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Abstract: This paper exploits the concept of periodic invariance in order to derive a novel predictive control algorithm for switched systems. The offline computation here concerns the definition of sequences of switches which return the state vector to a set after a given number of moves, while an online optimization is used to improve performance by exploiting information available on the value of the state at each time instant. The algorithm is shown to have closed loop stability and to ensure that the state is steered to a bounded set of ellipsoids centered on the target state. The results of the paper are illustrated by means of a numerical example. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the problem of parameter-dependent filtering for discrete-time systems with polytopic uncertainties. The uncertain parameters are supposed to reside in a polytope. Being different from previous results in the quadratic framework, the parameter-dependent Lyapunov function is used in this paper. Both full- and reduced-order filters are designed, which guarantee the asymptotic stability and a prescribed performance level. The filter parameters can be obtained from the solution of convex optimization problems in terms of linear matrix inequalities, which can be solved via efficient interior-point algorithms. Numerical examples are presented to illustrate the feasibility and less conservativeness of the proposed method. [Copyright &y& Elsevier]

Least-squares based non-parametric estimation of the wave propagation functions of a viscoelastic material is considered in this paper. Widely used nonlinear least-squares-based algorithms are often computationally expensive and suffer from numerical problems. In this paper, we propose a class of subspace estimators which assume equidistant sensor configuration. The proposed estimator is computationally economical and numerically robust. Analytical expressions for the estimation accuracy have been derived. It is also shown that the subspace estimator achieves the optimal accuracy under the optimal weighting. The algorithm is employed on simulated data as well as on real experimental data. The results therefrom are shown to confirm the analytical results. [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, 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]

Abstract: This paper proposes a sweep coverage formulation for a multi-agent system to cover a region with uncertain workload density, and provides a decentralized coverage algorithm based on the formulation. To achieve the coverage, the covered region is divided into a finite number of stripes, and an algorithm is proposed by incorporating two operations on stripes: workload partition and sweeping. Theoretical analysis is given to estimate the error between the actual coverage time and the optimal time, and numerical analysis is provided to illustrate the proposed algorithm. [Copyright &y& Elsevier]

Abstract: DIRECT is a sample-based global optimisation method for Lipschitz continuous functions defined over compact multidimensional domains. This paper adapts the DIRECT method with a modified termination criterion for global extremum seeking control of multivariable dynamical plants. Finite-time semi-global practical convergence is established based on a periodic sampled-data control law, whose sampling period is a parameter which determines the region and accuracy of convergence. A crucial part of the development is dedicated to a robustness analysis of the DIRECT method against bounded additive perturbations on the objective function. Extremum seeking involving multiple units is also considered within the same context as a means to increase the speed of convergence. Numerical examples of global extremum seeking based on DIRECT are presented at the end. [Copyright &y& Elsevier]

Abstract: In this paper, we provide a stability analysis for linear systems which have simultaneous saturations in the states and their rates (dynamics). The stability analysis is conducted via a formulation based on the polytopic representations of two saturation functions, state and rate saturation functions. Sufficient conditions to guarantee local and global asymptotical stability of such systems are derived. Furthermore, using the invariance analysis, we estimate the domain of attraction of the system when it is locally asymptotically stable. The stability conditions are formulated as an iterative LMI (ILMI) algorithm. Finally, numerical examples are presented to validate the proposed method. [Copyright &y& Elsevier]

Abstract: This paper aims to provide a numerical algorithm able to locate all unstable poles, and therefore the characterization of the stability as a function of the delay, for a class of linear fractional-order neutral systems with multiple commensurate delays. We start by giving the asymptotic position of the chains of poles and the conditions for their stability for a small delay. When these conditions are met, the root continuity argument and some simple substitutions allow us to determine the locations where some roots cross the imaginary axis, providing therefore the complete characterization of the stability windows. The same method can be extended to provide the position of all unstable poles as a function of the delay. [Copyright &y& Elsevier]

Abstract: This paper introduces the concept and characterization of Disturbance-Dwell-Time invariance (DDT-invariance) and Constraint Admissible DDT-invariance (CADDT-invariance) for constrained systems with additive disturbance under dwell-time switching. Using the characterization, necessary and sufficient conditions for DDT-invariance and algorithms for the computation of the minimal and maximal constraint admissible convex DDT-invariant sets are provided. Numerical examples of such sets for several systems are also provided. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the partial pole assignment problem (PPAP) for high order control systems. It is shown that solving the PPAP is essentially solving a pole assignment for a linear system of a much lower order, and the robust PPAP is then concerning the robust pole assignment problem for this linear system. Based on this theory, a rather simple algorithm for solving the robust PPAP is proposed, and numerical examples show that this algorithm does lead to comparable results with earlier algorithms, but at much lower computational cost. [Copyright &y& Elsevier]

Abstract: In this paper, the distributed consensus problem for second-order continuous-time multi-agent networks with sampled-data communication is investigated. Motivated by impulsive control strategy, two kinds of impulsive distributed consensus algorithm are proposed. These algorithms only utilize the sampled information and are implemented at sampled times. By using the stability theory of impulsive systems and properties of the Laplacian matrix, necessary and sufficient conditions are obtained to ensure the consensus of the controlled networks. It is shown that the control gains, the sampling period and the eigenvalues of the Laplacian matrix of the communication graph play key roles in achieving the consensus. A numerical example is given to illustrate the results. [Copyright &y& Elsevier]

Abstract: This paper proposes a state-space solution to weight optimization problem in loop-shaping control. A pointwise-in-frequency weight optimization framework is transformed into convex optimization searches that are independent of frequency. The introduced optimization problem therefore avoids gridding of the frequency space and consequently, the inaccuracies attributed to fitting transfer functions to magnitude data. In this optimization problem, the order of the weight is specified ‘a priori’, thus facilitating the synthesis of low-order controllers, which is desirable from an implementation perspective. The proposed solution algorithm simultaneously synthesizes a robust stabilizing controller and a loop-shaping weight (pre-compensator) that maximize the robust stability margin subject to constraints on the performance and the singular values of the weight. Three numerical examples are given to illustrate the effectiveness of the technique. [Copyright &y& Elsevier]

Abstract: This paper proposes a robust output feedback model predictive control (MPC) scheme for linear parameter varying (LPV) systems based on a quasi-min–max algorithm. This approach involves an off-line design of a robust state observer for LPV systems using linear matrix inequality (LMI) and an on-line robust output feedback MPC algorithm using the estimated state. The proposed MPC method for LPV systems is applicable for a variety of systems with constraints and guarantees the robust stability of the output feedback systems. A numerical example for an LPV system subject to input constraints is given to demonstrate its effectiveness. [Copyright &y& Elsevier]

Abstract: This paper investigates the finite-time distributed consensus problem for multi-agent systems using a binary consensus protocol and the pinning control scheme. Compared with other consensus algorithms which need the complete state or output information of neighbors, the proposed algorithm only requires sign information of the relative state measurements, that is, the differences between a node’s state and that of its neighbors. This corresponds to only requiring a single-bit quantization error relative to each neighbor. This signum protocol is realistic in terms of observed behavior in animal groups, where relative motion is determined not by full time-signal measurements, but by coarse estimates of relative heading differences between neighbors. The signum protocol does not require explicit measurement of time signals from neighbors, and hence has the potential to significantly reduce the requirements for both computation and sensing. Analysis of discontinuous dynamical systems is used, including the Filippov solutions and set-valued Lie derivative. Based on the second-order information on the evolution of Lyapunov functions, the conditions that guarantee the finite-time consensus for the systems are identified. Numerical examples are given to illustrate the theoretical results. [Copyright &y& Elsevier]

Abstract: The paper is devoted to a state filtering problem of Markov jump processes given the continuous and/or counting observations. All the transition intensity matrix, observation plan and counting intensity are parameterized by a random vector with uncertain distribution on a known support set. The estimation problem is formulated in minimax settings with a conditional optimality criterion. We reduce the initial minimax problem to a dual problem of constrained quadratic optimization. The corresponding numerical algorithm of minimax filtering is presented as well as its illustrative implementation in the monitoring of a TCP link status under uncertainty. [Copyright &y& Elsevier]

Abstract: In this paper the identification of Boolean control networks is addressed. First, necessary and sufficient conditions are obtained for the identification of state equation from input-state data. Then a necessary and sufficient condition for a controllable Boolean network to be observable is presented. Based on these two results, a necessary and sufficient condition for the identification from input–output data is achieved. To practically identify the model, a numerical algorithm is proposed. Two particular cases: (i) identification of systems with a known network graph; (ii) identification of a higher order Boolean network, are also investigated. Finally, the approximate identification for large size networks is explored. [Copyright &y& Elsevier]

Abstract: Descriptor systems consisting of a large number of differential–algebraic equations (DAEs) usually arise from the discretization of partial differential–algebraic equations. This paper presents an efficient algorithm for solving the coupled Sylvester equation that arises in converting a system of linear DAEs to ordinary differential equations. A significant computational advantage is obtained by exploiting the structure of the involved matrices. The proposed algorithm removes the need to solve a standard Sylvester equation or to invert a matrix. The improved performance of this new method over existing techniques is demonstrated by comparing the number of floating-point operations and via numerical examples. [ABSTRACT FROM AUTHOR]

Abstract: Most existing prevention methods tackle the deadlock issue arising in flexible manufacturing systems modeled with Petri nets by adding monitors and arcs. Instead, this paper presents a new one based on a characteristic structure of WS3PR, an extension of System of Simple Sequential Processes with Resources (S3PR) with weighted arcs. The numerical relationships among weights, and between weights and initial markings are investigated based on simple circuits of resource places, which are the simplest structure of circular wait, rather than siphons. A WS3PR satisfying a proposed restriction is inherently deadlock-free and live by configuring its initial markings. A set of polynomial algorithms are developed to implement the proposed method. Several examples are used to illustrate them. [Copyright &y& Elsevier]

Abstract: In this paper, we revisit the synchronization problems for coupled harmonic oscillators in a dynamic proximity network. Unlike many existing algorithms for distributed control of complex dynamical networks that require explicit assumptions on the network connectivity, we show that the coupled harmonic oscillators can always be synchronized, without imposing any network connectivity assumption. Moreover, we also investigate the synchronization with a leader and show that all harmonic oscillators can asymptotically attain the position and velocity of the leader, again without any assumption on connectivity of the followers. Numerical simulation illustrates the theoretical results. [Copyright &y& Elsevier]

Abstract: In this paper we present a novel algorithm to identify LPV systems with affine parameter dependence operating under open- and closed-loop conditions. A factorization is introduced which makes it possible to form a predictor that predicts the output, which is based on past inputs, outputs, and scheduling data. The predictor contains the LPV equivalent of the Markov parameters. Using this predictor, ideas from closed-loop LTI identification are developed to estimate the state sequence from which the LPV system matrices can be constructed. A numerically efficient implementation is presented using the kernel method. It turns out that if structure is present in the scheduling sequence the computational complexity reduces even more. [Copyright &y& Elsevier]

Abstract: This paper revisits the problem of infinite horizon constrained linear quadratic regulation (LQR) for discrete-time systems. It is known that there exists a finite horizon such that the infinite horizon constrained LQR problem can be solved as a finite horizon constrained LQR problem. We first propose several algorithms to estimate the upper bound on the length of this finite horizon. Conservativeness and computational complexity of these algorithms are compared through numerical examples. [Copyright &y& Elsevier]

Abstract: This paper presents a new numerical algorithm based on interval analysis able to prove that a differentiable function is injective. This algorithm also performs a partition of the domain in subsets where, for all , the cardinality of is constant. In the context of parameter estimation, we show how this algorithm provides an efficient and numerical method to study the structural identifiability of parametric models. [Copyright &y& Elsevier]

Abstract: A direct algorithm to estimate continuous-time ARMA (CARMA) models is proposed in this paper. In this approach, we first pass the observed data through an input-to-state filter and compute the state covariance matrix. The properties of the state covariance matrix are then exploited to estimate the half-spectrum of the observed data at a set of user-defined points on the right-half plane. Finally, the continuous-time parameters are obtained from the half-spectrum estimates by solving an analytic interpolation problem with a positive real constraint. As shown by simulations, the proposed algorithm delivers much more reliable estimates than indirect modeling approaches, which rely on estimating an intermediate discrete-time model. [Copyright &y& Elsevier]

The conventional way to treat integral quadratic constraint (IQC) problems is to transform them into semi-definite programs (SDPs). SDPs can then be solved using interior point methods which have been proven efficient. This approach, however, is not always the most efficient since it introduces additional decision variables to the SDP, and the additional decision variables sometimes largely increase the complexity of the problem. In this paper, we demonstrate how to solve IQC problems by other alternatives. More specifically, we consider two cutting plane algorithms. We will show that in certain cases these cutting plane algorithms can solve IQC problems much faster than the conventional approach. Numerical examples, as well as some explanations from the point of view of computational complexity, are provided to support our point. [Copyright &y& Elsevier]