Distributed output feedback stationary consensus of multi-vehicle systems in unknown environments

In the traditional distributed consensus of multi-vehicle systems, vehicles agree on velocity and position using limited information exchange in their local neighborhoods. Recently, distributed leaderless stationary consensus has been proposed in which vehicles agree on a position and come to a stop. The proposed stationary consensus schemes are based on all vehicles'' access to their own absolute velocity measurements, and they do not guarantee this collective behavior in the presence of disturbances that persistently excite vehicles'' dynamics. On the other hand, traditional distributed disturbance rejection leaderless consensus algorithms may result in an uncontrolled increase in the speed of multi-vehicle system. In this paper, we propose a dynamic relative-output feedback leaderless stationary algorithm in which only a few vehicles have access to their absolute measurements. We systematically design the distributed algorithm by transforming this problem into a static feedback robust control design challenge for the low-order modified model of vehicles with fictitious modeling uncertainties. We further propose dynamic leader-follower stationary consensus algorithms for multi-vehicle systems with a static leader, and find closed-form solutions for the consensus gains based on design matrices and communication graph topology. Finally, we verify the feasibility of these ideas through simulation studies.     

Bipartite consensus of multi‐agent systems over signed graphs: State feedback and output feedback control approaches

This paper studies bipartite consensus problems for continuous‐time multi‐agent system over signed directed graphs. We consider general linear agents and design both state feedback and dynamic output feedback control laws for the agents to achieve bipartite consensus. Via establishing an equivalence between bipartite consensus problems and the conventional consensus problems under both state feedback and output feedback control approaches, we make direct application of existing state feedback and output feedback consensus algorithms to solve bipartite consensus problems. Moreover, we propose a systematical approach to design bipartite consensus control laws. Copyright © 2016 John Wiley & Sons, Ltd.     

Iterative learning control of multi-agent systems with random noises and measurement range limitations

In this paper, the iterative learning control is introduced to solve the consensus tracking problem of a multi-agent system with random noises and measurement range limitation. A distributed learning control algorithm is proposed for all agents by utilising its nearest neighbour measured information from previous iterations. With the help of the stochastic approximation technique, we first establish the consensus convergence of the input sequences in almost sure sense for fixed topology as the iteration number increases. Then, we extend the results to switching topologies case which is dynamically changing along the time axis. Illustrative simulations verify the effectiveness of the proposed algorithms.     

Finite-time consensus algorithm for multi-agent systems with double-integrator dynamics

In this paper, we discuss the finite-time consensus problem for leaderless and leader–follower multi-agent systems with external disturbances. Based on the finite-time control technique, continuous distributed control algorithms are designed for these agents described by double integrators. Firstly, for the leaderless multi-agent systems, it is shown that the states of all agents can reach a consensus in finite time in the absence of disturbances. In the presence of disturbances, the steady-state errors of any two agents can reach a region in finite time. Secondly, for the leader–follower multi-agent systems, finite-time consensus algorithms are also designed based on distributed finite-time observers. Rigorous proof is given by using Lyapunov theory and graph theory. Finally, one example is employed to verify the efficiency of the proposed method.     

Optimal control of distributed multiagent systems with finite-time group flocking

The flocking of multiple intelligent agents, inspired by the swarm behavior of natural phenomena, has been widely used in the engineering fields such as in unmanned aerial vehicle (UAV) and robots system. However, the performance of the system (such as response time, network throughput, and resource utilization) may be greatly affected while the intelligent agents are engaged in cooperative work. Therefore, it is concerned to accomplish the distributed cooperation while ensuring the optimal performance of the intelligent system. In this paper, we investigated the optimal control problem of distributed multiagent systems (MASs) with finite-time group flocking movement. Specifically, we propose two optimal group flocking algorithms of MASs with single-integrator model and double-integrator model. Then, we study the group consensus of distributed MASs by using modern control theory and finite-time convergence theory, where the proposed optimal control algorithms can drive MASs to achieve the group convergence in finite-time while minimizing the performance index of the intelligence system. Finally, experimental simulation shows that MASs can keep the minimum energy function under the effect of optimal control algorithm, while the intelligent agents can follow the optimal trajectory to achieve group flocking in finite time.     

Bearing-based formation stabilization using event-triggered control

This article studies two distributed bearing-based event-triggered schemes to achieve formation stabilization. We focus on systems with double-integrator dynamics with bearings sensing capabilities. Firstly, we propose a bearing-only event-triggered condition (ETC) that is edge-dependent which drives the control updates of the agents using only information dependent on relative sensed quantities. Secondly, along with bearing measurements we make use of local agent state measurements to arrive at an ETC that uses this collective measurement to drive the sensing and control updates of an agent. In doing so, we propose a new control law that renders the final formation stationary. Simulations are provided to verify the validity of the proposed algorithms.     

LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.     

区块链共识算法研究综述

共识算法是区块链系统维护数据一致性的核心机制.本文深入调研并分析了具有代表性的共识算法及其演化历程;基于共识过程提出共识算法的分类模型,并对各类型中代表性的共识算法进行详细分析;最后从去中心化、可扩展性、安全性、一致性、可用性、分区容忍性六个方面建立了一套共识算法的评价指标体系,并对代表性的共识算法进行对比分析,给出各...     

Leader-following consensus of single-integrator multi-agent systems under noisy and delayed communication

This paper proposes a leader-following consensus control for continuous-time single-integrator multi-agent systems with multiplicative measurement noises and time-delays under directed fixed topologies. Each agent in the team receives imprecise information states corrupted by noises from its neighbours and from the leader; these noises are depending on the agents’ relative states information. Moreover, the information states received are also delayed by constant or time-varying delays. An analysis framework based on graph theory and stochastic tools is followed to derive conditions under which the tracking consensus of a constant reference is achieved in mean square. The effectiveness of the proposed algorithms is proved through some simulation examples.     

Backstepping-based synchronisation of uncertain networked Lagrangian systems

In this article, we study the synchronisation problem of uncertain networked Lagrangian systems on directed communication topologies. For the nominal model without uncertainties, we propose a backstepping-based synchronisation design for heterogenous Lagrangian systems on directed graphs with a spanning tree. We relax earlier constraints on the feedback gain for the distributed synchronisation control law, which encompasses the existing double integrator consensus problem as a special case. We then extend the proposed design to the case without relative velocity measurement. For the uncertain Lagrangian model, we develop a distributed adaptive redesign so that asymptotic synchronisation convergence is achieved in the presence of linearly parameterised model uncertainties. Simulation results show the effectiveness of the proposed method.     

Average Consensus for Multi‐Agent System with Measurement Noise and Binary‐Valued Communication

This paper considers the average consensus problems of a class of multi‐agent systems (MAS) with binary‐valued communication. Each agent can only obtain its neighbor's binary‐valued information under measurement noise because of limited bandwidth in communication channels. To seek consensus, we propose a two‐scale multi‐agent consensus algorithm with distributed strategy by combining state estimation and control design alternately. An exponential step size is chosen in the state estimation process and the estimation method can be proved to be asymptotically efficient. Additionally, by utilizing a distributed control law designed based on the estimates of the neighbors' states with a constant gain, we further prove that the proposed average consensus algorithm is convergent. Furthermore, the proposed average consensus algorithm is given and proved. Finally, some simulation results, which illustrate the effectiveness of the obtained results, are also given in the paper.     

Event‐Based Consensus Controller for Linear Multi‐Agent Systems Over Directed Communication Topologies: A Co‐Design Approach

The paper addresses the distributed event‐triggered consensus problem in directed topologies for multi‐agent systems (MAS) with general linear dynamic agents. A co‐design approach is proposed to determine parameters of the consensus controller and its event‐triggered mechanism (ETM), simultaneously. This approach guarantees asymptotic stability along with decreasing data transmission among agents. In the proposed event‐based consensus controller, each agent broadcasts data to the neighbors only at its own triggering instants; this differs from previous studies in which continuous data streams among agents were required. Furthermore, the proposed control law is based on the piecewise constant functions of the measurement values, which are updated at triggering instants. In this case the control scheme decreases the communication network usage, energy consumption, and wear of the actuator. As a result, it facilitates distributed implementation of the proposed consensus controller for real‐world applications. A theorem is proved to outline sufficient conditions to guarantee the asymptotic stability of the closed‐loop system with the event‐based consensus controller. Another theorem is also proved to show the Zeno behavior exclusion. As a case study, the proposed event‐based controller is applied for a diving consensus problem to illustrate the effectiveness of the method.     

动态非线性智能体网络的渐近一致协议

提出了由一般非线性方程描述的多个智能体组成的网络达成渐近一致的概念,以及多动态智能体网络的渐近一致协议问题,介绍了表征网络信息流的图论知识,讨论了在一定的网络信息流拓扑结构情况下,使用非线性控制理论得出的多动态智能体网络最终达成渐近一致的协议的形式、计算方法及其存在条件。运用该一致协议对多无人机系统进行编队控制,其仿真结果表明了该一致协议的应用领域和有效性。     

基于事件触发的二阶多智能体系统平均一致性

针对二阶多智能体系统在固定无向拓扑下的平均一致性问题,为减少不必要的资源浪费,给出一种基于事件触发控制的一致性算法．首先,针对每个智能体设计基于状态误差形式的触发函数,只有当状态误差达到特定值时智能体才触发事件,在两个相邻事件触发时刻之间保持控制输入不变．然后,利用模型转换思想将系统的一致性问题转化为稳定性问题,并利用矩阵理论和李亚普诺夫理论给出系统达到平均一致的充分条件．最后,通过仿真实验验证该理论方案的有效性.     

Model and robust gain‐scheduled PID control of a bio‐inspired morphing UAV based on LPV method

In this paper, a linear parameter‐varying (LPV)‐based model and robust gain‐scheduled structural proportion integral and derivative (PID) control design solution are proposed and applied on a bio‐inspired morphing wing unmanned aerial vehicle (UAV) for the morphing process. In the LPV model method, the authors propose an improved modeling method for LPV systems. The method combines partial linearization and function substitution. Using the proposed method, we can choose the varying parameters simply, thus creating a model that is more flexible and applicable. Then, a robust gain‐scheduled structural PID control design method is given by introducing a structural matrix to design a structural PID controller, which is more consistent with the structure of the PID controller used in practice and has a simpler structure than representative ones in the existing literature. The simulation results show that the developed LPV morphing UAV model is able to catch the response of the original nonlinear model with a smaller error than the existing Jacobian linearization method and the designed controller can maintain stable flights in practice with satisfactory robustness and performance.     

Decentralized adaptive consensus control for discrete‐time heterogeneous semiparametric multiagent systems

Different from the consensus control of traditional multiagent systems, this paper studies the decentralized adaptive consensus control for discrete‐time heterogeneous hidden leader‐following semiparametric multiagent system, in which the dynamic equation of each agent has both parametric uncertainties and nonparametric uncertainties. In the considered system, there is a hidden leader agent who can receive the reference signal, but it can only affect the states of those agents who are in its neighborhood. For other following agents, they do not know the leader's existence or the reference signal, and they can only receive information from their neighbors. Our goal is to design decentralized adaptive controllers to make sure that all agents can track the reference signal, and the closed‐loop system achieves consensus in the presence of mutual coupling relations. Due to the existence of both parametric and nonparametric uncertainties in the system, we need to estimate them separately. For the parametric part, we propose a novel dead zone with threshold converging to zero to modify the traditional gradient update law, while for the nonparametric part, we introduce an auxiliary variable including both two uncertainties to facilitate the nonparametric uncertainties compensation. Based on the certainty equivalence principle in adaptive control theory, the decentralized adaptive controller is designed for each agent to make sure that all of them can track the reference signal. Finally, under the proposed control protocol, strict mathematical proofs are given by using Lyapunov theory; then, simulation results are provided to demonstrate the effectiveness of proposed decentralized adaptive controllers.     

Target-enclosing control for second-order multi-agent systems

This paper poses the enclosing control problem with identical geometry for a group of targets which are either stationary or moving and offers consensus-based distributed control protocols. An estimator is first introduced to estimate the central position of the targets. We then propose a target-enclosing control law with velocity information based on the centre estimating algorithm and consensus theory. A target-enclosing control law without the velocity information is further designed. The Lyapunov theory and Lasalle’s invariance principle are applied to show the convergence of the proposed control algorithms. Finally, numerical simulations are given to illustrate the effectiveness of our proposed strategy.     

平面四旋翼无人飞行器运送系统的轨迹规划与跟踪控制器设计

对于四旋翼无人飞行器运送系统而言,需要保证飞行过程中负载的摆幅维持在适当的范围内,并且在飞行器到达目的地后负载无残余摆动.本文针对四旋翼无人飞行器运送系统,提出了一种新颖的轨迹规划与跟踪控制方法.论文首先得到了平面四旋翼无人飞行器的运动特性与负载摆角之间的非线性耦合关系.通过相平面内的几何分析,分别设计了两个轴方向上的分段式加速度轨迹.这种轨迹具有简洁的解析表达式并可获得较高的运送效率,同时满足飞行器的速度,加速度等物理约束.为了使四旋翼无人飞行器准确跟踪规划好的轨迹,本文基于反步法设计了一种非线性跟踪控制器,并通过李雅普诺夫方法对其闭环稳定性进行分析,证明其能使跟踪误差指数收敛于零.论文最后通过仿真结果验证了本文所提出方法的可行性与有效性,及其对外界干扰的鲁棒性.     

Intermittent observer-based consensus control for multi-agent systems with switching topologies

In this paper, we focus on the consensus problem for leaderless and leader–followers multi-agent systems with periodically intermittent control. The dynamics of each agent in the system is a linear system, and the interconnection topology among the agents is assumed to be switching. We assume that each agent can only share the outputs with its neighbours. Therefore, a class of distributed intermittent observer-based consensus protocols are proposed for each agent. First, in order to solve this problem, a parameter-dependent common Lyapunov function is constructed. Using this function, we prove that all agents can access a prescribed value, under the designed intermittent controller and observer, if there are suitable conditions on communication. Second, based on the investigation of the leader-following consensus problem, we design a new distributed intermittent observer-based protocol for each following agent. Finally, we provide an illustrative example to verify the effectiveness of the proposed approach.     

多智能体切换网络自适应组一致性

在切换网络下,针对具有未知且互不相同的非线性动态的多智能体系统,假设其未知的非线性动态可线性参数化,并运用自适应控制策略和牵制控制方法,提出了一种组一致性算法,使得多智能体系统在切换网络下达到组一致性。该算法仅仅依赖相邻智能体间的相对位置信息,基于Lyapunov理论、Barbalat引理、自适应控制理论及代数图论等理论,对该算法进行了稳定性分析和参数收敛分析。通过一个仿真实例验证了提出的算法有效性。