Neural network commissioning of a PI controller for a rigidly coupled motor/mechanical system

A neural network based tuning scheme for a motion control system is described. A continuous-time tuning rule is developed. This provides guaranteed system performance, based on both frequency domain (bandwidth) and time domain (overshoot) criteria. The neural net is trained entirely from simulation experiments and its pattern recognition capabilities are utilised to determine optimum controller gain values from experimental test data. It is found that the finite bandwidth of the current loop amplifier controlling the motor current can lead to undesirable effects on the demanded closed loop velocity performance. A shift factor is introduced to the neural net selected gains to counter the overshoot and bandwidth error introduced by the non-ideality of this loop. A nonlinear sampling technique is introduced which allows sufficiently accurate tuning over a larger workspace of parameter variation. The subsequent on-line performance of the neural net-tuned servo-system is tested through experimental results. The neural net topology and training algorithm are also detailed