Control for hybrid systems: Applications and methods for adaptation and optimality

摘要

Hybrid systems have been attracting considerable attention in recent years because of their ability to model complex physical systems with both continuous and discrete dynamic behavior. When controlling such complex systems, a ubiquitous problem is the presence of large parametric uncertainties. Adaptive control has been used in recent years for hybrid systems to cope with parametric uncertainties in classical non-hybrid systems, especially for switched systems. Some results for uncertain switched linear systems have been established, with some performance guarantees in terms of asymptotic stabilization and tracking, for example. In addition, analyzing more general performance measures for hybrid systems is another relevant topic. Despite many theoretical frameworks being developed (hybrid model predictive control, embedding, just to name a few) it is well known that optimal control of hybrid systems is challenging due to the inherently complex dynamics. In view of this, the present issue collects 16 research articles on control and performance optimization of hybrid systems, covering both theoretical research and emerging applications. The first group of articles1-4 focuses on theoretical methodology of control and performance optimization for complex hybrid systems. New stability conditions and control designs based on numerical methods, including linear matrix inequalities and gradient based optimization methods, are introduced to address time delays, parametric uncertainties, and the nonlinearities involved in the switching dynamics with stability and performance guarantees. The second group of articles,5-14 constituting the major category of this issue, applies adaptive control, optimization methods, and hybrid system theories to various practical fields, ranging from UAVs, AGVs, robotics, and water systems to batch processes. The last group15, 16 considers distributed cyber physical systems, which deals with parametric uncertainties, communication constraints, and signal estimations by resorting to adaptive control and fast Kalman filtering. In a nutshell, this special issue provides some fresh insight into the adaptive control and optimization of hybrid systems, which may stimulate future developments from the broad perspectives of both theories and applications.