Adaptive neural control with stable learning

S.J. Hepworth and A.L. Dexter

Mathematics and Computers in Simulation (MATCOM), 1996, vol. 41, issue 1, 39-51

Abstract: The paper considers the problem of training on-line a neural network model of non-linear heater battery for implementation in a model-based control scheme. A stable learning scheme is proposed which reduces parameter drift due to process-model mismatch in radial basis function (RBF) networks. A network of pre-defined structure is trained and shown to exhibit finite model mismatch errors, which can produce parameter drift or “de-training” of the network, resulting in inferior control performance. A deadzone approach, similar to ones used in linear dynamic system identification, is applied to RBF network adaption, successfully reducing the degree of “de-training”. The learning scheme is used in a neural controller which is capable of compensating for plant non-linearities, and adapting on-line to degradation in the plant. Experimental results are presented which have been obtained from a flow-controlled heating coil, on a full-size air conditioning plant at the UK Building Research Establishment.

Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:41:y:1996:i:1:p:39-51

DOI: 10.1016/0378-4754(95)00057-7

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