We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The alg...
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We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats intermodular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulator VisibleSim, and in real-life experiments on the modular robotic system Blinky Blocks.
We consider aggregative games with affine coupling constraints, where agents have partial information on the aggregate value and can only communicate with neighboring agents. We propose a single-layer distributed algo...
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We consider aggregative games with affine coupling constraints, where agents have partial information on the aggregate value and can only communicate with neighboring agents. We propose a single-layer distributed algorithm that reaches a variational generalized Nash equilibrium, under constant step sizes. The algorithm works on a single timescale, i.e., it does not require multiple communication rounds between agents before updating their action. The convergence proof leverages an invariance property of the aggregate estimates and relies on a forward-backward splitting for two preconditioned operators and their restricted (strong) monotonicity properties on the consensus subspace.
This article focuses on the distributed static estimation problem. A belief propagation (BP) based estimation algorithm is studied for its convergence and accuracy. More precisely, we give conditions under which the B...
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This article focuses on the distributed static estimation problem. A belief propagation (BP) based estimation algorithm is studied for its convergence and accuracy. More precisely, we give conditions under which the BP-based distributed estimator is guaranteed to converge and we give concrete characterizations for its accuracy. Our results reveal new insights and properties of this distributed algorithm, leading to better theoretical understanding of static distributed state estimation and new applications of the algorithm.
In this article, a distributed multiobjective optimization problem is formulated for the resource allocation of network-connected multiagent systems. The framework encompasses a group of distributed decision makers in...
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In this article, a distributed multiobjective optimization problem is formulated for the resource allocation of network-connected multiagent systems. The framework encompasses a group of distributed decision makers in the subagents, where each of them possesses a local preference index. Novel distributed algorithms are proposed to solve such a problem in a distributed manner. The weighted L-p preference index is utilized in each agent since it can provide a robust Pareto solution to the problem. By using distributed fixed-time optimization methods, the L-p preference index is constructed online without specifying the unknown parameters. Then, it is proved that the problem admits a unique Pareto solution. By exploiting consensus and gradient descent techniques, asymptotic convergence to the optimal solution is established via Lyapunov theories. Distinct from most of the current works, the proposed framework does not require any prior information in the formulation process, and private data can be well protected using this distributed approach. Numerical examples are included to validate the effectiveness of the proposed algorithms.
Communication systems are affected by channel distortions. Impulsive noise is one of the significant factors for channel impairments. The standard additive white Gaussian noise (AWGN) channel model and conventional es...
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Communication systems are affected by channel distortions. Impulsive noise is one of the significant factors for channel impairments. The standard additive white Gaussian noise (AWGN) channel model and conventional estimation algorithms like least mean square (LMS) and its variants tend to be ineffective under such conditions. This paper presents a robust adaptive channel estimation algorithm using the Geman-McClure estimator in a diffusion-based distributed network. The analytical study on mean stability and mean square analysis is carried out under two separate noise statistics: Symmetric alpha-stable (S alpha S) and Bernoulli-Gaussian (BG) distribution. The computer simulations confirm the proposed algorithm's competitive robustness compared to the Maximum Correntropy Criterion and Minimum Kernel Risk Sensitive Loss algorithms at a high impulsive noise environment without exponential cost function. Further, the efficiency is also verified by simulating the bit error rate by designing a minimum mean square error (MMSE) equalizer with the estimated coefficients.
Many real-world data are labeled with natural orders, i.e., ordinal labels. Examples can be found in a wide variety of fields. Ordinal regression is a problem to predict ordinal labels for given patterns. There are sp...
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Many real-world data are labeled with natural orders, i.e., ordinal labels. Examples can be found in a wide variety of fields. Ordinal regression is a problem to predict ordinal labels for given patterns. There are specially developed ordinal regression methods to tackle this type of problems, but they are usually centralized. However, in some scenarios, data are collected distributedly by nodes of a network. For the purpose of privacy protection or due to some practical constraints, it is difficult or impossible to transmit the data to a fusion center for processing. Thus the centralized ordinal regression methods are inapplicable. In this paper, we formulate a distributed generalized ordered logit model for distributed ordinal regression. To estimate parameters in the model, a distributed constrained optimization formulation based on maximum likelihood methods is established. Then, we propose a projected gradient based algorithm to solve the optimization problem. We prove the consensus and the convergence of the proposed distributed algorithm. We also conduct numerical simulations on synthetic and real-world datasets. Simulation results show that the proposed distributed algorithm is comparable to the corresponding centralized algorithm. Even when the data label distribution among nodes is unbalanced, the proposed algorithm still has competitive performance.
As powerline communication (PLC) technology does not require dedicated cabling and network setup, it can be used to easily connect multitude of IoT devices deployed in enterprise environments for sensing and control r...
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As powerline communication (PLC) technology does not require dedicated cabling and network setup, it can be used to easily connect multitude of IoT devices deployed in enterprise environments for sensing and control related applications. IEEE has standardized the PLC protocol in IEEE 1901, also known as HomePlug AV (HPAV) which has been widely adopted in mainstream PLC devices. A key weakness of HPAV protocol is that it does not support spectrum sharing. Currently, each link in an HPAV PLC network operates over the whole available spectrum, and only one link can operate at any time within a single collision domain. In this work, through an extensive measurement study of HPAV PLCs in a real enterprise environment using commodity off-the-shelf (COTS) HPAV PLC devices, we discover that spectrum sharing can significantly benefit enterprise level PLC networks. To this end, we propose a distributed spectrum sharing technique for enterprise HPAV PLC networks, and show that fine-grained distributed spectrum sharing on top of current HPAV MAC protocols can significantly boost the aggregated and per-link throughput, by allowing multiple PLC links to communicate concurrently, while requiring only a few modifications to the existing HPAV devices and protocols.
This study presents a fixed-time convergent algorithm to achieve distributed least square (DLS) solutions of networked linear equations. Each agent in the network only knows a subset of the equations and can only exch...
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This study presents a fixed-time convergent algorithm to achieve distributed least square (DLS) solutions of networked linear equations. Each agent in the network only knows a subset of the equations and can only exchange messages with its nearest neighbors. Unlike finite-time counterparts, the settling time of the fixed-time distributed algorithm does not depend upon the initial states, and can be preassigned according to the requirements of the task. Numerical simulations verify the theoretical results.
We present a novel generalized constrained convex optimization model for multiagent systems that contains both the local, coupled equality, and inequality constraints, and a global resource allocation constraint. This...
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We present a novel generalized constrained convex optimization model for multiagent systems that contains both the local, coupled equality, and inequality constraints, and a global resource allocation constraint. This model unifies the traditional constrained optimization problem, the resource allocation problem, and the economic dispatch problem. Unlike the majority of literature where each local objective function is required to be convex, we only require a milder condition that the global objective function is convex. The gradient of the global Lagrangian is estimated locally by each agent using the dynamic average consensus protocol. Synchronously, modified primal-dual dynamics produce the optimal solution via the estimated gradient. The generalized Lagrange multiplier method is introduced to avoid the usual positive projections in the presence of inequality constraints. This leads to smooth dynamics and a continuous Lyapunov derivative, which enables the exponential stability analysis. Simulation examples support the proposed distributed methods.
This article proposes a new control strategy for the voltage regulation of unbalanced low-voltage (LV) distribution grids with high penetration of distributed renewable energy sources (DRESs). The proposed method uses...
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This article proposes a new control strategy for the voltage regulation of unbalanced low-voltage (LV) distribution grids with high penetration of distributed renewable energy sources (DRESs). The proposed method uses the available reactive power of DRESs as the primary means for voltage regulation. Furthermore, its distinct feature is the use of a distributed control architecture prioritizing the response of DRESs to maintain the network voltages within permissible limits and minimize the network losses. The prioritization process is locally implemented by each DRES combining two types of information: (a) the sensitivity matrix that quantifies the impact of reactive power variations on the network voltages and (b) voltage measurements along the network. Time-domain and time-series simulations on the IEEE European LV test feeder are performed to evaluate the performance of the proposed method against existing decentralized, distributed and centralized, optimization-based methods.
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