Mengbin Ye

Multiagent self-localization using bearing only measurements

This paper proposes a two stage approach to solving a simple network localization problem arising in the control of multi-vehicle formation shapes using bearing-only measurements. While it is impossible for one agent to localize, in its own coordinate basis, a second agent undergoing arbitrary plane motion using bearing-only measurements, this paper shows how to use a combination of a Fourier Transform and an overdetermined linear system of equations to allow two agents undergoing plane circular motion to localize each other. It is postulated that each agent only knows the parameters fully describing its own motion and must determine enough parameters of the other agent to localize it. A Fourier Transform of the measured bearing is used by each agent to obtain an approximate magnitude of the other agents angular velocity and a two-dimensional search grid is used in an overdetermined linear equation system to solve the localization problem. The paper investigates the effect of noise in bearing measurements on the accuracy of the proposed method, offering some potential methods of decreasing the effect of noise.

Model-independent rendezvous of Euler-Lagrange agents on directed networks

This paper proposes a distributed, modelindependent algorithm to achieve rendezvous to a stationary leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics. We show that if the directed graph contains a directed spanning tree, with the leader as the root node and with no incoming edges, then a model-independent algorithm semi-globally achieves the rendezvous objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the parameters of the self-dynamics of any agent in the network. For stability, a pair of control gain terms for each agent are required to meet several inequalities and so design of the algorithm requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithms effectiveness.

Bearing-Only Measurement Self-Localization, Velocity Consensus and Formation Control

Self-localization and formation control tasks are considered when each agent in a multiagent formation observes its neighbors but does not communicate. Each agent is restricted to a predefined motion type on a 2-D plane and collects bearing-only measurements over a time interval to localize neighboring agents. The localization process is used by a three agent formation to achieve velocity consensus combined with formation shape control. Simulations are provided and noisy bearing measurements are investigated.

Cooperative localisation of UAVs in a GPS-denied environment using bearing measurements

This paper studies the problem of localising a Global Positioning System (GPS)-denied Unmanned Aerial Vehicle (UAV) in two-dimensional space. Suppose there are two vehicles, one which is equipped with GPS and the other is GPS-denied (but has an inertial navigation system (INS) and so is able to determine its trajectory in a local coordinate frame, but not a global coordinate frame). The GPS-equipped vehicle broadcasts its global coordinates to the GPS-denied vehicle and the GPS-denied vehicle also obtains, in its local coordinate frame, a bearing measurement of the GPS-equipped UAV. The paper shows that with four or more such measurements and generic trajectories of the two UAVs, localisation in a global coordinate frame of the GPS-denied UAV is achievable. Certain nongeneric trajectories for which localisation is impossible are also identified. While in the first instance, the solution assumes zero noise in the measurements, the techniques are then extended to deal with the presence of measurement noise.

Event-Triggered Algorithms for Leader-Follower Consensus of Networked Euler-Lagrange Agents

This paper proposes three different distributed event-triggered control algorithms to achieve leader–follower consensus for a network of Euler–Lagrange agents. We first propose two model-independent algorithms for a subclass of Euler–Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work studying event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities, which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information, and bounds on the initial conditions. When the Euler–Lagrange agents have dynamics that include the vector of gravitational potential forces, an adaptive algorithm is proposed. This requires more information about the agent dynamics but allows for the estimation of uncertain parameters associated with the agent self-dynamics. For each algorithm, a trigger function is proposed to govern the event update times. The controller is only updated at each event, which ensures that the control input is piecewise constant and thus saves energy resources. We analyze each controller and trigger function to exclude Zeno behavior.

Model-independent trajectory tracking of Euler-Lagrange agents on directed networks

The problem of trajectory tracking of a moving leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics is studied in this paper using a distributed, model-independent control law. We show that if the directed graph contains a directed spanning tree, with the leader as the root node, then a model-independent algorithm semi-globally achieves the trajectory tracking objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the agent self-dynamics, though reasonably, certain bounds are known. For stability, a pair of control gains for each agent are required to satisfy lower bounding inequalities and so design of the algorithm is centralised and requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithms effectiveness.

Event-based leader-follower consensus for multiple Euler-Lagrange systems with parametric uncertainties

An adaptive, distributed, event-triggered controller is proposed in this paper to study the problem of leader-follower consensus for a directed network of Euler-Lagrange agents. We show that if each agent uses the proposed controller, the leader-follower consensus objective is globally asymptotically achieved if the directed network contains a directed spanning tree with the leader as the root node. We provide a trigger function to govern the event time; at each event time the controller is updated. In doing so, we also obtain an explicit lower bound on the time interval between events and thus we conclude that the proposed controller does not exhibit Zeno behavior. Simulations are provided which show the effectiveness of the proposed controller. Also shown in the simulations is the piecewise constant nature of the control law; this significantly reduces the number of updates required by each actuator, thereby saving energy resources.

A variable gain model-independent algorithm for rendezvous of Euler-Lagrange agents on directed networks

A variable-gain model-independent control law is proposed to solve the problem of rendezvous to a leader for a directed network of Euler-Lagrange agents. A sufficiency condition for stability is developed, requiring centralised design of the two control gains. Compared to existing results which use constant-gain model-independent controllers for directed networks, our work has two key differences. Firstly, the damping term may begin at zero, and increases only if rendezvous has not been achieved. Constant-gain controllers use conservative gain values which negatively impact convergence speed. Secondly, we introduce a novel method of analysing the Lyapunov derivative, which provides useful and unique insight into analysis of variable-gain controllers for multi-agent systems. Simulations are provided to show the effectiveness of the controller.