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On Distributed Convex Optimization Under Inequality and Equality Constraints via PrimalDual Subgradient Methods. Extended Version,” Tech. Rep., 2010 [Online]. Available: http://arxiv.org/abs/1001.2612 Minghui Zhu (M’11) received the B.E. degree (with hon
 University of California, San Diego (UC San
, 2002
"... Abstract—We consider a general multiagent convex optimization problem where the agents are to collectively minimize a global objective function subject to a global inequality constraint, a global equality constraint, and a global constraint set. The objective function is defined by a sum of local ..."
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Cited by 50 (10 self)
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Abstract—We consider a general multiagent convex optimization problem where the agents are to collectively minimize a global objective function subject to a global inequality constraint, a global equality constraint, and a global constraint set. The objective function is defined by a sum of local objective functions, while the global constraint set is produced by the intersection of local constraint sets. In particular, we study two cases: one where the equality constraint is absent, and the other where the local constraint sets are identical. We devise two distributed primaldual subgradient algorithms based on the characterization of the primaldual optimal solutions as the saddle points of the Lagrangian and penalty functions. These algorithms can be implemented over networks with dynamically changing topologies but satisfying a standard connectivity property, and allow the agents to asymptotically agree on optimal solutions and optimal values of the optimization problem under the Slater’s condition. Index Terms—Cooperative control, distributed optimization, multiagent systems. I.
Attackresilient distributed formation control via online adaptation
 In IEEE Int. Conf. on Decision and Control
, 2011
"... Abstract. This paper tackles a distributed formation control problem where a group of vehicles is remotely controlled by a network of operators. Each operatorvehicle pair is attacked by an adversary, who corrupts the commands sent from the operator to the vehicle. From the point of view of operator ..."
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Abstract. This paper tackles a distributed formation control problem where a group of vehicles is remotely controlled by a network of operators. Each operatorvehicle pair is attacked by an adversary, who corrupts the commands sent from the operator to the vehicle. From the point of view of operators, each adversary follows an attacking strategy linearly parameterized by some (potentially timevarying) matrix which is unknown a priori. In particular, we consider two scenarios depending upon whether adversaries can adapt their attacking tactics online. To assure mission completion in such a hostile environment, we propose two novel attackresilient distributed control algorithms that allow operators to adjust their policies on the fly by exploiting the latest collected information about adversaries. Both algorithms enable vehicles to asymptotically achieve the desired formation from any initial configuration and initial estimate of the adversaries ’ strategies. It is further shown that the sequence of the distances to the desired formation is square summable for each proposed algorithm. In numerical examples, the convergence rates of our algorithms are exponential, outperforming the theoretic results.
On distributed optimization under inequality and equality constraints via penalty primaldual methods
, 2010
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Fast distributed consensus with chebyshev polynomials
 In American Control Conference
, 2011
"... Abstract — Global observation of the environment is a key component in sensor networks and multirobot systems. Distributed consensus algorithms make all the nodes in the network to achieve a common perception by local interactions between direct neighbors. The convergence rate of these algorithms ..."
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Cited by 3 (1 self)
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Abstract — Global observation of the environment is a key component in sensor networks and multirobot systems. Distributed consensus algorithms make all the nodes in the network to achieve a common perception by local interactions between direct neighbors. The convergence rate of these algorithms depends on the network connectivity, which is related to the second largest eigenvalue of the weighted adjacency matrix of the communication graph. When the connectivity is small, a large number of communication rounds is required to achieve the consensus. In this paper we present a new distributed consensus algorithm which uses the properties of Chebyshev polynomials to significantly increase the convergence rate. The algorithm is expressed in the form of a linear iteration and, at each step, the nodes only require to transmit their current state to their neighbors. The difference with respect to previous approaches is that our algorithm is based on a second order difference equation. We provide the analytical expression of the convergence rate and we study in which conditions it is faster than computing the powers of the weighted matrix. This improvement reduces the number of messages between nodes, saving both power and time to the networked system. We evaluate our algorithm in a simulated environment showing the benefits of our approach.
Consensus on Asynchronous Communication Networks in Presence of External Input
"... Abstract—This paper presents a class of multiagent systems where the state of each agent is driven by its own local protocol, and by exogenous timevarying input signal. These inputs may represent agent dynamics and, in presence of unreliable communication medium, transmission noise. We consider mu ..."
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Abstract—This paper presents a class of multiagent systems where the state of each agent is driven by its own local protocol, and by exogenous timevarying input signal. These inputs may represent agent dynamics and, in presence of unreliable communication medium, transmission noise. We consider multiagent systems operating over asynchronous networks. Examples of such systems are messagepassing systems in which agents don’t share a global clock and communicate only by sending and receiving messages that may be lost, delayed arbitrarily, and delivered out of order. We present conditions on the protocols and input signals that ensure zero or bounded steadystate error over asynchronous communication networks. We discuss two specific examples and apply our general results to them. These systems are an iterative groupbased protocol for average consensus of timevarying quantities, and a scheme for spatial pattern configuration, where mobile agents communicate via messagepassing. I.
A Generalized Distributed Consensus Algorithm for Monitoring and Decision Making in the IoT (Invited Paper)
"... Abstract—In this paper, we propose a method to distributively monitor a dynamic mobile network. For this purpose, we take advantage of the consensus theory to provide each node with a common view of the network. More specifically, we give a decentralized algorithm to estimate a timevarying distribu ..."
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Abstract—In this paper, we propose a method to distributively monitor a dynamic mobile network. For this purpose, we take advantage of the consensus theory to provide each node with a common view of the network. More specifically, we give a decentralized algorithm to estimate a timevarying distribution, where each node has a partial information on this distribution. Our algorithm allows a tradeoff between the precision of its estimation and its bandwidth consumption. We validate our approach by simulation under NS3, considering the distribution of several network metrics. Simulation results demonstrate how our algorithm can give insights on the network behavior that could be exploitable in a decision making process. I.
iDISTRIBUTED CONSENSUS IN MULTIROBOT SYSTEMS WITH VISUAL PERCEPTION
, 2012
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A Systematic Design Process for Internal Model Average Consensus
"... Abstract — In the dynamic average consensus problem, agents in a communication network use information from their immediate neighbors to track the average of the group’s timevarying inputs. Estimators based on the internal model principle solve this decentralized averaging problem with zero steady ..."
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Abstract — In the dynamic average consensus problem, agents in a communication network use information from their immediate neighbors to track the average of the group’s timevarying inputs. Estimators based on the internal model principle solve this decentralized averaging problem with zero steadystate tracking error while providing robustness to network topology changes, agent failures, and communication faults. We develop a systematic process for designing these estimators. By formulating estimator synthesis as a robust control problem, we decouple the design process from specific networks. This formulation allows us to use an existing robust pole placement method to design estimators that meet performance specifications for a set of networks. I.