This paper studies the leader-following consensus problem of second-order multi-agent systems with directed topologies. By employing the asynchronous sampled-data protocols, sufficient conditions for leader-following consensus with both constant velocity leader and variable velocity leader are derived. {Leader-following quasi-consensus can be achieved in multi-agent systems when all the agents sample the information asynchronously.} Numerical simulations are provided to verify the theoretical results.
The simultaneous problem of consensus and trajectory tracking of linear multi-agent systems is considered in this paper, where the dynamics of each agent is represented by a single-input single-output linear system. In order to solve this problem, a distributed control strategy is proposed in this work, where the trajectory and the formation of the agents are achieved asymptotically even in the presence of switching communication topologies and smooth formation changes, and ensuring the closed-loop stability of the multi-agent system. Moreover, the structure and dimension of the representation of the agent dynamics are not restricted to be the same, as usually assumed in the literature. A simulation example is provided in order to illustrate the main results.
This paper considers a distributed state estimation problem for multi-agent systems under state inequality constraints. We first give a distributed estimation algorithm by projecting the consensus estimate with help of the consensus-based Kalman filter (CKF) and projection on the surface of constraints. The consensus step performs not only on the state estimation but also on the error covariance obtained by each agent. Under collective observability and connective assumptions, we show that consensus of error covariance is bounded. Based on the Lyapunov method and projection, we provide and prove convergence conditions of the proposed algorithm and demonstrate its effectiveness via numerical simulations.
This paper is concerned with solving the distributed resource allocation optimization problem by multi-agent systems over undirected graphs. The optimization objective function is a sum of local cost functions associated to individual agents, and the optimization variable satisfies a global network resource constraint. The local cost function and the network resource are the private data for each agent, which are not shared with others. A novel gradient-based continuous-time algorithm is proposed to solve the distributed optimization problem. We take an event-triggered communication strategy and an event-triggered gradient measurement strategy into account in the algorithm. With strongly convex cost functions and locally Lipschitz gradients, we show that the agents can find the optimal solution by the proposed algorithm with exponential convergence rate, based on the construction of a suitable Lyapunov function. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed scheme.
In this paper, the distributed H∞ estimation problem is investigated for a moving target with local communication and switching topology. Based on the solution of the algebraic Riccati equation, a recursive algorithm is proposed using constant gain. The stability of the proposed algorithm is analysed by using the Lyapounov method, and a lower bound for estimation errors is obtained for the proposed common H∞ filter. Moreover, a bound for the H∞ parameter is obtained by means of the solution of the algebraic Riccati equation. Finally, a simulation example is employed to illustrate the effectiveness of the proposed estimation algorithm.
Distributed optimization over unbalanced graphs is an important problem in multi-agent systems. Most of literatures, by introducing some auxiliary variables, utilize the Push-Sum scheme to handle the widespread unbalance graph with row or column stochastic matrix only. But the introduced auxiliary dynamics bring more calculation and communication tasks. In this paper, based on the in-degree and out-degree information of each agent, we propose an innovative distributed optimization algorithm to reduce the calculation and communication complexity of the conventional Push-Sum scheme. Furthermore, with the aid of small gain theory, we prove the linear convergence rate of the proposed algorithm.
In this paper, the distributed output regulation problem of linear multi-agent systems with parametric-uncertain leaders is considered. The existing distributed output regulation results with exactly known leader systems is not applicable. To solve the leader-following with unknown parameters in the leader dynamics, a distributed control law based on an adaptive internal model is proposed and the convergence can be proved.
This paper studies the dynamic coverage control problem for cooperative region reconnaissance where a group of agents are required to reconnoitre a given region. The main challenge of this problem is that the sensing region of each agent is an ellipse. This modeling results in asymmetric(directed) interactions among agents. First, the region reconnaissance is formulated as a coverage problem, where each point in the given region should be surveyed until a preset level is achieved. Then, a coverage control law is designed that minimizes coverage performance index by finite switches between nominal control laws and perturbation control law. Finally, numerical simulations are provided to indicate the efficiency of the proposed control law.
In this paper, we revisit the artificial potential based approach in the flocking control for multi-agent systems, where our main concerns are migration and trajectory tracking problems. The static destination or, more generally, the moving reference point is modeled by a virtual leader, whose information is utilized by some agents, called active agents (AA), for the controller design. We study a decentralized flocking controller for the case where the set of AAs is fixed. Some results on the velocity consensus, collision avoidance, group configuration and robustness are proposed. Further, we apply the proposed controller to the observer based flocking control of a team of nonholonomic mobile robots.
Technology has undergone rapid development in the past several decades and we are now at a point where many technologies are available to help create smart cities. Many technology companies and research institutions as well as political organizations are currently discussing this field with the highest priority. One can say that the biggest challenge to smart cities is not technologies themselves, but the merging of all available technologies into one symbiotic unit that fulfills the expected objectives. Smart cities are about connecting subsystems, sharing and evaluating data, and providing quality of life and satisfaction to citizens. We have various models of transportation systems, optimizations of energy usage, street lighting systems, building management systems, urban transport optimizations, however currently, such models are dealt with separately. In this paper, we provide an overview of the smart city concept and discuss why Multi-agent systems are the right tool for the modeling of smart cities. The biggest challenge is in connecting and linking particular subsystems within a smart city. In this paper, a modeling of a smart city building blocks is provided and demonstrated with one particular example -- a smart street lighting system. Focus will be on the decomposition of the system into subsystems as well as a description of particular modules. We propose to build models and since each individual entity can be modeled as an agent with its beliefs, desires and intentions, we suggest using Multi-agent systems as a tool for modeling systems` connections within the smart city and assessing how best to use the data from those systems.