This paper proposes a novel control method that integrates event-triggered control and self-triggered sampling for switched linear systems. In cyber-physical systems such as smart grids, autonomous driving, and IoT systems, reducing communication load and energy consumption has become increasingly important. Triggered control, which performs state measurements and control updates only, when necessary, has attracted significant attention; however, conventional approaches have been mainly limited to single-mode systems, leaving challenges in their application to more complex systems.

In this study, a unified theoretical framework is developed in which the control input update timing, mode switching, and next sampling time are determined based on the upper bounds of a common Lyapunov function, thereby naturally extending the approach to switched linear systems with multiple modes. A notable original element of this work lies in the systematic design of event-triggering conditions and sampling intervals through the combination of upper-bound evaluations of the Lyapunov function based on both the current state and the initial state.

The proposed method is grounded in a rigorous theoretical development assuming the existence of a common Lyapunov function, and the structure of lemmas and theorems is logically well organized. In particular, the evaluation method for guaranteeing uniform ultimate boundedness in switched systems is carefully derived, and both the underlying assumptions and the range of applicability are clearly specified, highlighting the strong theoretical validity and high level of completeness of the work. Furthermore, numerical examples demonstrate that the proposed method effectively reduces the number of state samplings and control input updates while maintaining control performance.

In summary, this paper presents an outstanding research contribution that combines originality, systematic theoretical development, and potential impact on future research in cyber-physical systems, and is therefore well deserving of the IEICE 2025 Best Paper Award.