Neural Self-Tuning Adaptive Control on Non-Minimum Phase System Developed forFlexible Robotic ARM

摘要

The neural self-tuning control (NSTC) algorithm was developed and applied tocontrol the tip of a flexible arm system. The dynamics of the flexible arm tip involves an unstable zero, therefore making the system non-minimum phase. The NSTC is based on an indirect control method where the identification is performed by the neural network and the control is based on the generalized minimum variance (GMV) control law. Simulation results of the NSTC scheme and the conventional adaptive self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady-state response.