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.

近年来，由于混合系统能够对具有连续和离散动态行为的复杂物理系统进行建模，因此引起了广泛的关注。当控制这样的复杂系统时，普遍存在的问题是存在较大的参数不确定性。近年来，自适应控制已用于混合系统，以应对经典非混合系统中的参数不确定性，特别是对于开关系统。已经建立了不确定线性切换系统的一些结果，例如在渐近稳定和跟踪方面具有一些性能保证。此外，分析混合系统的更多常规性能指标是另一个相关主题。尽管正在开发许多理论框架（混合模型预测控制，嵌入，

有鉴于此，本期收集了16篇关于混合系统的控制和性能优化的研究文章，涵盖了理论研究和新兴应用。第一组文章1-4专注于复杂混合系统的控制和性能优化的理论方法。引入了新的基于数值方法的稳定性条件和控制设计，包括基于线性矩阵不等式和基于梯度的优化方法，以解决具有稳定性和性能保证的开关动力学中的时间延迟，参数不确定性和非线性。第二组文章5-14构成这一问题的主要类别，将自适应控制，优化方法和混合系统理论应用于各个实际领域，从无人飞行器，自动导引车，机器人技术和供水系统到分批处理。最后一组15、16考虑分布式网络物理系统，该系统通过采用自适应控制和快速卡尔曼滤波来处理参数不确定性，通信约束和信号估计。

简而言之，本期专刊为混合动力系统的自适应控制和优化提供了一些新鲜的见识，从理论和应用的广泛角度来看，这可能会刺激未来的发展。