Evolutionary neural networks and DNA computing algorithms for dual-axis motion control

A new method is proposed to deal with the dual-axis control of a multi-variables system with two induction motors. Investigation of resolving the cross-coupling problem of dual-axis platform is addressed by a neural net-based decoupling compensator and a sufficient condition ensuring closed-loop stability is derived. An evolutionary algorithm processing the universal seeking capability is proposed for finding the optimal connecting weights of the neural decoupling compensator and the gains of PID controllers. Extensive numerical studies verify the performance and applicability of the proposed design under a variety of operating conditions.

► The DNACA coded neural network-based positioning cross-coupling error compensation system is applicable to dual-axes.
► The purpose is to characterize the admissible domain of the PID control gains and ANN so that the closed-loop would remain to be stable.
► Evaluation of the transient response can be executed via an effective method of directly relating to the tracking performance.
► Seeking for the optimal connecting weights of the ANN and PID control gains is the major issue.