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In this paper, we analyze the behavior of stochastic approximation schemes with set-valued maps in the absence of a stability guarantee. We prove that after a large number of iterations, if the stochastic approximation process enters the domain of attraction of an attracting set, it gets locked into the attracting set with high probability. We demonstrate that the above-mentioned result is an effective instrument for analyzing stochastic approximation schemes in the absence of a stability guarantee, by using it to obtain an alternate criterion for convergence in the presence of a locally attracting set for the mean field and by using it to show that a feedback mechanism, which involves resetting the iterates at regular time intervals, stabilizes the scheme when the mean field possesses a globally attracting set, thereby guaranteeing convergence. The results in this paper build on the works of Borkar, Andrieu et al., and Chen et al., by allowing for the presence of set-valued drift functions. [ABSTRACT FROM AUTHOR]

In this note, it is shown that the results claimed in the paper —as well as the examples presented there—are incorrect. [ABSTRACT FROM AUTHOR]

In this paper, the locally fixed-time and globally fixed-time $\mathcal {H}_{\infty }$ control problems for the port-controlled Hamiltonian (PCH) systems are investigated via the interconnection and damping assignment passivity-based control (IDA-PBC) technique. Compared with finite-time stabilization, where the convergence time of the closed-loop system's states relies on the initial values, the settling time of fixed-time stabilization can be adjusted to achieve desired equilibrium point regardless of initial conditions. The concepts of fixed-time $\mathcal {H}_{\infty }$ control, fixed-time stability region (or region of attraction), and fixed-time stability boundary are presented in this paper, and the criterions of globally fixed-time attractivity of a prespecified locally fixed-time stability region are obtained. Combining the locally fixed-time stability of an equilibrium point and the globally fixed-time attractivity of a prespecified fixed-time stability region, the globally fixed-time $\mathcal {H}_{\infty }$ control problem of PCH system is effectively solved. Two novel control laws are designed to deal with the globally fixed-time $\mathcal {H}_{\infty }$ control problem, and the conservativeness in estimating the settling time is also briefly discussed. An illustrative example shows that the theoretical results obtained in this paper work very well in the fixed-time $\mathcal {H}_{\infty }$ control design for PCH systems. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems In which a prespecified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. In order to search for the optimal switching instants, the derivatives of the optimal cost with respect to the switching instants need to be known. The most. important contribution of the paper is a method which first transcribes an optimal control problem into an equivalent problem parameterized by the switching instants and then obtains the values of the derivatives based on the solution of a two point boundary value differential algebraic equation formed by the state, costate, stationarily equations, the boundary and continuity, conditions, along with their differentiations. This method is applied to general switched linear quadratic problems and an efficient method based on the solution of an initial value ordinary differential equation is developed. An extension of the method is also applied to problems with internally forced switching. Examples are shown to illustrate the results in the paper. [ABSTRACT FROM AUTHOR]

This paper examines the long-standing problem of linear quadratic regulation and stabilization for Itô stochastic systems with input delay. This problem remains a primary challenge because the separation principle does not hold for Itô stochastic systems. This paper presents a complete solution to the problem: 1) The (sufficient and necessary) solvability condition of the optimal control and the analytical controller are given based on the modified Riccati differential equation defined herein. 2) The sufficient and necessary stabilization condition in mean square sense is explored. We show that the Itô stochastic system with input delay is stabilized if and only if the modified algebraic Riccati equation developed in this paper has a unique positive-definite solution. The essential obstacle encountered in this paper concerns a Delayed Forward-Backward Stochastic Differential Equation (D-FBSDE), which is mathematical challenging. [ABSTRACT FROM PUBLISHER]

A production system is characterized by both its steady state and transient properties. While extensive research efforts have been spent in the analysis of the steady state of production systems, very few results, especially analytical ones, have been reported regarding their transient behavior. Indeed, transient behavior of production systems has significant practical and theoretical implications. A better understanding of the transient properties of production systems is critical to effective utilization of real-time production data for efficient factory floor operation and management. In the framework of serial production lines with geometric machines and finite buffers, this paper develops mathematical models for transient analysis and derives closed-form expressions for evaluating the production rate, consumption rate, work-in-process, and probabilities of machine starvation and blockage, during transients. In addition, a computationally efficient algorithm based on aggregation is developed to approximate the transient performance measures with high accuracy. Numerical experiments show that the methods developed can be applied to systems with time-varying machine parameters as well. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show how insight obtained from a mean-field model can be used to create an architecture for approximate dynamic programming (ADP) for a certain class of games comprising of a large number of agents. The general technique is illustrated with the aid of a mean-field oscillator game model introduced in our prior work. The states of the model are interpreted as the phase angles for a collection of nonhomogeneous oscillators, and in this way the model may be regarded as an extension of the classical coupled oscillator model of Kuramoto. The paper introduces ADP techniques for design and adaptation (learning) of approximately optimal control laws for this model. For this purpose, a parameterization is proposed, based on an analysis of the mean-field PDE model for the game. In an offline setting, a Galerkin procedure is introduced to choose the optimal parameters while in an online setting, a steepest descent algorithm is proposed. The paper provides a detailed analysis of the optimal parameter values as well as the Bellman error with both the Galerkin approximation and the online algorithm. Finally, a phase transition result is described for the large population limit when each oscillator uses the approximately optimal control law. A critical value of the control penalty parameter is identified: above this value, the oscillators are incoherent; and below this value (when control is sufficiently cheap) the oscillators synchronize. These conclusions are illustrated with results from numerical experiments. [ABSTRACT FROM AUTHOR]

In this paper, we study the cooperative global robust output regulation problem for a class of lower triangular nonlinear uncertain multi-agent systems. We first introduce a type of distributed internal model to convert the cooperative global robust output regulation problem into a global robust stabilization problem of a so-called augmented multi-agent system which is in the block lower triangular form. We then further show that, under a set of standard assumptions, the augmented multi-agent system can be globally stabilized via a distributed state feedback control law by developing a block backstepping technique. A special case of the main result of this paper leads to the solution of the leader-following global robust consensus problem for a class of nonlinear uncertain multi-agent systems. [ABSTRACT FROM AUTHOR]

This paper extends the result of an earlier paper on constructive robust nonlinear output feedback controller design, which is based on the use of IQCs and minimax LQG control. In this paper, in addition to copies of the nonlinearities, we include copies of the inverses of the nonlinearities in the controller. Consequently more IQCs are exploited in the design procedure, which gives a smaller bound on the closed-loop cost function than the earlier result. [ABSTRACT FROM AUTHOR]

Monotone systems comprise an important class of dynamical systems that are of interest both for their wide applicability and because of their interesting mathematical properties. It is known that under the property of quasimonotonicity time-delayed systems become monotone, and some remarkable properties have been reported for such systems. These include, for example, the fact that for linear systems global asymptotic stability of the undelayed system implies global asymptotic stability for the delayed system under arbitrary bounded delays. Nevertheless, extensions to nonlinear systems have thus far relied primarily on the conditions of homogeneity and subhomogeneity, and it has been conjectured that these can be relaxed. Our aim in this paper is to show that this is feasible for a general class of nonlinear monotone systems by deriving convergence results in which simple properties of the undelayed system lead to delay-independent stability. In particular, one of our results shows that if the undelayed system has a convergent trajectory that is unbounded in all components as $t\rightarrow-\infty$, then the system is globally asymptotically stable for arbitrary bounded time-varying delays. This follows from a more general result derived in the paper that allows to quantify delay-independent regions of attraction, which can be used to prove global asymptotic stability for various classes of systems. These also recover various known delay-independent stability results that are discussed within the paper. [ABSTRACT FROM PUBLISHER]

In this paper, the question of expected time to convergence is addressed for unbiased quantized consensus on undirected connected graphs, and some strong results are obtained. The paper first provides a tight expression for the expected convergence time of the unbiased quantized consensus over general but fixed networks. It is shown that the maximum expected convergence time lies within a constant factor of the maximum hitting time of an appropriate lazy random walk, using the theory of harmonic functions for reversible Markov chains. Following this, and using electric resistance analogy of the reversible Markov chains, the paper provides an upper bound of $O(nmD\log n)$ for the expected convergence time to consensus, where $n$, $m$ and $D$, denote, respectively, the number of nodes, the number of edges, and the diameter of the network. Moreover, the paper shows that the above upper bound is tight up to a factor of $\log n$ for some simple graphs such as line graph and cycle. Finally, the results are extended to bound the expected convergence time of the underlying dynamics in time-varying networks. Modeling such dynamics as the evolution of a time inhomogeneous Markov chain, the paper derives an upper bound for the expected convergence time of the dynamics using the spectral representation of the networks. This upper bound is significantly better than earlier ones given for different quantized consensus protocols over time-varying graphs. [ABSTRACT FROM PUBLISHER]

The scenario approach is a general methodology for data-driven optimization that has attracted a great deal of attention in the past few years. It prescribes that one collects a record of previous cases (scenarios) from the same setup in which optimization is being conducted and makes a decision that attains optimality for the seen cases. Scenario optimization is by now very well understood forconvexproblems, where a theory exists that rigorously certifies the generalization properties of the solution, that is, the ability of the solution to perform well in connection to new situations. This theory supports the scenario methodology and justifies its use. This paper considersnonconvexproblems. While other contributions in the nonconvex setup already exist, we here take a major departure from previous approaches. We suggest that the generalization level is evaluated only after the solution is found and its complexity in terms of the length of a support subsample (a notion precisely introduced in this paper) is assessed. As a consequence, the generalization level is stochastic and adjusted case by case to the available scenarios. This fact is key to obtain tight results. The approach adopted in this paper applies not only to optimization, but also to generic decision problems where the solution is obtained according to a rule that is not necessarily the optimization of a cost function. Accordingly, in our presentation we adopt a general stance of which optimization is just seen as a particular case. [ABSTRACT FROM AUTHOR]

This paper is concerned with the analysis and synthesis of interconnected systems constructed from heterogeneous positive subsystems and a nonnegative interconnection matrix. We first show that admissibility, to be defined in this paper, is an essential requirement in constructing such interconnected systems. Then, we clarify that the interconnected system is admissible and stable if and only if a Metzler matrix, which is built from the coefficient matrices of positive subsystems and the nonnegative interconnection matrix, is Hurwitz stable. By means of this key result, we further provide several results that characterize the admissibility and stability of the interconnected system in terms of the Frobenius eigenvalue of the interconnection matrix and the weighted L1- induced norm of the positive subsystems again to be defined in this paper. Moreover, in the case where every subsystem is SISO, we provide explicit conditions under which the interconnected system has the property of persistence, i.e., its state converges to a unique strictly positive vector (that is known in advance up to a strictly positive constant multiplicative factor) for any nonnegative and nonzero initial state. As an important consequence of this property, we show that the output of the interconnected system converges to a scalar multiple of the right eigenvector of a nonnegative matrix associated with its Frobenius eigenvalue, where the nonnegative matrix is nothing but the interconnection matrix scaled by the steady-stage gains of the positive subsystems. This result is then naturally and effectively applied to formation control of multiagent systems with positive dynamics. This result can be seen as a generalization of a well-known consensus algorithm that has been basically applied to interconnected systems constructed from integrators. [ABSTRACT FROM PUBLISHER]

This paper proposes a strategy to estimate the velocity and position of neighbor agents using distance measurements only. Since with agents executing arbitrary motions, instantaneous distance-only measurements cannot provide enough information for our objectives, we postulate that agents engage in a combination of circular motion and linear motion. The proposed estimator can be used to develop control algorithms where only distance measurements are available to each agent. As an example, we show how this estimation method can be used to control the formation shape and secure velocity consensus of the agents in a multi agent system. [ABSTRACT FROM PUBLISHER]

Given an unstable hybrid stochastic differential equation (SDE), can we design a feedback control, based on the discrete-time observations of the state at times $0, \tau, 2\tau, \ldots$ , so that the controlled hybrid SDE becomes asymptotically stable? It has been proved that this is possible if the drift and diffusion coefficients of the given hybrid SDE satisfy the linear growth condition. However, many hybrid SDEs in the real world do not satisfy this condition (namely, they are highly nonlinear) and there is no answer to the question, yet if the given SDE is highly nonlinear. The aim of this paper is to tackle the stabilization problem for a class of highly nonlinear hybrid SDEs. Under some reasonable conditions on the drift and diffusion coefficients, we show how to design the feedback control function and give an explicit bound on $\tau$ (the time duration between two consecutive state observations), whence the new theory established in this paper is implementable. [ABSTRACT FROM AUTHOR]

The robust control problem of a class of nonlinear systems subject to external disturbances, control gain uncertainty, and nonlinear uncertainties is investigated in this paper using a nonlinearity estimator-based control approach. Different from the existing results, the crucial but highly restrictive hypothesis on the boundedness of nonlinear uncertainties is removed from this paper by means of the tools of semiglobal stabilization. By delicately constructing a specific composite Lyapunov function for the closed-loop system as well as several useful level sets, the rigorous qualitative robustness performance is presented for the closed-loop system. Finally, an example of a single-link manipulator is utilized to demonstrate the performance specification claimed by the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper considers the quadratic control problem of discrete-time Markov jump linear systems with constraints on the norm of the state and control variables. We assume that the Markov chain parameter is not available, and instead, there is a detector, which emits signals providing information on this parameter. It is desired to derive a feedback linear control using the information provided by this detector in order to stochastically stabilize the closed-loop system, satisfy the constraints whenever the initial conditions belong to an invariant set, and minimize an upper bound for the quadratic cost. We show that a linear matrix inequality (LMI) optimization problem can be formulated in order to obtain a solution for this problem. Two other related problems, one for minimizing the guaranteed quadratic cost considering fixed initial conditions and the other for maximizing an estimate of the domain of an invariant set, can also be formulated using our LMI approach. This paper is concluded with some numerical simulations. [ABSTRACT FROM AUTHOR]

The articles in this special issue focus on security and privacy of distributed algorithms and network systems. The integration of computation, communication, and control technologies has led to the widespread emergence of large scale engineering systems. These network systems are deployed in numerous fields, including electric and smart grids, transportation and smart cities, health-care and manufacturing, and so forth. Due to the rapid growth in number of geographically deployed units, such as sensors, computers and controllers, traditional centralized control and optimization algorithms may not be efficient, robust, or even applicable at all. Instead, distributed control and optimization algorithms offer a promising and desirable approach to operate and guarantee the well functioning of network systems. The aim of this special issue is to provide a window into the recent developments of the fundamentals and applications of security and privacy of distributed algorithms in network systems. [ABSTRACT FROM AUTHOR]

This paper establishes some new results on the Bott–Duffin procedure—a fundamental result in both passive network synthesis and linear systems theory. Since its appearance in 1949, this procedure has puzzled circuit and systems researchers as the networks produced contain an apparently extravagant number of elements. In this paper, we prove that the networks obtained by the Bott–Duffin procedure contain the least number of reactive elements (six) and the least number of resistors (two) among all series-parallel networks realizing a certain type of impedance function (a biquadratic minimum function). We further show that the Bott–Duffin procedure, in combination with some simple network transformations, can be used to produce all of the series-parallel networks containing exactly six reactive elements and two resistors which realize any specified biquadratic minimum function. In the course of the argument, we prove several additional interesting results pertaining to series-parallel realizations of minimum functions. The results are motivated by the application of the inerter device to passive mechanical control. [ABSTRACT FROM AUTHOR]

This paper introduces a new multi-agent control problem, called an affine formation control problem, with the objective of asymptotically reaching a configuration that preserves collinearity and ratios of distances with respect to a target configuration. Suppose each agent updates its own state using a weighted sum of its neighbor's relative states with possibly negative weights. Then the affine control problem can be solved for either undirected or directed interaction graphs. It is shown in this paper that an affine formation is stabilizable over an undirected graph if and only if the undirected graph is universally rigid, while an affine formation is stabilizable over a directed graph in the $d$-dimensional space if and only if the directed graph is $(d + 1)$-rooted. Rigorous analysis is provided, mainly relying on Laplacian associated with the interaction graph, which contain both positive and negative weights. [ABSTRACT FROM AUTHOR]

This paper describes a new paradigm for stock trading involving the use of classical feedback controllers which are “model free” in that they use neither parameterization nor estimation of stock price dynamics. At time $t$, the control signal is the investment level $I(t)$, obtained via a mapping on the so-called gain-loss function $g(t)$. While such strategies fall under the umbrella of technical analysis, our approach differs from the literature in a fundamental way: Whereas existing work in finance involves statistical analysis via historical back-testing, our new control-theoretic paradigm aims to provide “certification theorems” giving conditions under which certain robustness properties are guaranteed with respect to benchmark classes for the time-varying stock price $p(t)$. We demonstrate our ideas using a linear feedback implementation of a new stock-trading scheme called Simultaneous Long-Short. The analysis is carried out in a so-called idealized frictionless market first using smooth prices for pedagogical purposes and then using a more realistic benchmark involving Geometric Brownian Motion. Finally, simulations are given which include real-world implementation issues. [ABSTRACT FROM AUTHOR]

The paper introduces and studies differentially positive systems, that is, systems whose linearization along an arbitrary trajectory is positive. A generalization of Perron–Frobenius theory is developed in this differential framework to show that the property induces a conal order that strongly constrains the asymptotic behavior of solutions. The results illustrate that behaviors constrained by local order properties extend beyond the well-studied class of linear positive systems and monotone systems, which both require a constant cone field and a linear state space. [ABSTRACT FROM PUBLISHER]

In this paper, we consider nonlinear stochastic systems and intersect ideas from nonlinear control theory and numerical analysis. In particular, we use the idea of relative degree. This concept guarantees smoothness properties of the output and this, in turn, allows one to establish properties that are unique to the control-theoretic perspective. The contributions of the current paper are threefold. Firstly, we define different error measures that extend the ideas of local and global approximation errors for nonlinear stochastic systems. Secondly, we demonstrate that the concept of relative degree plays a key role in obtaining higher order of accuracy for integration procedures compared to Euler-Maruyama integration. We show that a particular state-space model, named STTS model, has an improved order of accuracy when compared to an Euler-Maruyama approximation, at no significant extra computational cost. Thirdly, we show that a further approximation to the STTS model, named MSTTS model, while retaining the order of local errors, has explicit sampling zero dynamics, associated with the noise processes, that have no continuous-time counterpart. The extra zero dynamics are shown to be a function of the Euler-Frobenius polynomials. To the best of the authors' knowledge, this is the first reference to sampling zero dynamics for stochastic nonlinear systems. [ABSTRACT FROM AUTHOR]

This paper investigates the existence of nonimpulsive unique solution and stochastic stability problems for the discrete-time linear rectangular descriptor Markov jump systems at two cases. For every case, the sufficient and necessary conditions, in terms of strict linear matrix inequalities, are eventually proposed to guarantee that the discrete-time linear rectangular descriptor Markov jump systems are column (row) regular, column (row) causal, stochastically stable, and have a unique solution. In addition, some assumptions are introduced to ensure that there is no impulse in the solution at the instant when the Markov process jumps. Moreover, the concrete procedures on how to apply the results obtained in this paper are presented as well. Finally, an example is provided to demonstrate the effectiveness of the results. [ABSTRACT FROM AUTHOR]

This paper deals with optimal feedback selection problem in linear time-invariant (LTI) structured systems for arbitrary pole placement, an important open problem in structured systems. Specifically, we solve the sparsest feedback selection problem for LTI structured systems. In this paper, we consider structurally cyclic systems with dedicated inputs and outputs. For this class of systems, we prove that the sparsest feedback selection problem is equivalent to the strong connectivity augmentation problem in graph theory. We present an $O(n^2)$ algorithm to find a sparsest feedback matrix for structured systems when every state is actuated by a dedicated input and every state is sensed by a dedicated output, where $n$ denotes the number of states in the system. If the inputs and the outputs are such that not every state is actuated by a dedicated input and/or not every state is sensed by a dedicated output, then we provide an $O(n^3)$ algorithm for the sparsest feedback selection problem. These results show that sparsest feedback selection with dedicated i/o is a specific case of the optimal feedback selection problem that is solvable in polynomial time. We later analyze the sparsest feedback selection problem for structurally cyclic systems when both the input and the output are dedicated and the feedback pattern is constrained. When some of the feedback links are forbidden, we prove that the problem is NP-hard. The results in this paper along with the previously known results conclude that the optimal feedback selection problem is polynomial-time solvable only for the dedicated input–output case without forbidden feedback links and also without weights for the feedback links. [ABSTRACT FROM AUTHOR]