器和状态估计器。 然后，提出的结构将集成到自组织模糊的滑动模式跟踪控制器中。 神经网络和模糊逻辑控制器已在闭环应用中取得了很大成功。 但是，这些应用程序虽然非常成功，但没有证据证明对控制变量的不确定系统有限的稳定性有限。 在拟议的研究中，Lyapunov的直接方法，Hahn对Lyapunov方法的扩展和LaSalle的不变性原理将用于模糊隐藏滑动模式控制和识别结构的稳定性和保证性能分析。 提议的控制器，估计器和标识符将在最近开发的地面车辆模型上进行测试，该模型包括侧重重量转移和轮胎模型。 该模型适用于实时评估车辆动力学行为。 拟议研究的结果将有助于基本控制理论以及智能车辆控制系统。