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In this paper, we consider solving a composite optimization problem with coupling constraints in a multi-agent network based on proximal gradient method. In this problem, all the agents jointly minimize the sum of individual cost functions composed of smooth and possibly non-smooth parts. To this end, we derive the dual problem by the concept of Fenchel conjugate, which gives rise to the dual proximal gradient algorithm by allowing for the asymmetric individual interpretations of the global constraints. Then, an asynchronous dual proximal gradient algorithm is proposed for the asynchronous networks with heterogenous step-sizes and communication delays. For both the two algorithms, if the non-smooth parts of the objective functions are simple-structured, we only need to update dual variables by some simple operations, accounting for the reduction of the overall computational complexity. Analytical convergence rate of the proposed algorithms is derived and their efficacy is verified by solving a social welfare optimization problem of electricity market in the numerical simulation., 18 pages, 5 figures

High dimensionality data have become common in neuroimaging fields, especially group-level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data-driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group datasets is necessary for such analyses and is crucial to ensure the accuracy and stability of the outputs. In this study, we describe an fMRI data reduction strategy based on an adapted neighborhood preserving embedding (NPE) algorithm. Both simulated and real data results indicate that, compared with the widely used data reduction method, principal component analysis, the NPE-based data reduction method (a) shows superior performance on efficient data reduction, while enhancing group-level information, (b) develops a unique stratagem for selecting components based on an adjacency graph of eigenvectors, (c) generates more reliable and reproducible brain networks under different model orders when the outputs of NPE are used for ICA, (d) is more sensitive to revealing task-evoked activation for task fMRI, and (e) is extremely attractive and powerful for the increasingly popular fast fMRI and very large datasets. peerReviewed

This paper proposes a Lyapunov-based economic MPC scheme for nonlinear sytems with non-monotonic Lyapunov functions. Relaxed Lyapunov-based constraints are used in the MPC formulation to improve the economic performance. These constraints will enforce a Lyapunov decrease after every few steps. Recursive feasibility and asymptotical convergence to the steady state can be achieved using Lyapunov-like stability analysis. The proposed economic MPC can be applied to minimize energy consumption in HVAC control of commercial buildings. The Lyapunov-based constraints in the online MPC problem enable the tracking of the desired set-point temperature. The performance is demonstrated by a virtual building composed of two adjacent zones.

Summary In this paper, robust containment problem is investigated for a class of multi-leader multi-agent linear systems in the presence of time-varying uncertainties. To achieve containment, a new kind of adaptive containment protocols are proposed for the multi-agent systems. Specifically, the designed protocols consist of a continuous linear term and a discontinuous term. The linear term of the designed protocol is employed to achieve containment while the discontinuous term is utilized to eliminate the effect of uncertain dynamics on the achievement of containment. By using tools from non-smooth analysis and algebraic graph theory, some efficient criteria for achieving robust containment in the closed-loop multi-agent systems are obtained and analyzed. One favorable property of the designed protocol is that containment in the closed-loop multi-agent systems can be achieved in a fully distributed fashion over any given connected and detail-balanced communication graph without using any global information about the communication graph. The effectiveness of the analytical results is finally verified by performing numerical simulations. Copyright © 2016 John Wiley & Sons, Ltd.

Summary This paper studies the robust consensus tracking problem of multiple second-order systems with additive disturbances and a direct communication topology. We design a continuous, bounded and distributed controller that is composed of a tracker and an uncertainty and disturbance estimator. The tracker makes the nominal closed-loop system globally asymptotically stable, while the output of uncertainty and disturbance estimator attenuates the effect of disturbances. We show that if the disturbances converge to constants, the tracking error converges asymptotically to zero, whereas for other types of disturbances, the obtained error system is small-signal L∞ stable. Some inequalities are developed to show the relationship between the ultimate bounds of tracking errors and the design parameters. Finally, simulation results for four cases are presented to demonstrate the performance of the controller. Copyright © 2015 John Wiley & Sons, Ltd.

Summary This paper studies a distributed coordinated control problem for a class of linear multi-agent systems subject to two types of attacks. The problem boils down to how to achieve secure consensus tracking for multi-agent systems with connected and disconnected (paralyzed) directed switching topologies caused by two types of attacks. The attacks on the edges instead of nodes lead to the loss of security performance. Two cases are studied in this paper. First, under only a class of connectivity-maintained attacks, sufficient conditions are derived to achieve secure consensus tracking in mean-square. Second, when the multi-agent systems are further subject to a class of connectivity-broken attacks, novel sufficient conditions are further obtained to ensure secure consensus tracking with a specified convergence rate by virtue of the idea of average dwell time switching between some stable and unstable subsystems. Three numerical simulations are finally given to illustrate the theoretical analysis. Copyright © 2015 John Wiley & Sons, Ltd.

Summary In this paper, we study the robust consensus tracking problem for a class of high-order multi-agent systems with unmodelled dynamics and unknown disturbances. A continuous robust state feedback control algorithm is proposed to enable the agents to achieve robust consensus tracking of a desired trajectory. By utilizing Lyapunov analysis methods and an invariance-like theorem, sufficient conditions for semi-global asymptotic consensus tracking are established. A robust output feedback control algorithm is designed to obtain a uniformly ultimately bounded consensus tracking result. Numerical simulations are provided to show the effectiveness of the proposed algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

This paper proposes a spectrum leasing strategy based on cooperative relaying for cognitive radio networks. The basic idea is to make the primary user lease a fraction of the licensed spectrum to the secondary user, which acts as a relay for the primary user in return. We formulate the spectrum leasing problem as a Nash bargaining game to avoid the ineffective solution obtained by Stackelberg game. The cooperative condition and the optimal time allocation can be obtained by solving the game. Numerical results demonstrate that both the primary user and the secondary user can obtain larger profits from the spectrum leasing based on Nash bargaining solution. Copyright © 2014 John Wiley & Sons, Ltd.

Visual servo control systems use information from images along with knowledge of the optic parameters (i.e. camera calibration) to position the camera relative to some viewed object. If there are inaccuracies in the camera calibration, then performance degradation and potentially unpredictable response from the visual servo control system may occur. Motivated by the desire to incorporate robustness to the camera calibration, different control methods have been developed. Previous adaptive/robust controllers (especially for six degree-of-freedom camera motion) rely heavily on properties of the rotation parameterization to formulate state estimates and a measurable closed-loop error system. All of these results are based on the singular axis–angle parameterization. Motivated by the desire to express the rotation by a non-singular parameterization, efforts in this paper address the question: Can state estimates and a measurable closed-loop error system be crafted in terms of the quaternion parameterization when the camera calibration parameters are unknown? To answer this question, a contribution of this paper is the development of a robust controller and closed-loop error system based on a new quaternion-based estimate of the rotation error. A Lyapunov-based analysis is provided which indicates that the controller yields asymptotic regulation of the rotation and translation error signals given a sufficient approximate of the camera calibration parameters. Simulation results are provided that illustrate the performance of the controller for a range of calibration uncertainty. Copyright © 2009 John Wiley & Sons, Ltd.