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In this paper, an off-policy model-free algorithm is presented for solving the cooperative optimal output regulation problem for linear discrete-time multi-agent systems. First, an adaptive distributed observer is designed for each follower to estimate the leader's information. Then, a distributed feedback-feedforward controller is developed for each follower to solve the cooperative optimal output regulation problem utilizing the follower's state information and the adaptive distributed observer. Based on reinforcement learning method, an adaptive algorithm is presented to find the optimal feedback gains via online data collecting from system trajectory. By designing a Sylvester map, the solution to the regulator equations is calculated via data collected from the optimal feedback gain design steps, and the feedforward control gain is found. Finally, an off-policy model-free algorithm is proposed to design the distributed feedback-feedforward controller for each follower to solve the cooperative optimal output regulation problem. A numerical example is given to verify the effectiveness of this proposed approach.

No abstract

In this paper, an off-policy model-free algorithm is presented for solving the cooperative optimal output regulation problem for linear discrete-time multi-agent systems. First, an adaptive distributed observer is designed for each follower to estimate the leader’s information. Then, a distributed feedback-feedforward controller is developed for each follower to solve the cooperative optimal output regulation problem utilizing the follower’s state information and the adaptive distributed observer. Based on the reinforcement learning method, an adaptive algorithm is presented to find the optimal feedback gains via online data collection from system trajectory. By designing a Sylvester map, the solution to the regulator equations is calculated via data collected from the optimal feedback gain design steps, and the feedforward control gain is found. Finally, an off-policy model-free algorithm is proposed to design the distributed feedback-feedforward controller for each follower to solve the cooperative optimal output regulation problem. A numerical example is given to verify the effectiveness of this proposed approach.

The freshness of information is critical for patient vital signs and physiological parameters in the healthcare system because changes in these parameters can indicate a patient's overall health status and guide treatment decisions. In this paper, we consider an edge device-aided smart healthcare system that relies on a resource management scheme. The medical center requires patient information, and edge nodes process the latest measurements received by each wearable device. Our goal is to find the optimal strategy to minimize the worst case of information freshness, i.e., the peak AoI age of information (PAoI). Firstly, we model the problem as a Markov Decision Process (MDP). Then, we design two separate Reinforcement Learning (RL)-based algorithms to find the optimal strategy that minimizes energy consumption and the average PAoI. To minimize energy consumption, we propose a pair of sleep mechanisms, including the N policy and p wake-up policy, to improve the energy efficiency of each wearable device. Simulation results show that the proposed wake-up strategy and the proposed RL algorithm make a better trade-off between the average PAoI and power dissipation compared to the baseline schemes.INDEX TERMS Smart health, age of information (AoI), sleep-scheduling, deep reinforcement learning (DRL), deep deterministic policy gradient (DDPG).