Predefined Time and Prespecified Precision for Bearing-Constrained AAV Swarm.

This article presents a bearing-based formation control method for autonomous aerial vehicle (AAV) swarms, allowing users to specify both convergence time and precision in advance. Unlike traditional distance-based methods, which rely on intricate distance measurements, our approach simplifies constraints using bearing information, reducing hardware and sensing requirements. It also eliminates the need to update control commands for each AAV, as formation reconfiguration can be achieved solely by adjusting the motion trajectory of formation leaders.

Optimal Evolution Strategy for Continuous Strategy Games on Complex Networks via Reinforcement Learning.

This article presents an optimal evolution strategy for continuous strategy games on complex networks via reinforcement learning (RL). In the past, evolutionary game theory usually assumed that agents use the same selection intensity when interacting, ignoring the differences in their learning abilities and learning willingness. Individuals are reluctant to change their strategies too much.

Collision-Free Formation Control for Heterogeneous Multiagent Systems Under DoS Attacks.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP).

Heuristic Heterogeneous Graph Reasoning Networks for Fact Verification.

Existing studies on table-based fact verification generally capture linguistic evidence from claim-table subgraphs or logical evidence from program-table subgraphs independently. However, there is insufficient association interaction between the two types of evidence, which makes it difficult to obtain valuable consistency features between them. In this work, we propose heuristic heterogeneous graph reasoning networks (H2GRN) to capture the shared consistent evidence by strengthening associations between linguistic and logical evidence from two perspectives of graph construction and reasoning mechanism.

Expert System-Based Multiagent Deep Deterministic Policy Gradient for Swarm Robot Decision Making.

In this article, an expert system-based multiagent deep deterministic policy gradient (ESB-MADDPG) is proposed to realize the decision making for swarm robots. Multiagent deep deterministic policy gradient (MADDPG) is a multiagent reinforcement learning algorithm proposed to utilize a centralized critic within the actor-critic learning framework, which can reduce policy gradient variance. However, it is difficult to apply traditional MADDPG to swarm robots directly as it is time consuming during the path planning, rendering it necessary to propose a faster method to gather the trajectories.

Bionic Swarm Control Based on Second-Order Communication Topology.

In this article, we propose bionic swarm control based on second-order communication topology (SOCT) inspired by the migration of birds, which solves the difficulty in constructing communication topologies and high-computational complexity in controlling large-scale swarm systems. To realize bionic swarm control, there are three problems supposed to be solved. First, the adjacency matrix and the Laplacian matrix in traditional methods cannot be applied to SOCT directly, which should be redesigned.

Practical discontinuous tracking control for a permanent magnet synchronous motor.

In this paper, the practical discontinuous control algorithm is used in the tracking controller design for a permanent magnet synchronous motor (PMSM). Although the theory of discontinuous control has been studied intensely, it is seldom applied to the actual systems, which encourages us to spread the discontinuous control algorithm to motor control. Due to the constraints of physical conditions, the input of the system is limited.

Game-Based Backstepping Design for Strict-Feedback Nonlinear Multi-Agent Systems Based on Reinforcement Learning.

In this article, the game-based backstepping control method is proposed for the high-order nonlinear multi-agent system with unknown dynamic and input saturation. Reinforcement learning (RL) is employed to get the saddle point solution of the tracking game between each agent and the reference signal for achieving robust control. Specifically, the approximate optimal solution of the established Hamilton-Jacobi-Isaacs (HJI) equation is obtained by policy iteration for each subsystem, and the single network adaptive critic (SNAC) architecture is used to reduce the computational burden.

Time-Varying Optimal Formation Control for Second-Order Multiagent Systems Based on Neural Network Observer and Reinforcement Learning.

This article addresses a distributed time-varying optimal formation protocol for a class of second-order uncertain nonlinear dynamic multiagent systems (MASs) based on an adaptive neural network (NN) state observer through the backstepping method and simplified reinforcement learning (RL). Each follower agent is subjected to only local information and measurable partial states due to actual sensor limitations. In view of the distributed optimized formation strategic needs, the uncertain nonlinear dynamics and undetectable states may jointly affect the stability of the time-varying cooperative formation control.

Nonsingular Practical Fixed-Time Adaptive Output Feedback Control of MIMO Nonlinear Systems.

This article studies the nonsingular fixed-time control problem of multiple-input multiple-output (MIMO) nonlinear systems with unmeasured states for the first time. A state observer is designed to solve the problem that system states cannot be measured. Due to the existence of the unknown system nonlinear dynamics, neural networks (NNs) are introduced to approximate them.

Swarm Control for Self-Organized System With Fixed and Switching Topology.

In this article, we propose the swarm control for a self-organized system with fixed and switching topology, which can realize aggregation, dispersion, or switching formation when swarm moves. The self-organized system can automatically construct the communication topology for intelligent units in swarm. Swarm control can realize aggregation and dispersion of intelligent units based on its communication topology when swarm moves.

Automatic Leader-Follower Persistent Formation Generation With Minimum Agent-Movement in Various Switching Topologies.

This paper presents the generation strategy, motion planning, and switching topologies of a distance-based leader-follower relation-invariable persistent formation (RIPF) of multiagent systems (MASs). An efficient algorithm is designed to find out if a persistent formation can be generated from a rigid graph. Derived from the properties of a rigid graph, the algorithm to generate RIPF from any initial location is presented.