This paper describes a control system for a tubular synchronous linear motor based on a combination of a linear PID controller and a nonlinear neural network. The nonlinear part of the controller is introduced to progressively augment the tracking performance of the system and is trained online by a compact GA. In particular, we implement a variant of a known compact GA that well lends itself to practical implementations in low capacity microcontrollers, thanks to its reduced memory requirements and better distributed computational loads. The potential of the proposed approach is assessed by means of experimental tests using a tubular linear synchronous motor prototype. The control system obtained through genetic search outperforms alternative schemes obtained with linear design techniques in terms of robustness to payload mass change and sensitivity to static friction.

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