Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion

This article explores deep reinforcement learning (DRL) for the flocking control of unmanned aerial vehicle (UAV) swarms. The flocking control policy is trained using a centralized-learning-decentralized-execution (CTDE) paradigm, where a centralized critic network augmented with additional information about the entire UAV swarm is utilized...

DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention

Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient...

Spiking Neural-Networks-Based Data-Driven Control

Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering because SNNs can potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware is being...

Deep reinforcement learning control approach to mitigating actuator attacks

This paper investigates the deep reinforcement learning based secure control problem for cyber–physical systems (CPS) under false data injection attacks. We describe the CPS under attacks as a Markov decision process (MDP), based on which the secure controller design for CPS under attacks is formulated as an action policy learning using data....

A Secure Robot Learning Framework for Cyber Attack Scheduling and Countermeasure

The problem of learning-based control for robots has been extensively studied, whereas the security issue under malicious adversaries has not been paid much attention to. Malicious adversaries can invade intelligent devices and communication networks used in robots, causing incidents, achieving illegal objectives, and even injuring people....

Barrier Function-based Safe Reinforcement Learning for Formation Control of Mobile Robots

Distributed model predictive control (DMPC) concerns how to online control multiple robotic systems with constraints effectively. However, the nonlinearity, nonconvexity, and strong interconnections of dynamic system models and constraints can make the real-time and real-world DMPC implementations nontrivial. Reinforcement learning (RL)...

Learning-Based Multi-Robot Formation Control With Obstacle Avoidance

Multi-robot formation control has been intensively studied in recent years. In practical applications, the multi-robot system's ability to independently change the formation to avoid collision among the robots or with obstacles is critical. In this study, a multi-robot adaptive formation control framework based on deep reinforcement learning...

Secure Control for Cyber-Physical Systems under Malicious Attacks

This article investigates the secure control problem for cyber-physical systems when the malicious data are injected into the cyber realm, which directly connects to the actuators. Based on moving target defense (MTD) and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system (A,B) is...

Learning Tracking Control for Cyber-Physical Systems

This paper investigates the problem of optimal tracking control for cyber-physical systems (CPS) when the cyber realm is attacked by denial-of-service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and...

Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines a conventional control method with reinforcement learning to enhance control accuracy and intelligence. In the proposed...

Reinforcement learning control of constrained dynamic systems with uniformly ultimate boundedness stability guarantee

Reinforcement learning (RL) is promising for complicated stochastic nonlinear control problems. Without using a mathematical model, an optimal controller can be learned from data evaluated by certain performance criteria through trial-and-error. However, the data-based learning approach is notorious for not guaranteeing stability, which is...