Wenfeng Hu

Tao Chen, Hongju Wang, Wenfeng Hu

This paper characterizes the triggering behaviors of event-triggered control systems from a geometric-algebraic perspective. We first model the feasibility of inter-event time transition relations as a nonconvex quadratic constraint satisfaction problem and reformulate it as an equivalent linear cone problem, which provides a clearer geometric description of the feasible region, making subsequent analysis more reliable. Building on this formulation, we establish necessary and sufficient conditions that rigorously determine whether a given transition relation is feasible. Based on this condition, we propose an algorithm that computes the set of all feasible transition relations. Numerical simulations further demonstrate how the feasibility of specific transitions evolves with the control parameter σ, with visualizations of the feasible state space offering intuitive insight into parameter selection and system design.

Vincent Huang, Tobias Ley, Martha Vlachou-Konchylaki et al.

Applying deep reinforcement learning (RL) on real systems suffers from slow data sampling. We propose an enhanced generative adversarial network (EGAN) to initialize an RL agent in order to achieve faster learning. The EGAN utilizes the relation between states and actions to enhance the quality of data samples generated by a GAN. Pre-training the agent with the EGAN shows a steeper learning curve with a 20% improvement of training time in the beginning of learning, compared to no pre-training, and an improvement compared to training with GAN by about 5% with smaller variations. For real time systems with sparse and slow data sampling the EGAN could be used to speed up the early phases of the training process.