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The goal of traffic management is to efficiently allocate network resources via adjustment of source transmission rates and routes selection. Mathematically, it aims to solve a traditional utility maximization problem in a fair and distributed manner. In this paper, we first develop a generalized multi-path utility maximization problem which features a weighted average of the classical Kelly’s formulation and the Voice’s model. Next, we design from this broader framework a family of multi-path dual congestion control algorithms whose equilibrium point can both achieve a desired bandwidth utilization and preserve a notion of fairness among competing users. Global stability can be guaranteed for the proposed schemes in the absence of delays by use of a totally novel Lyapunov function. Moreover, when heterogeneous propagation delays are taken into account, we establish decentralized and scalable sufficient conditions for robust global stability by constructing a reasonable Lyapunov–Krasovskii functional candidate. These conditions give estimates for the maximum admissible delays that the controller can tolerate without losing stability. Finally, we verify the results through simulation. [ABSTRACT FROM AUTHOR]

In many practical systems, supervisory control is not performed by one centralized supervisor, but by multiple local supervisors. When communication networks are used in such a system as the medium of information transmission, the communication channels between local supervisors and the system to be controlled will unavoidably result in communication delays. This paper investigates how to use these local supervisors to control the system in order to satisfy given specifications even under communication delays. The specifications are described by two languages: a minimal required language which specifies the minimal required performance that the supervised system must have and a maximal admissible language which specifies the maximal boundary that the supervised system must be in. The results show that if the control problem is solvable, then there exists the minimal control policy which can be calculated based on state estimates. Furthermore, we derive algorithms to check whether the control problem is solvable or not. [ABSTRACT FROM AUTHOR]

This paper considers the resilient beamforming problem for a network of collaborative sensors in presence of faulty nodes. The main objective is to synchronize the signal phases at an intended destination and maximize the signal power. Towards this objective, we first propose a random grouping distributed beamforming (RG-DB) algorithm in the fault-free setup. For the RG-DB algorithm, each sensor classifies itself into one of two groups according to a random mechanism over a time horizon, during which a group of sensors control the phases of their transmissions, while the other group of sensors do not update their phases. This paper shows global convergence towards phase synchronization with probability one by applying the RG-DB algorithm. On the basis of the RG-DB algorithm, a random grouping based resilient distributed beamforming (RG-RDB) algorithm is then developed to solve the resilient beamforming problem in presence of faulty sensors. It is shown that the RG-RDB algorithm asymptotically makes the probability of selecting a faulty node for collective signal transmitting converge to zero, and thus solves the resilient beamforming problem by a subset of properly functioning sensors. Simulations are provided validating the algorithms developed in this paper. [ABSTRACT FROM AUTHOR]

The algebraic and geometric properties for the local stability crossing curves (SCCs) of systems with two free parameters have attracted considerable interest, but some fundamental issues still remain unsolved. In this paper, we will develop a systematic approach for addressing such SCCs. First, we will parametrize the local SCCs through proposing an algorithm, with which the parametrization in the general case can be obtained. It will be seen that the parametrization in the general case is subject to some systems of Puiseux series, a new notion introduced in this paper. With the systems of Puiseux series, we can further explore the algebraic as well as the geometric properties of local SCCs. Next, all possible cases regarding the systems of Puiseux series and the topological structure will be appropriately classified. The resultant topological classifications, which take into account the distribution of the characteristic roots (from the stability standpoint), are insightful for the stability and stabilization studies. Finally, the asymptotic behavior issue when the parameters move along any given curve is addressed. We will show that the asymptotic behavior, in this case, corresponds to a group of Puiseux series, from which the detailed information on the local root loci is available. Moreover, this approach helps to verify the approaches proposed previously, such that all the approaches presented in our paper can be cross-validated. [ABSTRACT FROM AUTHOR]

In Marconi et al. (2007), the theory of nonlinear Luenberger observers was exploited to prove that a solution to the asymptotic output regulation problem for minimum-phase normal forms always exists. The paper provided an existence result and a very general regulator structure, although unfortunately, no constructive method was given to design all the degrees of freedom of the regulator. In this paper, we complete this design by introducing an adaptive unit tuning the regulator online by employing system identification algorithms selecting the "best" parameters according to a certain optimization policy. Instead of focusing on a single identification scheme, we give general conditions under which an algorithm may be used in the framework, and we develop a particular least-squares identifier satisfying these requirements. Closed-loop stability results are given, and it is shown that the asymptotic regulation error is related to the prediction capabilities of the identifier evaluated along the ideal error-zeroing steady-state trajectories. [ABSTRACT FROM AUTHOR]

Port-Hamiltonian system theory is a well-known framework for the control of complex physical systems. The majority of port-Hamiltonian control design methods base on an explicit input-state-output port-Hamiltonian model for the system under consideration. However in the literature, little effort has been made towards a systematic, automatable derivation of such explicit models. In this paper, we present a constructive, formally rigorous method for an explicit port-Hamiltonian formulation of multi-bond graphs. Two conditions, one necessary and one sufficient, for the existence of an explicit port-Hamiltonian formulation of a multi-bond graph are given. We summarise our approach in an algorithm for the automated generation of an explicit port-Hamiltonian model from a given multi-bond graph. An academic example illustrates the results of this paper. [ABSTRACT FROM AUTHOR]

This paper considers the multi-agent linear least-squares problem in a server–agent network architecture. The system comprises multiple agents, each with a set of local data points. The agents are connected to a server, and there is no inter-agent communication. The agents' goal is to compute a linear model that optimally fits the collective data. The agents, however, cannot share their data points. In principle, the agents can solve this problem by collaborating with the server using the server–agent network variant of the classical gradient-descent method. However, when the data points are ill-conditioned , the gradient-descent method requires a large number of iterations to converge. We propose an iterative pre-conditioning technique to mitigate the deleterious impact of the data points' conditioning on the convergence rate of the gradient-descent method. Unlike the conventional pre-conditioning techniques, the pre-conditioner matrix used in our proposed technique evolves iteratively. We show that our proposed algorithm converges linearly with an improved rate of convergence in comparison to both the classical and the accelerated gradient-descent methods. For the special case, when the solution of the least-squares problem is unique, our algorithm converges to the solution superlinearly. Through numerical experiments on benchmark least-squares problems, we validate our theoretical findings, and also demonstrate our algorithm's improved robustness against process noise. [ABSTRACT FROM AUTHOR]

In this paper, two distributed multi-proximal primal–dual algorithms are proposed to deal with a class of distributed nonsmooth resource allocation problems. In these problems, the global cost function is the summation of local convex and nonsmooth cost functions, each of which consists of one twice differentiable function and multiple nonsmooth functions. Communication graphs of underlying multi-agent systems are directed and strongly connected but not necessarily weighted-balanced. The multi-proximal splitting is introduced to deal with the difficulty caused by the unproximable property of the summation of those nonsmooth functions. Moreover, it can also guarantee the Lipschitz continuity of proposed algorithms. Auxiliary variables in the multi-proximal splitting are designed to estimate subgradients of nonsmooth functions. Theoretically, the convergence analysis is conducted by employing Lyapunov stability theory and integral input-to-state stability (iISS) theory with respect to sets. It shows that proposed algorithms converge to the optimal point while satisfying resource allocation constraints. [ABSTRACT FROM AUTHOR]

Zeroth-order optimization (ZO) algorithms have been recently used to solve black-box or simulation-based learning and control problems, where the gradient of the objective function cannot be easily computed but can be approximated using the objective function values. Many existing ZO algorithms adopt two-point feedback schemes due to their fast convergence rate compared to one-point feedback schemes. However, two-point schemes require two evaluations of the objective function at each iteration, which can be impractical in applications where the data are not all available a priori, e.g., in online optimization. In this paper, we propose a novel one-point feedback scheme that queries the function value once at each iteration and estimates the gradient using the residual between two consecutive points. When optimizing a deterministic Lipschitz function, we show that the query complexity of ZO with the proposed one-point residual feedback matches that of ZO with the existing two-point schemes. Moreover, the query complexity of the proposed algorithm can be improved when the objective function has Lipschitz gradient. Then, for stochastic bandit optimization problems where only noisy objective function values are given, we show that ZO with one-point residual feedback achieves the same convergence rate as that of two-point scheme with uncontrollable data samples. We demonstrate the effectiveness of the proposed one-point residual feedback via extensive numerical experiments. [ABSTRACT FROM AUTHOR]

Abstract: This paper is devoted to the design of a decentralized optimal batch LQ state observer for state estimation of large-scale interconnected systems, well suited for implementation on a sensor network. The here-proposed approach relies on both the use of an augmented Lagrangian formulation and a price-decomposition–coordination algorithm. The state estimation of an open-channel hydraulic system illustrates the effectiveness of this approach and is used to provide a comparison with alternative methods. [Copyright &y& Elsevier]

Abstract: Opacity is a confidentiality property that characterizes whether a “secret” of a system can be inferred by an outside observer called an “intruder”. In this paper, we consider the problem of enforcing opacity in systems modeled as partially-observed finite-state automata. We propose a novel enforcement mechanism based on the use of insertion functions. An insertion function is a monitoring interface at the output of the system that changes the system’s output behavior by inserting additional observable events. We define the property of “i-enforceability” that an insertion function needs to satisfy in order to enforce opacity. I-enforceability captures an insertion function’s ability to respond to every system’s observed behavior and to output only modified behaviors that look like existing non-secret behaviors. Given an insertion function, we provide an algorithm that verifies whether it is i-enforcing. More generally, given an opacity notion, we determine whether it is i-enforceable or not by constructing a structure called the “All Insertion Structure” (AIS). The AIS enumerates all i-enforcing insertion functions in a compact state transition structure. If a given opacity notion has been verified to be i-enforceable, we show how to use the AIS to synthesize an i-enforcing insertion function. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of system decomposition with respect to inputs is investigated for Boolean control networks (BCNs). First, based on the linear representation of BCNs, some algebraic equivalent conditions for the decomposability are obtained. Second, the concept of perfect equal vertex partition (PEVP) is proposed for BCNs. A necessary and sufficient graphical condition for the decomposability, i.e. the existence of a PEVP, is proposed. Third, for calculating a PEVP, an algorithm called Vertex Set Uniting Algorithm (VSUA) is designed. Finally, a biological example is analyzed to show the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper deals with the optimal control of a class of positive switched systems. The main feature of this class is that switching alters only the diagonal entries of the dynamic matrix. The control input is represented by the switching signal itself and the optimal control problem is that of minimizing a positive linear combination of the final state variable. First, the switched system is embedded in the class of bilinear systems with control variables living in a simplex, for each time point. The main result is that the cost is convex with respect to the control variables. This ensures that any Pontryagin solution is optimal. Algorithms to find the optimal solution are then presented and an example, taken from a simplified model for HIV mutation mitigation is discussed. [Copyright &y& Elsevier]

Abstract: This paper studies the supervisory control of nondeterministic discrete event systems to achieve a bisimulation equivalence between the controlled system and the deterministic specification. In particular, a necessary and sufficient condition is given for the existence of a bisimilarity enforcing supervisor, and a polynomial algorithm is developed to verify such a condition. When the existence condition holds, a bisimilarity enforcing supervisor is constructed. Otherwise, two methods are provided for synthesizing supremal feasible sub-specifications. [Copyright &y& Elsevier]

Abstract: This paper presents a numerical solution for scalar state constrained optimal control problems. The algorithm rewrites the constrained optimal control problem as a sequence of unconstrained optimal control problems which can be solved recursively as a two point boundary value problem. The solution is obtained without quantization of the state and control space. The approach is applied to the power split control for hybrid vehicles for a predefined power and velocity trajectory and is compared with a Dynamic Programming solution. The computational time is at least one order of magnitude less than that for the Dynamic Programming algorithm for a superior accuracy. [Copyright &y& Elsevier]

Abstract: In this paper, the stabilizability of discrete-time linear switched systems is considered. Several sufficient conditions for stabilizability are proposed in the literature, but no necessary and sufficient. The main contributions are the necessary and sufficient conditions for stabilizability based on the set-theory and the characterization of a universal class of Lyapunov functions. An algorithm for computing the Lyapunov functions and a procedure to design the stabilizing switching control law are provided, based on such conditions. Moreover, a sufficient condition for non-stabilizability for switched system is presented. Several academic examples are given to illustrate the efficiency of the proposed results. In particular, a Lyapunov function is obtained for a system for which the Lyapunov–Metzler condition for stabilizability does not hold. [Copyright &y& Elsevier]

Abstract: The subject of this paper is identification of discrete time nonlinear dynamical systems when the system dynamics are defined by a discontinuous nonlinear function with the location of the discontinuity unknown. By representing the nonlinear function using both a parametric term to capture the continuous part and a non-parametric term to capture the discontinuous part, we present an identification algorithm along with conditions for recovery of the true nonlinearity. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. The physical spatial field of interest is discretized and modeled by a Gaussian Markov random field (GMRF) with uncertain hyperparameters. From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. The main advantages of the proposed algorithm are: (1) the computational efficiency due to the sparse structure of the precision matrix, and (2) the scalability as the number of measurements increases. Thus, the prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and is also scalable to be usable for mobile sensor networks with limited resources. We also present a distributed version of the prediction algorithm for a special case. An adaptive sampling strategy is presented for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. The effectiveness of the proposed algorithms is illustrated by numerical experiments. [Copyright &y& Elsevier]

Abstract: Input shapers with time delays have proved useful in many applications related to controls for various oscillatory devices, for example flexible manipulators and cranes. In the paper, a novel approach for designing a zero-vibration signal shaper based on equally distributed delay is proposed. The parameter assessment of the shaper is based on the spectral approach. Various characteristics of the shaper are analyzed and compared with the classical zero-vibration shaper with a lumped delay. The analysis shows that the novel shaper is a slower, but more robust alternative to the classical shaper. Besides, the discrete implementation of the shaper is proposed and tested. It includes zero placement based parameter adjustment with the objective to preserve full compensation of the oscillatory mode by the discrete algorithm. [Copyright &y& Elsevier]

Abstract: This paper considers a network of vehicles moving in a two dimensional plane. The overall network, described by a collection of double integrator dynamics, is controlled by a novel distributed static output feedback methodology to maintain a desired formation. The distributed control architecture stabilizes the network using static output feedback of position information only, by exploiting delays in communication of the relative information. An optimization algorithm, based on Linear Matrix Inequalities together with the DIRECT search algorithm, is used to synthesize the controller gains and the delay. [Copyright &y& Elsevier]

Abstract: This paper studies the identification of finite impulse response (FIR) systems with binary-valued observations. Combining with the stochastic gradient algorithm and statistical property of the system noise, a recursive projection algorithm is proposed to estimate the unknown system parameters. Under some mild conditions on the a priori knowledge of the unknown parameters and inputs, the algorithm is proved to be convergent in the almost sure and mean square sense. Furthermore, the almost sure and mean square convergence rates of estimation errors are also obtained. A numerical example is given to demonstrate the effectiveness of the algorithm and the main results obtained. [Copyright &y& Elsevier]

Abstract: In this paper, finite-time position consensus and collision avoidance problems are investigated for multi-AUV (autonomous underwater vehicle) systems. First, based on the homogeneous control method, finite-time position consensus algorithms are proposed for both leaderless and leader–follower multi-AUV systems without considering collisions between the AUVs. Specifically, in the leader–follower case, a novel distributed finite-time observer is developed for the followers to estimate the leader’s velocity. Second, by constructing collision avoidance and connectivity maintenance functions, modified consensus algorithms containing corresponding gradient terms are presented for multi-AUV systems of both cases, which guarantee collision avoidance, connectivity maintenance, velocity matching, and consensus boundedness. Simulations demonstrate the effectiveness of the proposed control algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the state estimation problem for a class of Markovian Jump Linear Systems (MJLSs) in the presence of bounded polyhedral disturbances. A set-membership estimation algorithm is first proposed to find the smallest consistent set of all possible states, which is shown to be expressed by a union of multiple polytopes. The posterior probabilities of the system jumping modes are then estimated by introducing the Lebesgue measure, based on which the optimal point estimate is further provided. Moreover, a similarity relationship for polytopes is defined and an approximate method is presented to calculate the Minkowski sum of polytopes, which can help reduce the computational complexity of the overall estimation algorithm. [Copyright &y& Elsevier]

Abstract: The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The shape of the cost function, and hence the difficulty in solving the optimization problem, depends directly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. Therefore, it seems plausible to improve the convergence to the global minimum by properly choosing the spectrum of the input; in this paper, we address this problem. We present a condition for convergence to the global minimum of the cost function and propose its inclusion in the input design. We present the application of the proposed approach to case studies where the algorithms tend to get trapped in nonglobal minima. [Copyright &y& Elsevier]

Abstract: This paper presents a distributed algorithmic solution, termed Coalition formation and deployment algorithm , to achieve network configurations where agents cluster into coincident groups that are distributed optimally over the environment. The motivation for this problem comes from spatial estimation tasks executed with unreliable sensors. We propose a probabilistic strategy that combines a repeated game governing the formation of coalitions with a spatial motion component governing their location. For a class of probabilistic coalition switching laws, we establish the convergence of the agents to coincident groups of a desired size in finite time and the asymptotic convergence of the overall network to the optimal deployment, both with probability 1. We also investigate the algorithm’s time and communication complexity. Specifically, we upper bound the expected completion time of executions that use the proportional-to-number-of-unmatched-agents coalition switching law under arbitrary and complete communication topologies. We also upper bound the number of messages required per timestep to execute our strategy. The proposed algorithm is robust to agent addition and subtraction. From a coalitional game perspective, the algorithm is novel in that the players’ information is limited to the neighboring clusters. From a motion coordination perspective, the algorithm is novel because it brings together the basic tasks of rendezvous (individual agents into clusters) and deployment (clusters in the environment). Simulations illustrate the correctness, robustness, and complexity results. [Copyright &y& Elsevier]

Abstract: This paper presents a new regularized kernel-based approach for the estimation of the second order moments of stationary stochastic processes. The proposed estimator is defined by a Tikhonov-type variational problem. It contains few unknown parameters which can be estimated by cross validation solving a sequence of problems whose computational complexity scales linearly with the number of noisy moments (derived from the samples of the process). The correlation functions are assumed to be summable and the hypothesis space is a reproducing kernel Hilbert space induced by the recently introduced stable spline kernel. In this way, information on the decay to zero of the functions to be reconstructed is incorporated in the estimation process. An application to the identification of transfer functions in the case of white noise as input is also presented. Numerical simulations show that the proposed method compares favorably with respect to standard nonparametric estimation algorithms that exploit an oracle-type tuning of the parameters. [Copyright &y& Elsevier]

Abstract: This paper proves that the optimization problem of one-step point feature Simultaneous Localization and Mapping (SLAM) is equivalent to a nonlinear optimization problem of a single variable when the associated uncertainties can be described using spherical covariance matrices. Furthermore, it is proven that this optimization problem has at most two minima. The necessary and sufficient conditions for the existence of one or two minima are derived in a form that can be easily evaluated using observation and odometry data. It is demonstrated that more than one minimum exists only when the observation and odometry data are extremely inconsistent with each other. A numerical algorithm based on bisection is proposed for solving the one-dimensional nonlinear optimization problem. It is shown that the approach extends to joining of two maps, thus can be used to obtain an approximate solution to the complete SLAM problem through map joining. [Copyright &y& Elsevier]

Abstract: In this paper, we analyze a novel algorithm for 2-D ARMA model parameter estimation in the presence of noise and then develop a fast and efficient blind image restoration algorithm. We show that the novel algorithm can minimize a quadratic convex optimization problem and has a lower computational complexity than the conventional algorithms. As a result, the novel algorithm involves no convergence and local minimum issue. Moreover, the proposed blind image restoration algorithm can overcome the local minimization problem. Computed results confirm that the novel algorithm can more quickly obtain more accurate estimates than the conventional algorithms in the presence of noise. [Copyright &y& Elsevier]

Abstract: The main result of the paper consists in necessary and sufficient graphical conditions which ensure the generic discrete mode observability of structured switching descriptor systems. The methods used in the previous studies on the observability of switching linear systems on standard form are not applicable to switching descriptor systems. So, we develop a new approach starting from bipartite representations of these systems and then building a new kind of digraph dedicated to the discrete mode observability study. The proposed method assumes only the knowledge of the system’s structure and is applicable to a large class of descriptor systems including regular and non-regular systems even if they are square or under-determined. The provided conditions can be implemented by the classical graph-theory algorithms. [Copyright &y& Elsevier]

Abstract: The parameter or state estimation with bounded noises is getting increasingly important in many applications of practical systems with some uncertainties. The problem to estimate a deterministic parameter or state which is known to lie in an intersection of some ellipsoids can be formulated to find the Chebyshev center of the intersection set in the case of norm of the estimation error. In this paper, an appropriate positive semidefinite relaxation of non-convex optimization problem is derived, and then a new algorithm for robust minimax estimation is provided. Some examples are given to compare the approximate estimate with the existing relaxed Chebyshev center. [Copyright &y& Elsevier]

Abstract: This paper considers randomized discrete-time consensus systems that preserve the average “on average”. As a main result, we provide an upper bound on the mean square deviation of the consensus value from the initial average. Then, we apply our result to systems in which few or weakly correlated interactions take place: these assumptions cover several algorithms proposed in the literature. For such systems we show that, when the network size grows, the deviation tends to zero, and that the speed of this decay is not slower than the inverse of the size. Our results are based on a new approach, which is unrelated to the convergence properties of the system. [Copyright &y& Elsevier]

Abstract: This paper generalizes the concept of the depth-independent interaction matrix, developed for point and line features in our early work, to generalized image features. We derive the conditions under which the depth-independent interaction matrix can be linearly parameterized by the geometric parameters of the generalized image features, and propose an adaptive visual servo controller for robot manipulators using the generalized image features whose geometric parameters are unknown. To estimate the unknown parameters on-line, we propose new error functions that are linear to estimation errors of the parameters and an algorithm that minimizes the error functions using multiple images. The Lyapunov theory is used to prove asymptotic stability of the proposed controller based on the nonlinear dynamics of the manipulator. It is also shown that in addition to points and lines, other common image features like distances, angles, areas, and centroids all satisfy the conditions for the linear parameterization. Experiments have been conducted to validate the proposed control method. [Copyright &y& Elsevier]

Abstract: In this paper, a distributed cooperative tracking problem is studied for a group of non-identical second-order nonlinear systems with bounded external disturbances. The control goal is to drive the states of the followers to converge to those of a time-varying leader in the presence of only local information interactions from the leader to the followers and between the followers. We first propose a simple distributed control algorithm for the case when both the relative position and relative velocity measurements are available for feedback. It is shown that distributed cooperative tracking can be achieved if in the sensing topology the leader has directed paths to all followers. Then, the extension to the case when the velocity measurements are not available is studied. We consider two different scenarios, i.e., when the absolute position measurements and local communication between neighbors are available and only the relative position measurements are available. Distributed observers and distributed control inputs are designed to address these two scenarios. Numerical studies are carried out to illustrate the theoretical results. [Copyright &y& Elsevier]

This paper is concerned with the distributed filtering issue under the Cauchy-kernel-based maximum correntropy for large-scale systems subject to randomly occurring cyber-attacks in non-Gaussian environments. The considered cyber-attacks are hybrid and consist of both denial-of-service attacks and deception attacks. The weighted Cauchy kernel-based maximum correntropy criterion instead of the traditional minimum variance is put forward to evaluate the filtering performance against non-Gaussian noises as well as cyber-attacks. Based on the matrix decomposition and the fixed-point iterative update rules, the desired filter gain related with a set of Riccati-type equations is obtained to achieve the optimal filtering performance. Then, an improved version only dependent on the local information and neighboring one-step prediction is developed to realize the distributed implementation. Furthermore, the convergence of the developed fixed-point iterative algorithm is addressed via the famous Banach fixed-point theorem. Finally, a standard IEEE 39-bus power system is utilized to show the merit of the proposed distributed filtering algorithm in the presence of cyber-attacks and non-Gaussian noises. [ABSTRACT FROM AUTHOR]

In this paper, we provide a lazy control synthesis algorithm for monotone transition systems and directed safety specifications. Two classes of monotone transition systems are presented: state monotone transition systems and input-state monotone transition systems. For the first class of systems, a partial order is defined only on the state space. For the second, the input space is ordered as well. The introduced lazy synthesis approach is based on the efficient computation of predecessors. It benefits not only from a monotone property of transition systems but also from the ordered structure of the state (input) space and the fact that directed safety specifications are considered. To enrich the class of the considered specifications, we also present an incremental controller synthesis framework, which allows us to deal with intersections of upper and lower-closed safety requirements. We then compare the proposed approach with the classical safety synthesis algorithm and illustrate the advantages, in terms of run-time and memory efficiency, on an adaptive cruise control problem. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of event-triggered H ∞ filtering for linear discrete time-varying (LDTV) systems. Using the lifting technique, we firstly establish an equivalent relationship with a certain equivalent minimum problem of indefinite quadratic form subject to LDTV systems with non-uniform sampling periods. Then, based on Krein space projection and innovation analysis, sufficient and necessary conditions for the existence of desired filter are derived and a feasible solution is obtained in terms of Riccati recursions. Thus, an algorithm based on the time-update and event-update recursions is given for the implementation of event-triggered H ∞ filtering. Different from some existing results, a new event-triggered H ∞ filtering scheme is provided so that the estimation error can be completely decoupled from the event-triggered transmission error. Moreover, the new proposed Krein space approach is less conservative and more computational attractive than the existing methods based on recursive linear inequality matrix. Finally, a numerical example is given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with parameter estimation of finite impulse response (FIR) systems with binary observations. Combining a suitable design of the time-varying thresholds, a kind of sign-error type unified algorithm with projection is investigated for either deterministic systems or stochastic systems. The convergence properties of the studied algorithm are established under bounded persistent excitations. Specifically, for the case without noise, the square convergence rate is proved to be close to O 1 k 2 with respect to the time step k. For the case with bounded noises, the upper bound of the estimation error is obtained, which depends on the bound of the noises and the lower bound of the input persistent excitation condition. For the case with independent and identically distributed (i.i.d.) stochastic noises, the estimate is shown to converge to the true parameter in the sense of mean square and almost surely. Besides, the mean square convergence rate of the estimation error is of the order O 1 k . Numerical examples are supplied to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the consensus problem of a continuous-time uncertain multi-agent system (MAS) with time-varying interval uncertainties in the system matrix and unknown initial states is investigated via output feedback. Relating only to the interval bounds on the uncertainties, a reduced-order framer, which includes two Luenberger-like observers, is first derived. Then, a distributed control algorithm relying only on the information of the framers associated with each agent and its neighbors is proposed. It is shown that the control algorithm can render the reduced-order framer to be an exponential reduced-order interval observer, and simultaneously drive the uncertain MAS to reach robust consensus exponentially. Finally, numerical simulations for a large-scale MAS are used to verify the theoretical results. [ABSTRACT FROM AUTHOR]

Jump Markov linear systems (JMLS) are a useful model class for capturing abrupt changes in system behaviour that are temporally random, such as when a fault occurs. In many situations, accurate knowledge of the model is not readily available and can be difficult to obtain based on first principles. This paper presents a method for learning parameter values of this model class based on available input–output data using the maximum-likelihood framework. In particular, the expectation–maximisation method is detailed for this model class with attention given to a deterministic and numerically stable implementation. The presented algorithm is compared to state-of-the-art methods on several simulation examples with favourable results. [ABSTRACT FROM AUTHOR]

In this paper we deal with decision-coupled problems involving multiple agents over a network. Each agent has its own local objective function and local constraints, and all agents aim at finding the value of a common decision vector that minimizes the sum of all agents' cost functions and satisfies all local constraints. To this purpose, we introduce a Proximal-Tracking distributed optimization algorithm that integrates dynamic average consensus within the proximal minimization method. Convergence to an optimal consensus solution is guaranteed for any value of a constant penalty parameter, under a convexity assumption only, without requiring differentiability, Lipschitz continuity, or smoothness of the local objective functions. Numerical simulations show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the bumpless H ∞ control problem based on exponential stability and L 2 -gain analyses for a class of periodic piecewise linear systems. A novel characterization of bumpless transfer among a variety of subsystem controllers satisfying some interpolation constraints is introduced. By utilizing adjustable interpolation functions and multiple Lyapunov functions, more flexibility is provided so as to guarantee a desirable closed-loop performance and trajectory smoothness. Since the problem formulated boils down to a feasibility problem involving nonlinear matrix inequalities, an iterative algorithm which solves a series of convex optimization problems is developed. Numerical examples are presented to show the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

The notion of security index quantifies the least effort involved in conducting perfectly undetectable attacks. Thus, the security index enables a systems operator to assess the vulnerability of a component, informs sensor placement strategies, and helps in deciding the feasibility of secure estimators and fault detectors. In this paper, we investigate the (possible) variation in this index as a consequence of variation in the system parameters. To this end, we adopt a structured systems approach, typically represented by a directed graph, with the edges of the said graph being in one-to-one correspondence with the system parameters. We first show that the security index is generic. That is, for almost all choices of edge weights, the security index of a component remains the same. We refer to such an index as the generic security index. Secondly, we derive graph-theoretic conditions (and based on those an algorithm) for computing the generic security index. Third, we provide graph-theoretic conditions for computing lower (resp. upper) bounds on the values that the security index of a component can take for all nonzero choices of the edge weights of the directed graph. Finally, we provide a brute force search method for calculating the said bounds. [ABSTRACT FROM AUTHOR]

This paper presents a new circumnavigation pattern — safe convex-circumnavigation, for the agent to enclose the targets closely, which is a pattern with better performance in harsh environments than the traditional circular circumnavigation pattern. With this new pattern, the agent circumnavigates the convex hull of multiple stationary targets with an expected enclosing distance and speed using the agent's known position as well as the bearing-only information of the targets, in addition, no collision with this convex hull happens in the whole time domain. An algorithm containing scalar estimators and a control protocol is proposed to implement this pattern. The estimators are designed to locate the targets. In the control protocol, the velocity of the agent is decomposed to one directly pointing to the convex hull and one orthogonal to its current direction. The agent is forced to circumnavigate the convex hull of the targets' estimated positions and finally circumnavigate the convex hull of the real targets because the estimators converge to the exact positions of the targets. The corresponding convergence analysis is provided subsequently. Numerical simulation and real time experiment are conducted to verify the proposed method. [ABSTRACT FROM AUTHOR]

An event-triggered smoothing problem for hidden Markov models (HMMs) is investigated in this paper. The transmission of the measurements is jointly determined by a stochastic event-triggering condition and a Gilbert–Elliott communication channel. Firstly, the event-triggered risk-sensitive smoothed estimate is characterized by constructing an augmented processing of the smoothed information state, which is given by the product of the forward recursive information state and the backward recursive information state under a reference measure. Secondly, the risk-neutral smoothed estimate (namely, the MMSE smoother) is proved to be a special case of the obtained risk-sensitive one when the risk-sensitive parameter approaches zero. The implementation issues of the obtained results are discussed by introducing an alternative smoothing algorithm that is numerically equivalent to the original algorithm. The effectiveness of the proposed results is evaluated through a numerical example and comparative simulations with a naive risk-sensitive smoother that treats unreceived information as packet dropout. [ABSTRACT FROM AUTHOR]

In this paper, a class of optimization problems is investigated, where the objective function is the sum of N convex functions viewed as local functions and the constraints are N nonidentical closed convex sets. Additionally, it is aimed to solve the considered optimization problem in a distributed manner and thus a sequence of time-varying unbalanced directed graphs is introduced first to depict the information connection topologies. Then, the novel push-sum based constrained optimization algorithm (PSCOA) is developed, where the new gradient descent-like method is applied to settle the involved closed convex set constraints. Furthermore, the rigorous convergence analysis is shown under some standard and common assumptions and it is proved that the developed distributed discrete-time algorithm owns a convergence rate of O ( ln t t ) in general case. Specially, the convergence rate of O ( 1 t ) can be further obtained under the assumption that at least one objective function is strongly convex. Finally, simulation results are given to demonstrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents the output feedback control design for a tracking problem of a small-scale helicopter in the presence of uncertainties. The dynamics of the helicopter is an underactuated mechanical system and the form of control inputs is nonaffine. A time-scale approach is suggested to cope with underactuated mechanical systems to overcome lack of the number of inputs. A newly developed dynamic inversion with projection is used to deal with nonaffine control inputs to increase the region of attraction as compared to linearized inputs, i.e., an affine control input form and to deal with actuator's constraints and peaking in estimates from an Extended High-Gain Observer. The Extended High-Gain Observer is employed to quickly estimate model uncertainties and external disturbances. The singular perturbation method is used to analyze the stability of the closed-loop system in a multi-time scale structure. Based on the stability analysis, the design procedure for the proposed algorithm is presented for practical implementation. The effectiveness of the proposed control algorithm is shown via numerical simulations as well as experimental tests with a small-scale helicopter in an outdoor environment. [ABSTRACT FROM AUTHOR]

The controller state and reference governor (CSRG) is an add-on scheme for nominal closed-loop systems with dynamic controllers which supervises the controller internal state and the reference input to the closed-loop system to enforce pointwise-in-time constraints. By admitting both controller state and reference modifications, the CSRG can achieve an enlarged constrained domain of attraction compared to conventional reference governor schemes where only reference modification is permitted. This paper studies the CSRG for systems subject to stochastic disturbances and chance constraints. We describe the CSRG algorithm in such a stochastic setting and analyze its theoretical properties, including chance-constraint enforcement, finite-time reference convergence, and closed-loop stability. We also present examples illustrating the application of CSRG to constrained aircraft flight control. [ABSTRACT FROM AUTHOR]

The minimal robust positively invariant (mRPI) set plays an important role in robust model predictive control (RMPC). However, the exact computation of the mRPI set is generally intractable. This paper proposes a novel ℋ ∞ -type RMPC approach for linear systems with additive disturbances. This approach enables users to compute an ellipsoidal RPI set that approximates the minimal one in online implementations. Considering that a bilinear matrix inequality is involved in the problem formulation, an alternating optimization method is proposed in this work to ensure that the optimization problem to be solved online is always convex. Furthermore, a novel dual-mode control strategy is introduced that consists of two modes, i.e., with, and without alternating optimizations. The "classic mode" achieves fast convergence of the control algorithm, while the "alternating mode" can precisely approximate the mRPI set. None of the proposed approaches requires any off-line design, which makes practical applications more convenient. Simulations are conducted to demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

For many years, the Simplified Refined Instrumental Variable method for Continuous-time systems (SRIVC) has been widely used for identification. The intersample behaviour of the input plays an important role in this method, and it has been shown recently that the SRIVC estimator is not consistent if an incorrect assumption on the intersample behaviour is considered. In this paper, we present an extension of the SRIVC algorithm that is able to deal with continuous-time multisine signals, which cannot be interpolated exactly through hold reconstructions. The proposed estimator is generically consistent for any input reconstructed through zero or first-order-hold devices, and we show that it is generically consistent for continuous-time multisine inputs as well. The statistical performance of the proposed estimator is compared to the standard SRIVC estimator through extensive simulations. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of learning a generalized Nash equilibrium (GNE) in strongly monotone games. First, we propose semi-decentralized and distributed continuous-time solution algorithms that use regular projections and first-order information to compute a GNE with and without a central coordinator. As the second main contribution, we design a data-driven variant of the former semi-decentralized algorithm where each agent estimates their individual pseudogradient via zeroth-order information, namely, measurements of their individual cost function values, as typical of extremum seeking control. Third, we generalize our setup and results for multi-agent systems with nonlinear dynamics. Finally, we apply our methods to connectivity control in robotic sensor networks and almost-decentralized wind farm optimization. [ABSTRACT FROM AUTHOR]