Neurocontrol: Learning Control Systems Inspired by Neuronal Architectures and Human Problem Solving Strategies

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Processes which are heavily non-linear and/or very complex pose a problem for automatic control, yet they can often be handled easily by human operators. This book describes results from 10 years of research on learning control loops which imitate these human abilities. After discussing the contrast with adaptive control, the authors present some background on human information processing and behaviour. A neuronally-inspired memory layout and a mathematically inspired one are compared and it is shown that they learn much faster than back-propagation neural networks. Different architectures are given for the learning control loop. Their usefulness is demonstrated by simulation and results from applications to real pilot plants. The book should be of interest to control engineers as well as researchers in neural net applications and/or artificial intelligence.