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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 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]