Bursting Phenomena in Extended-Least Squares Based Self-Tuning Control

In this paper, bursting phenomena inherent to extended least-squares based self-tuning control are analyzed. It is shown that in the absence of external excitation and without imposing structural conditions on the system model, bursts of the tracking error and consequently drift of the parameter estimates are possible. Our analysis reveals the basic principle of learning and adaptation mechanisms: every increase of system disorder causes corresponding increases of system intelligence. On the other hand, every increase of system precision causes decreases of system intelligence.