adaptivrobust

Adaptivrobust is a term used to describe a class of control strategies that integrate adaptive and robust control techniques to maintain stable and desirable performance in the presence of model uncertainties and external disturbances. The concept acknowledges that purely adaptive methods may be sensitive to unmodeled dynamics or require persistent excitation, while purely robust methods can be conservative and slow to respond to real-time changes. Adaptivrobust frameworks attempt to combine online parameter estimation with robustness guarantees by embedding adaptive laws within a robust control structure, or by augmenting robust controllers with adaptive elements, such as parameter update laws and projection operators.

Common approaches include composite adaptive control, where parameter estimates are refined using both tracking errors and model errors; robust model reference adaptive control with a robustifying term to tolerate disturbances; and adaptive backstepping with H-infinity or mu-synthesis components. Stability is typically established using Lyapunov functions that include both parameter estimation errors and state errors, yielding conditions that can be checked or solved as LMIs in more complex systems.

Applications and limitations: Adaptivrobust concepts have been explored in aerospace, automotive, robotics, and process control. Benefits include improved disturbance rejection, faster adaptation to changing dynamics, and enhanced reliability under uncertainty. Limitations involve design complexity, potential conservatism, the need for sufficient excitation to guarantee convergence, and sensitivity to unmodeled high-frequency dynamics or modeling errors.