Abstract: Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.
Abstract: This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks over an undirected graph. A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements. Meanwhile, this control scheme can also provide more reasonable control signals when DoS attacks occur. To save network resources, an adaptive memory event-triggered mechanism (AMETM) is also proposed and Zeno behavior is excluded. It is worth mentioning that the AMETM’s updates do not require global information. Then, the observer and controller gains are obtained by using the linear matrix inequality (LMI) technique. Finally, simulation examples show the effectiveness of the proposed control scheme.
Abstract: Intersections are quite important and complex traffic scenarios, where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles. Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined, this paper proposes a long short-term memory based (LSTM-based) framework that combines intention prediction and trajectory prediction together. First, we build an intersection prior trajectories model (IPTM) by clustering and statistically analyzing a large number of prior traffic flow trajectories. The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory, and also serves as a reference for credibility evaluation. Second, we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage. Furthermore, the predicted intention is also a key that is associated with the prior trajectories model. The proposed framework is validated on two publically released datasets, next generation simulation (NGSIM) and INTERACTION. Compared with other prediction methods, our framework is able to sample a trajectory from the estimated distribution, with its accuracy improved by about 20%. Finally, the credibility evaluation, which is based on the prior trajectories model, makes the framework more practical in the real-world applications.
Abstract: This paper investigates the heading tracking problem of surface vehicles with unknown model parameters. Based on finite/fixed-time control theories and in the context of command filtered control, two novel adaptive control laws are developed by which the vehicle can track the desired heading within settling time with all signals of the closed-loop system are uniformly bounded. The effectiveness and performance of the schemes are demonstrated by simulations and comparison studies.
Abstract: This paper proposes a control strategy called enclosing control. This strategy can be described as follows: the followers design their control inputs based on the state information of neighbor agents and move to specified positions. The convex hull formed by these followers contains the leaders. We use the single-integrator model to describe the dynamics of the agents and proposes a continuous-time control protocol and a sampled-data based protocol for multi-agent systems with stationary leaders with fixed network topology. Then the state differential equations are analyzed to obtain the parameter requirements for the system to achieve convergence. Moreover, the conditions achieving enclosing control are established for both protocols. A special enclosing control with no leader located on the convex hull boundary under the protocols is studied, which can effectively prevent enclosing control failures caused by errors in the system. Moreover, several simulations are proposed to validate theoretical results and compare the differences between the three control protocols. Finally, experimental results on the multi-robot platform are provided to verify the feasibility of the protocol in the physical system.
Abstract: In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is difficult. To solve the problem, we propose an algorithm called hierarchical deep reinforcement learning with automatic sub-goal identification via computer vision (HADS) which takes advantage of hierarchical reinforcement learning to alleviate the sparse reward problem and improve efficiency of exploration by utilizing a sub-goal mechanism. HADS uses a computer vision method to identify sub-goals automatically for hierarchical deep reinforcement learning. Due to the fact that not all sub-goal points are reachable, a mechanism is proposed to remove unreachable sub-goal points so as to further improve the performance of the algorithm. HADS involves contour recognition to identify sub-goals from the state image where some salient states in the state image may be recognized as sub-goals, while those that are not will be removed based on prior knowledge. Our experiments verified the effect of the algorithm.
Abstract: The existing graph convolution methods usually suffer high computational burdens, large memory requirements, and intractable batch-processing. In this paper, we propose a high-efficient variational gridded graph convolution network (VG-GCN) to encode non-regular graph data, which overcomes all these aforementioned problems. To capture graph topology structures efficiently, in the proposed framework, we propose a hierarchically-coarsened random walk (hcr-walk) by taking advantage of the classic random walk and node/edge encapsulation. The hcr-walk greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version, while preserving graph structures well. To efficiently encode local hcr-walk around one reference node, we project hcr-walk into an ordered space to form image-like grid data, which favors those conventional convolution networks. Instead of the direct 2-D convolution filtering, a variational convolution block (VCB) is designed to model the distribution of the random-sampling hcr-walk inspired by the well-formulated variational inference. We experimentally validate the efficiency and effectiveness of our proposed VG-GCN, which has high computation speed, and the comparable or even better performance when compared with baseline GCNs.
Abstract: In this work, the dynamics of networked goods distribution systems subject to the control of a continuous-review order-up-to inventory policy are investigated. In the analytical study, as opposed to the earlier models constrained to the serial and arborescent interconnection structures, an arbitrary multi-echelon topology is considered. This external, uncertain demand, following any distribution, may be imposed on all network nodes, not just conveniently selected contact points. As in the physical systems, stock relocation to refill the reserves is subject to non-negligible delay, which poses a severe stability threat and may lead to cost-inefficient decisions. A state-space model is created and used as the framework for analyzing system properties. In particular, it is formally demonstrated that despite unpredictable demand fluctuations, a feasible (nonnegative and bounded) reserves replenishment signal is generated at all times, and the stock gathered at the nodes does not surpass a finite, precisely determined level. The theoretical content is illustrated with a case study of the Chinese oil supply system.
Abstract: Formation control of discrete-time linear multi-agent systems using directed switching topology is considered in this work via a reduced-order observer, in which a formation control protocol is proposed under the assumption that each directed communication topology has a directed spanning tree. By utilizing the relative outputs of neighboring agents, a reduced-order observer is designed for each following agent. A multi-step control algorithm is established based on the Lyapunov method and the modified discrete-time algebraic Riccati equation. A sufficient condition is given to ensure that the discrete-time linear multi-agent system can achieve the expected leader-following formation. Finally, numerical examples are provided so as to demonstrate the effectiveness of the obtained results.
Abstract: This paper investigates limited-budget consensus design and analysis problems of general high-order multiagent systems with intermittent communications and switching topologies. The main contribution of this paper is that the trade-off design between the energy consumption and the consensus performance can be realized while achieving leaderless or leader-following consensus, under constraints of limited budgets and intermittent communications. Firstly, a new intermittent limited-budget consensus control protocol with a practical trade-off design index is proposed, where the total budget of the whole multiagent system is limited. Then, leaderless limited-budget consensus design and analysis criteria are derived, in which the matrix variables of linear matrix inequalities are determined according to the total budget and the practical trade-off design parameters. Meanwhile, an explicit formulation of the consensus function is derived to describe the consensus state trajectory of the whole system. Moreover, a new two-stage transformation strategy is utilized for leader-following cases, by which the dynamics decomposition of leaderless and leader-following cases can be converted into a unified framework, and sufficient conditions of the leader-following limited-budget consensus design and analysis are determined via those of the leaderless cases. Finally, numerical simulations are given to illustrate theoretical results.
IEEE/CAA Journal of Automatica Sinica
JCR Impact Factor: 11.8, Top 4% (SCI Q1)
CiteScore: 17.6, Top 3% (Q1) Google Scholar h5-index: 77， TOP 5