Self-falsification in multimodel unfalsified adaptive switching control

摘要

This paper addresses a multimodel unfalsified adaptive switching control with finite fixed time window cost function by utilizing a self-falsification strategy. A closed-loop stability proof is provided, and it is shown that the forgetting factor employed with finite fixed windowed cost function improves the closed-loop performance. Furthermore, it is shown that the unfalsified adaptive control with nonmonotone cost function is unable to select the appropriate controller, and a new reset strategy is proposed to resolve this problem. The sequence monotonicity in the linear increasing cost-level algorithm causes a performance deterioration, and a sequence reset is introduced for performance enhancement. Effectiveness of the proposed method is investigated for a nonlinear pH neutralization process and the 2-cart benchmark example.