Abstract
To imitate human flexibility in controlling different complex non-linear processes on the basis of process observation and/or trial and error, learning control has been developed. The main elements of such control loops are interpolating memories. The chapter deals after an introduction to learning control loops with such devices by putting forward different alternatives, discussing their behaviour in general and going into details of recent research work on mathematically inspired interpolating memories. The respective improvements are motivated and results of applications in the areas of biotechnology and automotive control are presented. In a conclusion some further application areas and realisation aspects are discussed and a critical assessment of status and usefulness of learning control is given.
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Tolle, H., Gehlen, S., Schmitt, M. (1995). On Interpolating Memories for Learning Control. In: Hunt, K.J., Irwin, G.R., Warwick, K. (eds) Neural Network Engineering in Dynamic Control Systems. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-3066-6_7
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