Trajectory synthesis for stable tuning of non-collocated systems using the progressive learning theory

A novel approach to the design of trajectories for automatic tuning of non-collocated sensor/actuator systems is presented. This approach uses the progressive learning theory, a method for tuning many control parameters in a particular sequence so that all the parameters can be tuned stably and effectively. Using this progressive learning method a sequence of training trajectories can be designed, which not only satisfy geometric constraints for performing a task but also take into account the dynamics of the machine to better perform the task. This approach is implemented on a single degree-of-freedom high speed linear slider, and experimental results and discussions are presented at the end