Robust Periodic Adaptive Disturbance Observer Based Control Considering Long-Term Instability Problems for High-Load Motion Systems

Abstract

This study investigates the long-term instability problem of periodic-feedback-loop-based control schemes such as repetitive control, periodic adaptive disturbance observer (PADOB), and iterative learning control. The long-term instability problem is the one wherein a closed-loop system becomes unstable because a tracking error diverges with the increase in the number of iterations. A novel robust PADOB is proposed in this article to guarantee the long-term stability of the closed-loop system. The overall system eventually becomes stable because the proposed control scheme using an infinite impulse response (IIR) filter with an optimized lead time can stabilize the error propagation in the iteration domain. Compared to other methods such as zero-phase and IIR filters, the proposed control scheme is very practical because it allows selecting the cutoff frequency of the filter intuitively to stabilize the control system. The optimized lead time in the IIR filter is calculated considering the phase response of the IIR filter; it can improve adaptation speed using a high adaptation gain of the control system. Various simulation and experimental tests based on high-load motion systems that require high-precision positioning are performed to verify the theoretical analysis of the long-term instability problem and the effectiveness of the proposed control scheme.