Adaptive design of a fuzzy cerebellar model arithmetic controller neural network

J.-Y. Chen; P.-S. Tsai; C.-C. Wong

Adaptive design of a fuzzy cerebellar model arithmetic controller neural network

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Adaptive design of a fuzzy cerebellar model arithmetic controller neural network

Author(s): J.-Y. Chen ; P.-S. Tsai ; C.-C. Wong
DOI: 10.1049/ip-cta:20041117

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Author(s): J.-Y. Chen ¹ ; P.-S. Tsai ¹ ; C.-C. Wong ²
- Affiliations: 1: Department of Electronic Engineering, China Institute of Technology, Taipei, Taiwan
  2: Department of Electrical Engineering, Tamkang University, Teipei, Taiwan, ROC
Source: Volume 152, Issue 2, March 2005, p. 133 – 137
DOI: 10.1049/ip-cta:20041117 , Print ISSN 1350-2379, Online ISSN 1359-7035

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Adaptation fuzzy cerebellar model arithmetic controller (CMAC) neural networks are considered. Adaptation mechanisms for a fuzzy CMAC neural network are proposed to enable the construction of indirect and direct control laws. These control laws are then used to enhance the robustness of a closed-loop control system. It is shown that the fuzzy CMAC's can cope with the system's uncertainties using adaptation with no preliminary off-line learning phase being required. The adaptation laws are derived using a Lyapunov stability analysis, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system. Simulation results from the two systems show a satisfactory performance of the proposed control schemes even in the presence of modelling uncertainties.

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Adaptive design of a fuzzy cerebellar model arithmetic controller neural network

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