Motion parameter tuning for servomotor packs based on learning automata method

In this paper, we investigate the use of the learning automata method in tuning the motion parameters of servomotor packs with implicit control structures. For commercial servomotor packs, it is difficult to design motion parameters using systematic approaches. However, tuning these motion parameters is very important. Here, the tuning process is automated using a learning automata method that operates through interactions with unknown environments using a stochastic trial and error process; it also provides additional convergence information through probability density functions. Moreover, a tuning method that matches the dynamic responses of all synchronous motion axes is developed for increasing the contouring accuracy of multi-axis systems. The obtained experimental results indicate that the proposed method can effectively tune the motion parameters. As compared to tuning methods that do not consider matched dynamic responses, the proposed method achieves a reduction of 46.6% in the roundness error.