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Vol. 1,  No. 1, 2014

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2014, 1(1): .
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SPECIAL ISSUE PAPERS
An Overview of Distributed High-order Multi-agent Coordination
Jie Huang, Hao Fang, Lihua Dou, Jie Chen
2014, 1(1): 1-9.
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Research on multi-agent systems has attracted much attention in the past two decades, and numerous results have been obtained. Its widespread applications include spacecraft, mobile robots, sensor networks, etc. Most previous research works on multi-agent systems studied the first- and second-order dynamics. However, in engineering, many systems are modeled by higher-order dynamics. This paper reviews the major results and progress in distributed high-order multi-agent coordination. After the review, a short discussion section is included to summarize the existing research and propose several promising research directions along with some open problems that are deemed important for further investigations.
On Dynamics and Nash Equilibriums of Networked Games
Daizhan Cheng, Tingting Xu, Fenghua He, Hongsheng Qi
2014, 1(1): 10-18.
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Networked noncooperative games are investigated, where each player (or agent) plays with all other players in its neighborhood. Assume the evolution is based on the fact that each player uses its neighbors' current information to decide its next strategy. By using sub-neighborhood, the dynamics of the evolution is obtained. Then a method for calculating Nash equilibriums from mixed strategies of multi-players is proposed. The relationship between local Nash equilibriums based on individual neighborhoods and global Nash equilibriums of overall network is revealed. Then a technique is proposed to construct Nash equilibriums of an evolutionary game from its one step static Nash equilibriums. The basic tool of this approach is the semi-tensor product of matrices, which converts strategies into logical matrices and payoffs into pseudo-Boolean functions, then networked evolutionary games become discrete time dynamic systems.
Leader-following Rendezvous with Connectivity Preservation of Single-integrator Multi-agent Systems
Yi Dong, Jie Huang
2014, 1(1): 19-23.
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This paper studies the problem of leader-following rendezvous with connectivity preservation for a linear multiagent system where the leader system is a linear autonomous system and the follower system is a multiple single-integrator system. We develop a distributed state feedback control protocol to maintain the connectivity of the system and, at the same time, to achieve asymptotic tracking of all followers to the output of the leader system.
Optimal Deployment of Mobile Sensors for Target Tracking in 2D and 3D Spaces
Shiyu Zhao, Ben M Chen, Tong H Lee
2014, 1(1): 24-30.
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This paper proposes a control strategy to autonomously deploy optimal placements of range-only mobile sensors in 2D and 3D spaces. Based on artificial potential approaches, the control strategy is designed to minimize the intersensor and external potentials. The inter-sensor potential is the objective function for optimal sensor placements. A placement is optimal when the inter-sensor potential is minimized. The external potential is introduced to fulfill constraints on sensor trajectories. Since artificial potential approaches can handle various issues such as obstacle avoidance and collision avoidance among sensors, the proposed control strategy provides a flexible solution to practical autonomous optimal sensor deployment. The control strategy is applied to several optimal sensor deployment problems in 2D and 3D spaces. Simulation results illustrate how the proposed control strategy can improve target tracking performance.
Road Pricing Design Based on Game Theory and Multi-agent Consensus
Nan Xiao, Xuehe Wang, Lihua Xie, Tichakorn Wongpiromsarn, Emilio Frazzoli, Daniela Rus
2014, 1(1): 31-39.
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Consensus theory and noncooperative game theory respectively deal with cooperative and noncooperative interactions among multiple players/agents. They provide a natural framework for road pricing design, since each motorist may myopically optimize his or her own utility as a function of road price and collectively communicate with his or her friends and neighbors on traffic situation at the same time. This paper considers the road pricing design by using game theory and consensus theory. For the case where a system supervisor broadcasts information on the overall system to each agent, we present a variant of standard fictitious play called average strategy fictitious play (ASFP) for large-scale repeated congestion games. Only a weighted running average of all other players' actions is assumed to be available to each player. The ASFP reduces the burden of both information gathering and information processing for each player. Compared to the joint strategy fictitious play (JSFP) studied in the literature, the updating process of utility functions for each player is avoided. We prove that there exists at least one pure strategy Nash equilibrium for the congestion game under investigation, and the players' actions generated by the ASFP with inertia (players' reluctance to change their previous actions) converge to a Nash equilibrium almost surely. For the case without broadcasting, a consensus protocol is introduced for individual agents to estimate the percentage of players choosing each resource, and the convergence property of players' action profile is still ensured. The results are applied to road pricing design to achieve socially local optimal trip timing. Simulation results are provided based on the real traffic data for the Singapore case study.
Distributed Self-triggered Control for Consensus of Multi-agent Systems
Hao Zhang, Gang Feng, Huaicheng Yan, Qijun Chen
2014, 1(1): 40-45.
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This paper studies the consensus problem of general linear multi-agent systems via self-triggered control. Two distributed self-triggered control schemes based on state feedback and output feedback are developed respectively. It is shown that under the proposed control protocols, consensus can be reached if the communication graph of the multi-agent system is connected. An example is presented to illustrate the effectiveness of the proposed control methods.
Distributed Control of Nonlinear Uncertain Systems: A Cyclic-small-gain Approach
Tengfei Liu, Zhongping Jiang
2014, 1(1): 46-53.
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This paper presents a cyclic-small-gain approach to distributed control of nonlinear multi-agent systems for output agreement. Through a novel nonlinear control law design, the output agreement problem is transformed into a stabilization problem, and the closed-loop multi-agent system is transformed into a large-scale system composed of input-to-state stability (ISS) subsystems which are interconnected with each other through redefined outputs. By forcing the redefined outputs to go to arbitrarily small neighborhoods of the origin, practical consensus is achieved for the agents in the sense that their outputs ultimately converge to each other within an arbitrarily small region. A recently developed cyclic-small-gain result is adopted to assign appropriately the ISS gains to the transformed interconnected system. Moreover, if the system is disturbancefree, then consensus can be guaranteed. Interestingly, the closedloop multi-agent system is also robust to bounded time-delays and disturbances in information exchange.
Experimental Verification of a Multi-robot Distributed Control Algorithm with Containment and Group Dispersion Behaviors: the Case of Dynamic Leaders
Hejin Zhang, Zhiyun Zhao, Ziyang Meng, Zongli Lin
2014, 1(1): 54-60.
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This paper studies the containment and group dispersion control for a multi-robot system in the presence of dynamic leaders. Each robot is represented by a doubleintegrator dynamic model and a distributed control algorithm is developed to drive the multi-robot system to follow a group of dynamic leaders with containment and group dispersion behaviors. The effectiveness of the algorithm is then verified on a multi-robot control platform.
An Approximate Gradient Algorithm for Constrained Distributed Convex Optimization
Yanqiong Zhang, Youcheng Lou, Yiguang Hong
2014, 1(1): 61-67.
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In this paper, we propose an approximate gradient algorithm for the multi-agent convex optimization problem with constraints. The agents cooperatively compute the minimum of the sum of the local objective functions which are subject to a global inequality constraint and a global constraint set. Instead of each agent can get exact gradient, as discussed in the literature, we only use approximate gradient with some computation or measurement errors. The gradient accuracy conditions are presented to ensure the convergence of the approximate gradient algorithm. Finally, simulation results demonstrate good performance of the approximate algorithm.
Cooperative Localization of AUVs Using Moving Horizon Estimation
Sen Wang, Ling Chen, Dongbing Gu, Huosheng Hu
2014, 1(1): 68-76.
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This paper studies the localization problem of autonomous underwater vehicles (AUVs) constrained by limited size, power and payload. Such AUVs cannot be equipped with heavy sensors which makes their underwater localization problem difficult. The proposed cooperative localization algorithm is performed by using a single surface mobile beacon which provides range measurement to bound the localization error. The main contribution of this paper is twofold: 1) The observability of single beacon based localization is first analyzed in the context of nonlinear discrete time system, deriving a sufficient condition on observability. It is further compared with observability of linearized system to verify that a nonlinear state estimation is necessary. 2) Moving horizon estimation is integrated with extended Kalman filter (EKF) for three dimensional localization using single beacon, which can alleviate the computational complexity, impose various constraints and make use of several previous range measurements for each estimation. The observability and improved localization accuracy of the localization algorithm are verified by extensive numerical simulation compared with EKF.
Adaptive Neural Region Tracking Control of Multi-fully Actuated Ocean Surface Vessels
Xiaoming Sun, Shuzhi Sam Ge
2014, 1(1): 77-83.
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In this paper, adaptive neural network region tracking control is designed to force a group of fully actuated ocean vessels with limited sensing range to track a common moving target region, in the presence of uncertainties and unknown disturbances. In this control concept, the desired objective is specified as a moving region instead of a stationary point, region or a path. The controllers guarantee the connectivity preservation of the dynamic interaction network, and no collisions happen between any ocean vessels in the group. The tracking control design is based on the artificial potential functions, approximation-based backstepping design technique, and Lyapunov's method. It is proved that under the adaptive neural network control law, the tracking error of each ocean vessel converges to an adjustable neighborhood of the origin, although some of them do not access the desired target region directly. Simulation results are presented to illustrate the performance of the proposed approach.
Tracking Control of Leader-follower Multi-agent Systems Subject to Actuator Saturation
Airong Wei, Xiaoming Hu, Yuzhen Wang
2014, 1(1): 84-91.
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This paper addresses the tracking problem of leaderfollower multi-agent systems subject to actuator saturation. A leader node or command generator is considered, which generates the desired tracking trajectory. To track such a leader, a new family of scheduled low-and-high-gain feedback controllers is designed for each follower, provided that the linear dynamic mode is asymptotically null controllable with bounded controls, and such control laws rely on the asymptotic property of a class of parametric algebraic Ricatti equations. We show that if the associated undirected graph of the system is connected, with the proposed control law, all the followers can track the leader eventually. A simulation example is finally given to illustrate the performance of the proposed control scheme.
Decentralised Formation Control and Stability Analysis for Multi-vehicle Cooperative Manoeuvre
Aolei Yang, Wasif Naeem, Minrui Fei
2014, 1(1): 92-100.
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Multi-vehicle cooperative formation control problem is an important and typical topic of research on multi-agent system. This paper presents a formation stability conjecture to conceive a new methodology for solving the decentralised multivehicle formation control problem. It employs the "extensiondecomposition-aggregation" scheme to transform the complex multi-agent control problem into a group of sub-problems which is able to be solved conveniently. Based on this methodology, it is proved that if all the individual augmented subsystems can be stabilised by using any approach, the overall formation system is not only asymptotically but also exponentially stable in the sense of Lyapunov within a neighbourhood of the desired formation. Simulation study on 6-DOF aerial vehicles (Aerosonde UAVs) has been performed to verify the achieved formation stability result. The proposed multi-vehicle formation control strategy can be conveniently extended to other cooperative control problems of multi-agent systems.
Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings
Hongbin Ma, Yini Lv, Chenguang Yang, Mengyin Fu
2014, 1(1): 101-112.
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In this paper, the problem of decentralized adaptive filtering for multi-agent systems with uncertain couplings is formulated and investigated. This problem is challenging due to the mutual dependency of state estimation and coupling estimation. First, the problem is divided into four typical types based on the origin of coupling relations and linearity of the agent dynamics. Then models of the four types are given and the corresponding decentralized adaptive filtering algorithms are designed for the purpose of estimation of the unknown states and couplings which denotes the relations between agents and their neighbor agents in terms of states or outputs simultaneously, with preliminary stability analysis and discussions. For testing the effects of algorithm, with the so-called certainty-equivalence principle, control signals are designed based on the results of state estimation and coupling estimation got by the proposed decentralized adaptive filtering algorithms. Extensive simulations are conducted to verify the effectiveness of considered algorithms.