Adaptive neural network-based synchronized control of dual-axis linear actuators

Synchronized motion control with high accuracy becomes very essential part in industry. Due to some possible effect such as unknown disturbance or unmatched system model, it is difficult to obtain the precision of synchronous control using the conventional proportional-integral control method with parallel architecture. The adaptive compensator must be employed to eliminate tracking errors. The objective of this research is to propose the modified cross-coupling architecture using single-neuron proportional-integral controller and a synchronous compensator for dual-axis linear actuator. The single-neuron proportional-integral control strategy with delta learning algorithm can adjust the weighting coefficients of controllers to provide the robustness for each single-axis DC linear actuator system. A back-propagation neural network compensator is designed to adaptively reduce position and velocity errors between the two-axis servo systems. Both simulation and experimental results are developed to demonstrate that the synchronous position tracking performances in terms of root mean square error and sum of absolute error can be substantially improved, and the robustness to linear actuator uncertainties can be obtained as well. The proposed coupling strategy which uses the microchip platform and pulse-width modulation control technique is realized and implemented, and the synchronization performances to external disturbance load are illustrated by several experimental results.