Fuzzy Logic Controller and Cerebellar Model Arithmetic Computer Neural Networks for Active Vibration Suppression

用于主动振动抑制的模糊逻辑控制器和小脑模型算术计算机神经网络

• 批准号: 9460523
• 负责人: Zheng (Jason) Geng
• 金额: $ 7.48万
• 依托单位: Robotronics Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-15 至 1996-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9460523&HistoricalAwards=false
• 关键词: Fuzzy; Logic; Controller; Cerebellar; Model

The primary objective of this proposed work is to study a novel artificial neural network architecture which synergistically combines the preferred features of the CMAC (cerebellar model arithmetic computer) neural networks and the Fuzzy Logic Controller (therefore we called it the Fuzzy-CMAC Neural Network), and attempt to utilize this novel Fuzzy-CMAC neural network in the implementation of the real-time learning control systems for active vibration suppression (AVS) applications. In typical AVS systems, the plants to be controlled are usually poorly-modeled, complex, highly nonlinear, and non-deterministic mechanical systems. Robotronics, Inc. believes that the advances in study of neural networks, especially the novel Fuzzy-CMAC neural network architecture proposed here, may offer an ideal approach to implement self-learning nonlinear control resulting in a significant performance improvement in AVS systems. As two separate control schemes, both CMAC neural networks and fuzzy logic controllers provide valuable designs for intelligent control systems. However, there has been no research activity reported so far which bridge the connection between these two approaches and apply it to the AVS. This proposed work attempts to establish the relationship between these models from an architectural viewpoint. Various features of both models are compared and a Fuzzy CMAC model which combines the desirable features of both models will be developed. An approach to apply the Fuzzy CMAC to active vibration suppression is discussed. Preliminary simulation results show very good learning and control performance. During Phase 1 work, theoretic issues of the Fuzzy-CMAC neural network architecture will be conducted. The proposed Fuzzy-CMAC active vibration suppression approach will be verified through nonlinear simulations and will be implemented using real-time multiple DSP control hardware for experiments. The results of simulations and real-time experiments to be performed will be evaluated and compared with those of existing controllers.

这项工作的主要目标是研究一种新的人工神经网络架构，协同结合的CMAC的首选功能（小脑模型算术计算机）神经网络和模糊逻辑控制器（因此我们称之为模糊CMAC神经网络），并尝试将这种新型的Fuzzy-CMAC神经网络应用于真实的主动振动抑制（AVS）应用的时间学习控制系统。 在典型的AVS系统中，被控对象通常是建模不完善、复杂、高度非线性和不确定的机械系统。 Robotronics公司认为神经网络研究的进展，特别是本文提出的新型Fuzzy-CMAC神经网络结构，可以提供一种理想的方法来实现自学习非线性控制，从而显着提高AVS系统的性能。 CMAC神经网络和模糊逻辑控制器作为两种独立的控制方案，为智能控制系统提供了有价值的设计。然而，迄今为止还没有研究活动报道这两种方法之间的联系，并将其应用到AVS。本文试图从建筑学的角度建立这些模型之间的关系。这两个模型的各种功能进行了比较，并将开发一个模糊CMAC模型，结合这两个模型的理想功能。探讨了模糊小脑模型神经网络在振动主动控制中的应用。初步的仿真结果表明，非常好的学习和控制性能。 在第一阶段的工作，模糊CMAC神经网络架构的理论问题将进行。所提出的模糊CMAC主动振动抑制方法将通过非线性仿真进行验证，并将使用实时多DSP控制硬件进行实验。模拟和实时实验的结果进行评估，并与现有的控制器进行比较。