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.
