Relative Local Information Research Articles

Distributed Robust Optimization for Networked Agent Systems With Unknown Nonlinearities

The distributed robust optimization problem is investigated in this paper for a class of networked agent systems under stochastically switching communication graphs. Unlike most existing results on distributed optimization of networked agent systems, the dynamics of each agent in the present model are allowed to be subjected to matched linearly-parameterized unknown nonlinearities while the underlying communication graph is stochastically switching among some matrix-weighted communication graphs. The objective here is to cooperatively optimize a holistic cost function, which is a sum of all the convex local objective functions, under the mild condition that each agent may only have access to the gradient of its local objective function and the relative local information between itself and its neighbors. To solve such an outstanding optimization problem, a neuro-adaptive optimization protocol is developed based upon parameterizations of the unknown nonlinearities where the neuro-adaptive learning algorithm for estimating the weight matrices therein acts as the key role to compensate for the unknown nonlinearities. Furthermore, the signum function-based feedback law is subtly integrated into the proposed protocol to deal with the effect of the gradient term of the local objective function. Efficient criteria are provided to guide selections of proper control parameters under which the robust optimization problem is successfully resolved in the mean-square sense. Lastly, theoretical results are validated by presenting several illustrative simulation examples.

Formation control for discrete‐time heterogeneous multi‐agent systems

AbstractIn this article, the formation control problem is investigated for a class of general first/second‐order discrete‐time heterogeneous multi‐agent systems. Under directed connected topology, a novel formation control protocol is designed by using the relative local information among heterogeneous agents and the formation information in the desired formation. For the discrete‐time heterogeneous multi‐agent systems, some sufficient conditions are derived for reaching the desired formation. If the considered system has the same number of first‐order and second‐order agents, some necessary and sufficient conditions are also obtained by utilizing the bilinear transformation method. Finally, two numerical examples are proposed to demonstrate the feasibility of the obtained results.

Guaranteed-Performance Time-Varying Formation Control for Swarm Systems Subjected to Communication Constraints

Guaranteed-performance time-varying formation analysis and design problems for general high-order swarm systems with communication constraints are investigated. First, a guaranteed-performance time-varying formation control protocol is proposed by using the intermittent relative local information, and the performance function is introduced to guarantee the formation regulation performance among neighboring agents. An explicit presentation of the formation center function is presented to describe the movement trajectory of the formation as a whole. Then, sufficient conditions for guaranteed-performance time-varying formation analysis and design are respectively given by linear matrix inequality techniques, and a guaranteed-performance cost is determined meanwhile. When the desired guaranteed-performance time-varying formation satisfies the time-varying formation feasibility condition, swarm systems with communication constraints can achieve the guaranteed-performance time-varying formation by designing the gain matrix of the guaranteed-performance time-varying formation control protocol if the nominal coverage rate is larger than a positive threshold. Finally, a numerical simulation is shown to illustrate theoretical results.

Multi-resolution visual fiducial and assistant navigation system for unmanned aerial vehicle landing

To achieve more accurate navigation performance in landing process, a multi-resolution visual fiducial system is proposed as the assistance for unmanned aerial vehicle. This system use captured image of artificial landmark (e.g. barcode) to provide relative localization information in X, Y, Z axes, and Yaw, Roll, Pitch orientations. Multi-resolution coding system is designed to ensure unmanned aerial vehicle will not lose landing target detection due to limited visual angle or camera resolution. Experiments prove the performance of proposed system in detection accuracy, time consumption, and navigation. Comparison experiments with the state-of-the-art systems are also included and analyzed.

Second-order consensus in multi-agent systems with directed topologies and communication constraints

The paper investigates second-order consensus problem for multi-agent systems with nonlinear dynamic and directed topologies, where each agent can only communicate with its neighbors on some disconnected time intervals. A novel intermittent consensus protocol is designed by its own and the neighbor's relative local information. Based on the Lyapunov stability theory and the intermittent control method, some novel and simple criteria are derived for consensus of multi-agent systems under a fixed strongly connected topology, it is proved that consensus can be reached if the general algebraic connectivity and the measure of communication are larger than the corresponding threshold values, respectively. Finally, two numerical examples are provided to demonstrate the effectiveness of the obtained theoretical results.

Consensus for linear multi-agent system with intermittent information transmissions using the time-scale theory

This paper investigates the consensus problem for linear multi-agent system with fixed communication topology in the presence of intermittent communication using the time-scale theory. Since each agent can only obtain relative local information intermittently, the proposed consensus algorithm is based on a discontinuous local interaction rule. The interaction among agents happens at a disjoint set of continuous-time intervals. The closed-loop multi-agent system can be represented using mixed linear continuous-time and linear discrete-time models due to intermittent information transmissions. The time-scale theory provides a powerful tool to combine continuous-time and discrete-time cases and study the consensus protocol under a unified framework. Using this theory, some conditions are derived to achieve exponential consensus under intermittent information transmissions. Simulations are performed to validate the theoretical results.

Distributed consensus of multi-agent systems with nonlinear dynamics via adaptive intermittent control

The consensus problem is investigated for a class of multi-agent systems with nonlinear dynamics on a fixed directed information network, where each agent is assumed to share information only with its neighbors on some disconnected time intervals. A novel adaptive intermittent control protocol is first introduced, where only intermittent relative local information is used. Based on the Lyapunov stability theory combined with the method of the adaptive control and intermittent control, some novel and simple consensus criteria are derived under a fixed strongly connected topology, it is proved that consensus can be reached if the measure of communication is larger than a threshold value. The results are then extended to consensus with a virtual leader where the underlying topology is not necessarily strong connected or contains a directed spanning tree. Finally, two numerical examples are provided to demonstrate the effectiveness of the obtained theoretical results.

Robust Relative Localization Using a Novel Modified Rounding Estimation Technique

Accurate relative location estimation is a key requirement in indoor localization systems based on wireless sensor networks (WSNs). However, although these systems have applied not only various optimization algorithms but also fusion with sensors to achieve high accuracy in position determination, they are difficult to provide accurate relative azimuth and locations to users because of cumulative errors in inertial sensors with time and the influence of external magnetic fields. This paper based on ultra-wideband positioning system, which is relatively suitable for indoor localization compared to other wireless communications, presents an indoor localization system for estimating relative azimuth and location of location-unaware nodes, referred to as target nodes without applying any algorithms with complex variable and constraints to achieve high accuracy. In the proposed method, the target nodes comprising three mobile nodes estimate the relative distance and azimuth from two reference nodes that can be installed by users. In addition, in the process of estimating the relative localization information acquired from the reference nodes, positioning errors are minimized through a novel modified rounding estimation technique in which Kalman filter is applied without any time consumption algorithms. Experimental results show the feasibility and validity of the proposed system.

Multi-vehicle relative localization based on single range measurements

Most cooperative motion tasks of multi vehicle systems require the agents to share relative localization information. Assuming the relative velocity of the vehicles to be known, under suitable observability conditions, relative localization among a pair of agents can be performed based on single range measurements. The problem addressed consists in designing a relative localization solution for a networked group of vehicles measuring mutual ranges: in particular, the objective is to exploit the presence of intra-vehicle communications to enhance the range-based relative position estimation. Geometrical constraints associated to the agents' (unknown) positions are explicitly accounted for in the estimation schema. The approach brings together a recent single range localization solution with a projection based Kalman filter estimation technique in the presence of state space constraints. Simulation examples are provided showing the effectiveness of the proposed solution.

Consensus and its ℒ2-gain performance of multi-agent systems with intermittent information transmissions

This article addresses the consensus problem for cooperative multiple agents with nonlinear dynamics on a fixed directed information network, where each agent can only communicate with its neighbours intermittently. A class of control algorithms is first introduced, using only intermittent relative local information. By combining tools from switching systems and Lyapunov stability theory, some sufficient conditions are established for consensus of multi-agent systems without any external disturbances under a fixed strongly connected topology. Theoretical analyses are further provided for consensus of multi-agent systems in the presence of external disturbances. It is shown that a finite ℒ2-gain performance index for the closed-loop multi-agent systems can be guaranteed if the coupling strength of the network is larger than a threshold value determined by the average communication rate and the generalised algebraic connectivity of the strongly connected topology. The results are then extended to consensus with prescribed ℒ2-gain performance with a virtual leader where the underlying topology is not necessarily strong connected or contain a directed spanning tree. Numerical simulations are finally provided to verify and visualise the theoretical analysis.