Frank L. Lewis

This work studies model-free H_∞ policy design for a target tracking system with network-induced dropout and unknown system dynamics. By integrating reinforcement learning, robust output regulation theory, and state reconstruction, we address the target tracking problem while ensuring resilience to network-induced dropout. We show that this problem can be decomposed into two learning-based tasks: predicting the system state under packet dropout in the sensor/controller transmission channels, and designing an off-policy H_∞ control law to reduce the impact of disturbances on system performance. On this basis, we develop a state-reconstruction technique that integrates historical system data into a Smith predictor to estimate the tracking-system state and improve resilience to network-induced dropout. Furthermore, we propose a robust output-regulation design with H_∞ performance to control the target tracker effectively, even without prior knowledge of the system dynamics. Through extensive numerical simulations and hardware-in-the-loop (HIL) experiments, we validate the effectiveness of the proposed algorithms for robust target tracking under network dropout.

This paper focuses on the Nash equilibrium problem for non-cooperative games over general directed graphs with application to energy allocation. Unlike previous Nash equilibrium-seeking works that typically required strongly connected or undirected graphs, we now relax the network to contain a spanning tree. To overcome the inaccessibility caused by the general graph, we introduce an information collector who gathers player profiles and acts as the leader. Under this framework, each player preserves the state observation from leader through the spanning-tree network, and uses gradient descent to adjust variables for converging to an equilibrium point. We also present an enhanced version that invests the solo coupling gain with adaptation through distributed consensus error. The proposed schemes ensure the globally asymptotic stability of the Nash equilibrium-seeking process through Lyapunov analysis. Finally, numerical studies, as well as hardware-in-the-loop experiments for energy allocation on the RT-Lab platform, are provided to demonstrate the effectiveness of the proposed protocols.

This article investigates a defense strategy for the pursuit team under partial information, to minimize the number of evaders reaching the target region. The differential game characterized by players having heterogeneous speeds and sensing radii is decomposed into a collection of subgames, and the defense strategy is divided into a deployment strategy and a task assignment strategy. The deployment strategy is proposed by solving the constrained optimization problem associated with the barrier. The multiplayer task assignment is formulated as a maximum weight bipartite matching problem, and addressed by the sequential constrained integer programming. The effectiveness of the defense strategy is demonstrated through theoretical analysis and experimental validation.

Attaining both fast synchronization with high precision and operational stability in multimachine power systems remains a mammoth technical challenge. This article investigates complexity dynamics and fuzzy optimal prescribed-time control of a network of bidirectionally coupled fractional-order (FO) permanent magnet synchronous generator (PMSG). First, a mathematical model of a large network of bidirectionally coupled FO PMSG with few neighboring node connections and coupling strength is established based on the topological layout of a wind farm. Complexity dynamics reveal that this network can exhibit chimera and global synchronization states under varying conditions, including different FOs and coupling strengths. To achieve global synchronization, preset tracking precision within a prescribed-time, security constraint, and operational flexibility in this safety-critical network, a fuzzy optimal prescribed-time control strategy comprising a feedforward neural prescribed-time controller and a feedback safe optimal controller is proposed, using a type-3 fuzzy neural network (FNN) to address system uncertainty. Furthermore, we develop a hardware-in-the-loop simulation platform based on the STM32F767IGT6 microcontroller, featuring: 12-bit digital to analog converter (DAC) for waveform generation to oscilloscope, direct memory access (DMA)-controlled data transmission bypassing CPU intervention, and timer-configured overflow and event triggering mechanisms. Finally, extensive results further validate the feasibility and effectiveness of our scheme.

This article addresses the existing reinforcement learning (RL) control issues of continuous-time Markov jump systems (MJSs), including their synchronous iteration structure using nonlatest updates, nonmonotonic convergence, and requirement of initial admissible control and all-time persistent excitation (PE) condition. We propose advanced model-based and model-free RL algorithms that: 1) have asynchronous decoupled Lyapunov iteration equations to approximate the optimal control solutions using the latest updates; 2) determine the initial admissible control policy (IACP) and initial value function matrix without relying on engineering experiences; and 3) relax PE condition with a milder initial excitation (IE) condition. Rigorous theoretical analyses are provided to establish the monotonic convergence to the optimal control solution and the closed-loop stability at each iteration. Finally, the simulation and comparison results verify the proposed algorithms.