Adaptive neural control with stable learning

✍ Scribed by S.J. Hepworth; A.L. Dexter

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 697 KB
• Volume: 41
• Category: Article
• ISSN: 0378-4754
• DOI: 10.1016/0378-4754(95)00057-7

✦ Synopsis

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 pertbrmance. 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.