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Adaptive Predefined-Time Safety Learning Control for Switched Multi-Agent Systems: An Advanced Encryption Self-Triggered Algorithm
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AI
DOI：10.1109/TASE.2025.3615563.png)
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Abstract
En 中文
This study develops an advanced self-triggered predefined-time safety learning control algorithm for switched multi-agent systems with full-state mask. To strengthen encryption while reducing the impact to system performance, an improved settling time privacy preservation mechanism based on the full-state mask function is designed, which encrypts the true information of the system and enhances the privacy of information delivery. Unlike traditional learning control schemes, a novel actor-critic weight update law is designed to guarantee that the system energy cost is minimized resulting in predefined time optimization. Besides, an improved self-triggered condition with a compensation term is developed to overcome the complex challenges posed by full-state privacy preservation mechanism. It not only eliminates the necessity to continually monitor the triggered state of the system but also saves communication resources. Finally, the validity of the designed control scheme can be proven by a simulation experiment. Note to Practitioners—This paper is motivated by designing a self-triggered predefined-time optimal control method for switched multi-agent systems with full-state mask. As artificial intelligence continues to develop, information security and resource waste have become key problems in practical applications, such as smart grids and autonomous vehicles. In this study, we develop privacy preservation mechanism and optimal control strategy that minimize energy cost to overcome the above challenges. It is worth noting that the UAV swarm switches between different modes to adapt to different environments during the mission. With the help of a self-triggered algorithm, the photovoltaic inverter calculates the next control moment in advance to avoid continuous sampling during sudden changes in light thereby saving communication resources. Additionally, the predefined time control method is characterized by its fast and accurate convergence, which makes it widely used in fields such as missile guidance and rehabilitation robots.
Keywords:
Optimal control
switched multi-agent systems
privacy preservation
self-triggered control
full-state mask
predefined-time control
reinforcement learning
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