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. Specifically, 1) to enhance the close connectivity of the two subgraphs, rather than simply connecting two subgraphs by the nodes with the same content (the constructed graph in this way has severe sparsity), we construct a heuristic heterogeneous graph, which relies on claim semantics as heuristic knowledge to guide the connections of the program-table subgraph, and in turn expands the connectivity of the claim-table subgraph through logical information of programs as heuristic knowledge; and 2) to establish adequate association interaction between linguistic evidence and logical evidence, we design multiview reasoning networks. In detail, we propose local-view multihop knowledge reasoning (MKR) networks to enable the current node to establish association not only with one-hop neighbors, but also with multihop neighbors, to capture context-richer evidence information. We execute MKR on heuristic claim-table and program-table subgraphs to learn context-richer linguistic evidence and logical evidence, respectively. Meanwhile, we develop global-view graph dual-attention networks (DAN) that execute on the entire heuristic heterogeneous graph, reinforcing global-level significant consistency evidence. Finally, the consistency fusion layer is devised to weaken the disagreement between the three types of evidence to assist in capturing consistent shared evidence for verifying claims. Experiments on TABFACT and FEVEROUS demonstrate the effectiveness of H2GRN.

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. Hence, we design the practical discontinuous control algorithm for PMSM with input saturation. To achieve the tracking control of PMSM, we define the error variables of the tracking control, and the sliding mode control method is introduced to complete the design of the discontinuous controller. Based on the Lyapunov stability theory, the error variables are guaranteed to converge to zero asymptotically, and the tracking control of the system is realized. Finally, the validity of the proposed control method is verified by a simulation example and the experimental platform.

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. Then, through the combination of adaptive backstepping recursive technology and adding power integration technology, a nonsingular fixed-time adaptive output feedback control algorithm is proposed, which introduces a filter to avoid the complicated derivation process of the virtual control function. According to the fixed-time Lyapunov stability theory, the practical fixed-time stability of the closed-loop system is proven, which means that all signals of the closed-loop system remain bounded in a fixed time under the proposed algorithm. Finally, the effectiveness of the proposed algorithm is verified by the numerical simulation and practical simulation.

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. In addition, based on the separation operation of the error term from the derivative of the value function, we achieve the different proportions of the two agents in the game to realize the regulation of the final equilibrium point. Different from the general use of the neural network for system identification, the unknown nonlinear dynamic term is approximated based on the state difference obtained by the command filter. Furthermore, a sufficient condition is established to guarantee that the whole system and each subsystem included are uniformly ultimately bounded. Finally, simulation results are given to show the effectiveness of the proposed method.

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. Furthermore, focusing on Hamilton-Jacobi-Bellman optimization, it is almost incapable of directly dealing with unknown equations. Above uncertainty and immeasurability processed by adaptive state observer and NN simplified RL are further designed to achieve desired second-order formation configuration at the least cost. The optimization protocol can not only solve the undetectable states and realize the prescribed time-varying formation performance on the premise that all the errors are SGUUB, but also prove the stability and update the critics and actors easily. Through the above-mentioned approaches offer an optimal control scheme to address time-varying formation control. Finally, the validity of the theoretical method is proven by the Lyapunov stability theory and digital simulation.

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. Second, sub-swarm systems formed based on 2-order communication topology (2-OCT) and independently distributed with each other also need to be put forward to reduce computational complexity. At last, the followers in 1-order communication topology (1-OCT) are set as the leaders of sub-swarm systems in 2-OCT. As a result, coupling in large-scale swarm systems would be reduced. The bionic swarm controller is designed through the backstepping method. In this case, the stability of bionic swarm controller is proven by the designed Lyapunov function. The simulations show the efficiency of the designed bionic swarm controller. And the tracking-containment control based on SOCT with 42 swarm members is realized.

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. Besides, the trajectories obtained by the MADDPG are continuous by straight lines, which is not smooth and will be difficult for the swarm robots to track. This article aims to solve these problems by closing the above gaps. First, the ESB-MADDPG method is proposed to improve the training speed. The smooth processing of the trajectory is designed in the ESB-MADDPG. Furthermore, the expert system also provides us with many trained offline trajectories, which avoid the retraining each time we use the swarm robots. Considering the gathered trajectories, the model predictive control (MPC) algorithm is introduced to realize the optimal tracking of the offline trajectories. Simulation results show that combining ESB-MADDPG and MPC can realize swarm robot decision making efficiently.

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. In order to generate different RIPFs in the switching topology, state and transition matrices are introduced. To achieve the minimum agent-movement among RIPFs, a downward-tree combinatorial optimization algorithm is presented. In the end, with the selected minimum agent-movement RIPF, a control law is designed to drive initial RIPF to desired RIPF with given distances among agents. Simulation results show the proposed generation method, control law, and downward-tree are effective to realize the desired formation.

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. The proposed swarm control, in which distances between the related intelligent units are time varying, is different from traditional swarm consensus or swarm formation maintenance. To design swarm control, we define the normalization adjacency matrix and normalization degree matrix based on communication topology. The communication topology is automatically generated based on relation-invariable persistent formation. Depending on whether the communication topology changes or not, the swarm control can be classified as fixed topology and switching topology. Then, the swarm control with fixed and switching topology is designed and analyzed, respectively. The swarm control can realize stability asymptotically when topology is fixed and realize stability in finite time when topology is switched. The simulation results show that the proposed approaches are effective.