Flocking in Multi-Agent Systems with Multiple Virtual Leaders Based Only on Position Measurements

Most existing flocking algorithms assume one single virtual leader and rely on information on both relative positions and relative velocities among neighboring agents.In this paper,the problem of controlling a flock of mobile autonomous agents to follow multiple virtual leaders is investigated by using only position information in the sense that agents with the same virtual leader asymptotically attain the same velocity and track the corresponding virtual leader based on only position measurements.A flocking algorithm is proposed under which every agent asymptotically attains its desired velocity,collision between agents can be avoided,and the final tight formation minimizes all agents' global potentials.A simulation example is presented to verify and illustrate the theoretical results.

The Study of Virtual Enterprise＇s Leader

With the fast changing of environment, virtual enterprises become a kind of new manufacture mode day by day. In the course of establishment and operation of virtual enterprises, the leader＇s status is very important. The study of the leader＇s characteristic, function and how to discern will contribute to realize the leader＇s function and guarantee the healthy and fast development of virtual enterprises.

Flocking for Leader-Following Multi-Agent Systems with Time-Varying Delay

This paper investigates the flocking problem in multi-agent system with time-varying delay and a virtual leader. Each agent here is subject to nonlinear dynamics. For the system, the corresponding algorithm with time-varying delay is proposed. Under the assumption that the initial network is connected, it is proved that the distance between agents is in the desired distance. The theoretical deduction shows that the stable flocking motion is achieved.

Open-loop and closed-loop D^(α)-type iterative learning control for fractional-order linear multi-agent systems with state-delays

This study focuses on implementing consensus tracking using both open-loop and closed-loop Dα-type iterative learning control(ILC)schemes,for fractional-order multi-agent systems(FOMASs)with state-delays.The desired trajectory is constructed by introducing a virtual leader,and the fixed communication topology is considered and only a subset of followers can access the desired trajectory.For each control scheme,one controller is designed for one agent individually.According to the tracking error between the agent and the virtual leader,and the tracking errors between the agent and neighboring agents during the last iteration(for open-loop scheme)or the current running(for closed-loop scheme),each controller continuously corrects the last control law by a combination of communication weights in the topology to obtain the ideal control law.Through the rigorous analysis,sufficient conditions for both control schemes are established to ensure that all agents can achieve the asymptotically consistent output along the iteration axis within a finite-time interval.Sufficient numerical simulation results demonstrate the effectiveness of the control schemes,and provide some meaningful comparison results.

Design of Time-constrained Guidance Laws via Virtual Leader Approach

Guidance problems with flight time constraints are considered in this article. A new virtual leader scheme is used for design of guidance laws with time constraints. The core idea of this scheme is to adopt a virtual leader for real missiles to convert a guidance problem with time constraints to a nonlinear tracking problem,thereby making it possible to settle the problem with a variety of control methods. A novel time-constrained guidance （TCG） law, which can control the flight time of missiles to a prescribed time,is designed by using the virtual leader scheme and stability method. The TCG law is a combination of the well-known proportional navigation guidance（PNG） law and the feedback of flight time error. What＇ s more, this law is free of singularities and hence yields better performances in comparison with optimal guidance laws with time constraints. Nonlinear simulations demonstrate the effectiveness of the proposed law.

Personalized Real-Time Movie Recommendation System:Practical Prototype and Evaluation

With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized movie recommendation schemes utilizing publicly available movie datasets(e.g.,MovieLens and Netflix),and returning improved performance metrics(e.g.,Root-Mean-Square Error(RMSE)).However,two fundamental issues faced by movie recommendation systems are still neglected:first,scalability,and second,practical usage feedback and verification based on real implementation.In particular,Collaborative Filtering(CF)is one of the major prevailing techniques for implementing recommendation systems.However,traditional CF schemes suffer from a time complexity problem,which makes them bad candidates for real-world recommendation systems.In this paper,we address these two issues.Firstly,a simple but high-efficient recommendation algorithm is proposed,which exploits users1 profile attributes to partition them into several clusters.For each cluster,a virtual opinion leader is conceived to represent the whole cluster,such that the dimension of the original useritem matrix can be significantly reduced,then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes,our method can significantly reduce the time complexity,while achieving comparable recommendation performance.Furthermore,we have constructed a real personalized web-based movie recommendation system,MovieWatch,opened it to the public,collected user feedback on recommendations,and evaluated the feasibility and accuracy of our system based on this real-world data.