Abstract
The cerebellar model articulation computer (CMAC) neural network (NN) has advantages over fully connected NNs due to its increased structure. While its advantages over conventional control techniques have been recognized in the literature, it has primarily been used in system identification and pattern recognition, but not in control applications. This paper attempts to provide a comprehensive treatment of CMAC NNs in closed-loop control applications. Novel weight-update laws are derived that guarantee the stability of the closed-loop system. The passivity properties of the CMAC under the specified tuning laws are examined and the relationship between passivity and closed-loop stability is derived.
Published Version
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