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We revisit the sequential rate-distortion (SRD) tradeoff problem for vector-valued Gauss–Markov sources with mean-squared error distortion constraints. Our study is partly motivated by the question recently raised in the paper “Rate-cost tradeoffs in control” (in Proc. 54th Annu. Allerton Conf. Commun., Control, Comput., 2016, pp. 1157–1164) regarding the correctness of its solution algorithm known in the literature. We show via a counterexample that the dynamic reverse water-filling algorithm suggested by (15) of the paper “Stochastic linear control over a communication channel” (IEEE Trans. Autom. Control, vol. 49, pp. 1549–1561, 2004) is not applicable to this problem, and consequently, the closed-form expression of the asymptotic SRD function derived in (17) of the paper “Stochastic linear control over a communication channel” (IEEE Trans. Autom. Control, vol. 49, pp. 1549–1561, 2004) is not correct in general. Nevertheless, we show that the multidimensional Gaussian SRD function is semidefinite representable, and thus, it is readily computable. [ABSTRACT FROM AUTHOR]

This paper focuses on the modeling of time-varying covariance matrices using the state covariance of linear systems. Following concepts from optimal mass transport, we investigate and compare three types of covariance paths, which are solutions to different optimal control problems. One of the covariance paths solves the Schrödinger bridge problem. The other two types of covariance paths are based on generalizations of the Fisher–Rao metric in information geometry, which are the major contributions of this paper. The general framework is an extension of the approach proposed in the paper “Smooth interpolation of covariance matrices and brain network estimation” (IEEE Trans. Autom. Control), which focuses on linear systems without stochastic input The performances of the three covariance paths are compared using synthetic data and a real-data example on the estimation of dynamic brain networks using functional magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

This paper is concerned with the output feedback exponential stabilization for a one-dimensional wave equation with in-domain feedback/recirculation of a boundary velocity with a spatially constant coefficient, which is first studied in [IEEE Trans. Autom. Control, vol. 62, no. 9, pp. 4760–4767, Sep. 2017]. When there are no boundary internal uncertainty and external disturbance, it is shown that by using one displacement measurement only, the output feedback makes the closed-loop system exponentially stable, which essentially improves the result of [IEEE Trans. Autom. Control, vol. 62, no. 9, pp. 4760–4767, Sep. 2017]. When there are boundary internal uncertainty and external disturbance, using two displacement measurements only, we present an observer-based output feedback law that contains an infinite-dimensional disturbance estimator used to reject the boundary internal uncertainty and external disturbance. The resulting closed-loop system is shown to be exponentially stable and the state of all subsystem involved are uniformly stable. The Backstepping method for infinite dimensional system and active disturbance rejection control method play important roles in the design. [ABSTRACT FROM AUTHOR]

This paper addresses distributed average tracking of physical second-order agents with heterogeneous unknown nonlinear dynamics, where there is no constraint on input signals and there is no need for correct initialization. The unknown nonlinear terms in agents’ dynamics are heterogeneous, satisfying a Lipschitz-like condition that will be defined later and is more general than the Lipschitz condition. In the proposed algorithm, a control input and a filter are designed for each agent. Each agent's filter has two outputs and the idea is that the first output estimates the average of the input signals, and the second output estimates the average of the input velocities asymptotically. In parallel, each agent's position and velocity are driven to track, respectively, the first and the second outputs. Having an unknown term in agents’ dynamics necessitates designing the filters for agents. Since the nonlinear terms in agents’ dynamics can be unbounded and the input signals are arbitrary, novel time-varying gains are employed in agents’ filters and control inputs to overcome these unboundedness effects. Finally, the results are improved to achieve the distributed average tracking for a group of double-integrator agents, where there is no constraint on input signals and the filter is not required anymore. Numerical simulations are also presented to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a state-dependent control updating period strategy is proposed for use in interrupted implementation of real-time Model Predictive Control (MPC). The strategy can be used as soon as a certification bound is available for the underlying optimization algorithm. Moreover, a new fast-Gradient based certifiable algorithm is proposed with the associated certification bounds for convex generally constrained optimization problems. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

The article lists the committee and executive officers of the Institute of Electrical & Electronics Engineers (IEEE) Control Systems Society and offers information for authors about the research papers published by the society.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM PUBLISHER]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

The article presents members of the Board of Governors of the U.S. Institute of Electrical and Electronics Engineers (IEEE) J. Farrell as the president of the board, R. Beard and M. Amin as Technical Committee Chairpersons and also presents notifications for authors willing to submit their papers.