Consensus of double-integrator multi-agent systems without relative states derivative under relative-state dependent measurement noises*

This paper proposes a consensus protocol for continuous-time double-integrator multi-agent systems under noisy communication in directed topologies. Each agent’s control input relies on its own velocity and the relative positions with neighbours; it does not require the relative velocities. The agent receives its neighbours’ positions information corrupted by time-varying measurement noises whose intensities are proportional to the absolute relative distance that separates the agent from the neighbours. The consensus protocol is mainly based on the velocity damping gain to derive conditions under which the unbiased mean square χ-consensus is achieved in directed fixed topologies, and the unbiased mean square average consensus is achieved in directed switching topologies. The mean square state errors are quantified for both the positions and velocities. Finally, to illustrate the approach presented, some numerical simulations are performed.     

Leader-following consensus of double-integrator multi-agent systems with noisy measurements

This paper proposes a leader-following consensus control for continuous-time double-integrator multi-agent systems in noisy communication environment with a constant velocity reference state. Each follower in the team inaccurately measures its neighbors’ positions and the leader’s position if this follower has access to the leader, that the measured positions are corrupted by noises. The constant velocity of the leader is a priori well known. The consensus protocol is constructed based on algebraic graph theory and some stochastic tools. Conditions to ensure the tracking consensus in mean square are derived for both fixed and switching directed topologies. Finally, to illustrate the approach presented, some numerical simulations are carried out.     

Mean square average-consensus under measurement noises and fixed topologies: Necessary and sufficient conditions

In this paper, average-consensus control is considered for networks of continuous-time integrator agents under fixed and directed topologies. The control input of each agent can only use its local state and the states of its neighbors corrupted by white noises. To attenuate the measurement noises, time-varying consensus gains are introduced in the consensus protocol. By combining the tools of algebraic graph theory and stochastic analysis, the convergence of these kinds of protocols is analyzed. Firstly, for noise-free cases, necessary and sufficient conditions are given on the network topology and consensus gains to achieve average-consensus. Secondly, for the cases with measurement noises, necessary and sufficient conditions are given on the consensus gains to achieve asymptotic unbiased mean square average-consensus. It is shown that under the protocol designed, all agents’ states converge to a common Gaussian random variable, whose mathematical expectation is just the average of the initial states.     

Synchronization of General Linear Multi‐Agent Systems With Measurement Noises

This paper studies the synchronization of general linear multi‐agent systems with measurement noises in mean square. It shows that the conventional consensus protocol is efficient and robust to the additive and multiplicative measurement noises in mean square. For the additive measurement noises which are independent of the relative‐states, it shows that the multi‐agent systems can achieve synchronization in practical mean square. For the multiplicative measurement noises which are dependent of the relative‐states, it shows that the multi‐agent systems can achieve synchronization in (strict) mean square. Furthermore, the new consensus protocol is better than the conventional one at some specific situations, i.e., the multi‐agent systems with additive measurement noises can also achieve synchronization in (strict) mean square. Numerical simulations are also provided and the results show highly consistent with the theoretical results.     

Second-order consensus in multi-agent systems based on second-order neighbours’ information

This paper mainly investigates the consensus of second-order multi-agent systems by using the second-order neighbours’ information. The comparison between the convergence rate of second-order neighbour protocol and the one of general protocol is developed. When multi-agent system achieves maximum speed consensus, the relation between the parameter in general protocol and that in second-order neighbour protocol is analysed. If there exists delay in communication, the delay margins of general protocol and second-order neighbour protocol are derived. Numerical examples are given to illustrate the effectiveness of the proposed methods.     

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.     

Consensus control of second-order stochastic discrete-time multi-agent systems without velocity transmission

This article studies the almost-sure and the mean-square consensus control problems of second-order stochastic discretetime multi-agent systems with multiplicative noises. First, a control law based on the absolute velocity and relative position information is designed. Second, considering the existence of multiplicative noises and nonlinear terms with Lipschitz constants, the consensus control problem is solved through the use of a degenerated Lyapunov function. Then, for the linear second-order multi-agent systems, some explicit consensus conditions are provided. Finally, two sets of numerical simulations are performed.     

Leader-following consensus for single-integrator multi-agent systems with multiplicative noises in directed topologies

This paper proposes a leader-following consensus control for continuous-time single-integrator multi-agent systems with multiplicative measurement noises under directed fixed and switching topologies. The consensus controller is developed by combining the graph theory and stochastic tools. The control input for each agent relies on its own state and its neighbours’ states corrupted by noises, the noises are considered proportional to the relative distance between agents, both of the noisy case and the noise-free case are studied, and conditions to achieve mean square convergence under noisy measurement and asymptotic convergence in absence of noises are derived. Finally, in order to prove the validity of the consensus control, some simulations were carried out.     

Distributed consensus for multi-agent systems with delays and noises in transmission channels

This paper studies the distributed consensus problem for linear discrete-time multi-agent systems with delays and noises in transmission channels. Due to the presence of noises and delays, existing techniques such as the lifting technique and the stochastic Lyapunov theory are no longer applicable to the analysis of consensus. In this paper, a novel technique is introduced to overcome the difficulties induced by the delays and noises. A consensus protocol with decaying gains satisfying persistence condition is adopted. Necessary and sufficient conditions for strong consensus and mean square consensus are respectively given for non-leader–follower and leader–follower cases under a fixed topology. Under dynamically switching topologies and randomly switching topologies, sufficient conditions for strong consensus and mean square consensus are also obtained. Numerical examples are given to demonstrate the effectiveness of the proposed protocols.     

Consensus controller for multi-UAV navigation

In this paper, we design consensus algorithms for multiple unmanned aerial vehicles (UAV). We mainly focus on the control design in the face of measurement noise and propose a position consensus controller based on the sliding mode control by using the distributed UAV information. Within the framework of Lyapunov theory, it is shown that all signals in the closed-loop multi-UAV systems are stabilized by the proposed algorithm, while consensus errors are uniformly ultimately bounded. Moreover, for each local UAV, we propose a mechanism to define the trustworthiness, based on which the edge weights are tuned to eliminate negative influence from stubborn agents or agents exposed to extremely noisy measurement. Finally, we develop software for a nano UAV platform, based on which we implement our algorithms to address measurement noises in UAV flight tests. The experimental results validate the effectiveness of the proposed algorithms.     

多智能体系统自适应跟踪控制

基于带有非线性动态的二阶多智能体系统,研究了在有动态领导者条件下的跟踪一致性问题。假设跟随者只能获取邻居智能体的相对状态信息,只有一部分跟随者可以获得领导者的位置和速度信息,领导者的控制输入非零且不被任何一个跟随者可知。在通信拓扑为无向连通图的条件下,为了避免全局信息的不确定性,设计了分布式自适应控制协议。将系统的一致性问题转化为误差系统的一致性问题,通过Lyapunov稳定性理论和矩阵理论分析得到了该协议使系统达到一致的充分条件。最后用仿真例子证明了设计方法的有效性。     

Stochastic bounded consensus tracking of second-order multi-agent systems with measurement noises based on sampled-data with general sampling delay

This paper investigates the stochastic bounded consensus tracking problems of second-order multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, the measurements are corrupted by random noises and the signal sampling process induces the general sampling delay. First, the stochastic bounded consensus tracking protocol based on sampled-data with the general sampling delay is presented by using the delay decomposition technique. Second, the augmented matrix method, the probability limit theory and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. The theoretical results show that the convergence of the proposed protocol simultaneously depends on the constant feedback gains, the network topology, the sampled period and the sampling delay, and that the static consensus tracking error depends on not only the above-mentioned factors, but also the noise intensity and the upper bound of the velocity and the acceleration of the virtual leader. The obtained results cover no sampling delay and the small sampling delay as two special cases. Simulations are provided to demonstrate the effectiveness of the theoretical results.     

Second-order consensus of multi-agent systems with noises via intermittent control

Second-order consensus of multi-agent systems with noises via intermittent control is investigated in this paper. First, we study the mean-square consensus problem with communication noises by intermittent control. In order to reach consensus, under the strong directed interacted topology, by using the tools of graph theory and Lyapunov method, a distributed control protocol is proposed based on the noises and periodical intermittent information. The upper bound of noise strength in the sense of matrix norm and the lower bound of communication time duration are obtained. Second, a class of coupled system models which include delay-terms in their nonlinearities in the noisy environment is discussed. Under the balanced strongly connected topology, the sufficient conditions to achieve the mean-square average-consensus are obtained. Finally, simulations are given to illustrate the effectiveness of our results.     

Velocity Estimation for Robot Manipulators Using Neural Network

In robot manipulators, optical incremental encoders are widely used as the transducers to monitor joint position and velocity information. With incremental encoder, positional information is determined as discrete data relative to a reference (home) position. However, velocity information can only be deduced by processing the position data. In this paper, a method of using a neural network to estimate the velocity information of robotic joint from discrete position versus time data is proposed and evaluated. The architecture of the neural net and the training methodology are presented and discussed.This approach is then applied to estimate the joint velocity of a SCARA robot while performing an electronic component assembly task. Based on computer simulations, comparison of the accuracy of the neural network estimator with two other well established velocity estimation algorithms are made. The neural net approach can maintain good performance even in the presence of measurement noises.     

多智能体系统离散时间一致性问题中的参数设计

研究二阶多智能体系统在固定、有向通信拓扑条件下的离散时间一致性问题.针对每个智能体,采用基于速度、位置的分布式控制协议,分析速度、位置增益系数以及采样周期等参数对系统一致性的影响.结果表明,要实现二阶多智能体系统的一致性控制,在保持个体之间通信拓扑有生成树的同时,控制协议中的各个参数还必须满足一定的约束条件.系统拉普拉斯矩阵的特征值和采样周期对系统能否实现渐近一致起到了关键的作用.最后,给出一致性控制中参数设计的一种可行方法,并通过仿真对理论分析的正确性进行验证.     

Mean square consensus of multi-agent systems with multiplicative noises and time delays under directed fixed topologies

This paper investigates the mean square consensus problem of multi-agent systems impacted by the combined uncertainty of multiplicative noises and time delays. Considering general network under directed fixed topologies, we propose consensus protocol that take into account both the multiplicative noises and time delays. Using tools from stochastic differential delay equation (SDDE), martingale theory and stochastic inequality, we establish sufficient conditions and obtain the explicit consensus gain and delay upper bounds under which the proposed protocol leads to mean square consensus. In addition, we compare the impact of multiplicative and additive noise and reach the conclusion that multiplicative noises have the property of stabilizing effect. Simulations demonstrate the theoretical results.     

Distributed tracking control of leader-follower multi-agent systems under noisy measurement

In this paper, a distributed tracking control scheme with distributed estimators has been developed for a leader-follower multi-agent system with measurement noises and directed interconnection topology. It is supposed that each follower can only measure the relative positions of its neighbors in a noisy environment, including the relative position of the second-order active leader. A neighbor-based tracking protocol together with distributed estimators is designed based on a novel velocity decomposition technique. It is shown that the closed loop tracking control system is stochastically stable in mean square and the estimation errors converge to zero in mean square as well. A simulation example is finally given to illustrate the performance of the proposed control scheme.     

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.     

Quantised consensus by using the PD-like protocols in directed networks

This paper studies the distributed consensus problem in sampled-data multi-agent systems with directed network topologies subject to a quantisation constraint. Different from the widely used consensus protocol which exploits current information, we adopt a distributed proportional-differential (PD)-like protocol. First, we provide a necessary and sufficient condition of control gains which guarantee the consensus with the assumption that real-valued communication information can be obtained. Next, we analyse the quantisation effects in system performances under the proposed protocol; it is proved that the quantised consensus can be achieved. Finally, by providing numerical examples, we show that with appropriate parameters, the consensus can be achieved and the quantisation noises can be attenuated effectively.     

带虚拟领导者的多智能体系统的非光滑一致性

研究了在限制交互范围条件下具有一个虚拟领导者的二阶多智能体系统的一致性问题。假设多智能体系统中所有智能体均可以接收到领导者的信息，而智能体之间只有距离在一定范围内才可以进行相互通信。在相对状态反馈的线性一致性协议下，通过李雅普诺夫法，证明了该多智能体系统二阶一致性的充分条件。接着通过仿真实例验证了理论结果，并对该算法作出了总结。