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A new robust Kalman filter (KF) based on mixing distribution is presented to address the filtering issue for a linear system with measurement loss (ML) and heavy-tailed measurement noise (HTMN) in this paper. A new Student's t -inverse-Wishart-Gamma mixing distribution is derived to more rationally model the HTMN. By employing a discrete Bernoulli random variable (DBRV), the form of measurement likelihood function of double mixing distributions is converted from a weighted sum to an exponential product, and a hierarchical Gaussian state-space model (HGSSM) is therefore established. Finally, the system state, the intermediate random variables (IRVs) of the new STIWG distribution, and the DBRV are simultaneously estimated by utilizing the variational Bayesian (VB) method. Numerical example simulation experiment indicates that the proposed filter in this paper has superior performance than current algorithms in processing ML and HTMN. [ABSTRACT FROM AUTHOR]

In this paper, we consider the numerical solution of a large complex linear system with a saddle-point form obtained by the discretization of the time-harmonic eddy-current optimal control problem. A new Schur complement is proposed for this algebraic system, extending it to both the block-triangular preconditioner and the structured preconditioner. A theoretical analysis proves that the eigenvalues of block-triangular and structured preconditioned matrices are located in the interval [1/2, 1]. Numerical simulations show that two new preconditioners coupled with a Krylov subspace acceleration have good feasibility and effectiveness and are superior to some existing efficient algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the asymptotic consensus control of multi-agent systems with general linear agent dynamics is investigated. A neighbor-based adaptive event-triggering strategy with a dynamic triggering threshold is proposed, which leads to a fully distributed control of the multi-agent system, depending only on the states of the neighboring agents at triggering moments. By using the Lyapunov method, we prove that the states of the agents converge asymptotically. In addition, the proposed event-triggering strategy is proven to exclude Zeno behavior. The numerical simulation results illustrate that the agent states achieve consensus in sense of asymptotic convergence. Furthermore, the proposed strategy is shown to be scalable in case of variable agent numbers. [ABSTRACT FROM AUTHOR]

The effectiveness of this paper lies in presenting a new solution for the singular fractional two-dimensional linear continuous-time systems using the conformable derivative and Sumudu transform. The proposed technique combines the new advantageous features of conformal derivative and double-delta-Kronecker, which efficiently handles singularities and Sumudu transform, and provides an efficient solution for 2D singular Fornasini–Marchesini fractional models. Applying these approaches, we then derive new explicit expressions for the fundamental matrices of the considered model. The applicability and usefulness of our proposed methods are validated and evaluated by numerical simulations in order to show the accuracy of the obtained results. [ABSTRACT FROM AUTHOR]

Large-scale sparse linear equation solver plays an important role in both numerical simulation and artificial intelligence, and sparse triangular equation solver is a key step in solving sparse linear equation systems. Its parallel optimization can effectively improve the efficiency of solving sparse linear equation systems. In this paper, we design and implement a parallel algorithm for solving sparse triangular equations in combination with the features of the new generation of Sunway architecture, and optimize the access and communication respectively for 949 real equations and 32 complex equations in the SuiteSparse collection. The solution efficiency of the algorithm presented in this paper outperforms the cuSparse algorithm on NVIDIA V100 GPU platforms in more than 71% of the cases, and the speedup is even better in solving larger cases (matrix size greater than 10,000): our method increases the speedup from 1.29 time of the previous version to an average speedup of 5.54 and the best speedup of 32.18 over the sequential method on the next generation of Sunway architecture when using 64 slave cores. [ABSTRACT FROM AUTHOR]

Benefiting from the input-to-state stability (ISS) version of the Razumikhin stability theorem, this paper deals with the stabilization of continuous linear input-delay systems with state quantization and without any knowledge about the delay. An event-triggered delay independent truncated predictor feedback (ET-DITPF) controller with a switched feedback control law is proposed to guarantee the globally uniformly asymptotic stability (GUAS)/globally uniformly exponential stability (GUES) of the closed-loop system. As a prominent feature, this controller uses no information about the time delay (neither its value nor its upper bound). In the proposed event-triggered framework, a lower bound for each two consecutive events is derived to show the non-occurrence of the Zeno behavior. Numerical simulations confirm the effectiveness of the obtained theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we study the asymptotic stability of a particular class of linear 2D discrete Fornasini-Marchesini models. The solutions of the model can be expressed in terms of doubly indexed sequences in a simple way only when the matrices describing the model commute. In this situation, we are able to analyse directly the limit of all the trajectories. By doing so, we propose the first necessary and sufficient condition for asymptotic stability of Fornasini-Marchesini matrix models. This condition is not computationally challenging as it is ultimately based on the eigenvalues of the matrices describing the model. We support our result with numerical simulations. [ABSTRACT FROM AUTHOR]

This paper addresses the qualitative theory of mixed-order positive linear coupled systems with bounded or unbounded delays. First, we introduce a general result on the existence and uniqueness of solutions to mixed-order linear systems with time-varying delays. Next, we obtain necessary and sufficient criteria which characterize the positivity of mixed-order delay linear coupled systems. Our main contributions are in Section 5. More precisely, by using a smoothness property of solutions to fractional differential equations and developing a new appropriated comparison principle for solutions to mixed-order delay positive systems, we prove the attractivity of mixed-order non-homogeneous linear positive coupled systems under the impact of bounded or unbounded delays. We also establish a necessary and sufficient condition to| ensure the stability of homogeneous systems. As a consequence of these results, we show the smallest asymptotic bound of solutions to mixed-order delay positive non-homogeneous linear coupled systems where disturbances are continuous and bounded. Finally, we provide numerical simulations to illustrate the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers mechanical systems with linear velocity forces and highly non-linear positional forces containing distributed-delay terms. Asymptotic stability conditions of system equilibria are proved using Lyapunov's direct method and the decomposition method. The developed approaches are applied to the monoaxial stabilization of a solid body. The theoretical outcomes are confirmed by computer simulation results. [ABSTRACT FROM AUTHOR]

This paper is concerned with prescribed-time functional unknown input observer (PTFUIO) design for linear descriptor systems. First, by constructing two identical asymptotic convergence functional observers, a new PTFUIO is developed which can achieve an exact estimation within an arbitrarily prescribed time, where both the estimation accuracy and the convergence time pre-definition are independent of the initial conditions of the original system. Also, the necessary and sufficient conditions for the existence of such the PTFUIO are given in terms of the original system matrices which are easy to examine. Finally, numerical simulations are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Filippov systems have found applications in various fields. This paper mainly studies five Lotka–Volterra models of Filippov type, including a competitive system with linear interaction between two species, a competitive system with Holling type II or type III functional response and a symbiosis system with Holling type II or type III functional response. We investigate the stability of all equilibria and the boundary equilibrium bifurcations of these systems, either a persistence bifurcation or a nonsmooth fold bifurcation. We present the numerical simulation results for each case. Consequently, based on both theory and simulation, we analyze the ecological aspects under the intervention of harvesting at different prescribed thresholds. [ABSTRACT FROM AUTHOR]

In this paper, the optimal control problem for unknown continuous-time linear periodic (CTLP) systems is investigated. A model-based bias-policy iteration algorithm for CTLP systems with its convergence proof is established firstly. Based on that, the data-driven implementation is proposed to approximate the optimal controller without the knowledge of system dynamics. Compared with the existing methods, the advantages of the proposed method are that, the initial stabilizing controller requirement is relaxed, and the approximate optimal controller can be obtained without solving nonlinear differential equations. The numerical simulation verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates the theoretical requirements for conformable fractional-order linear systems to satisfy the fundamental principles of invariant subspaces, which are the foundation of geometric control theory, for the first time. The problem of exact disturbance decoupling in conformable fractional linear systems is tackled using newly developed geometric tools. Moreover, a set of necessary and sufficient conditions has been established to address this problem, which, at the same time, stabilizes the compensated system for any initial condition in the presence of unknown disturbances. We also show that employing the conformable fractional model as a synthesis model for geometric geometric decoupling control provides a better understanding of the structural system-theoretic characteristics of fractional systems, while upholding the fundamental properties of integer systems. To illustrate the applicability and effectiveness of the theoretical findings, two numerical simulations, including an application to an active suspension system, were carried out and compared with those of the Caputo derivative. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimal coordination (DOC) problem for multiple heterogeneous linear systems over strongly connected communication networks. By exploiting output regulation techniques, constructing a Laplacian matrix based on the outdegree, and assigning each agent an additional scalar state variable, a new DOC algorithms is developed, which can solve the DOC problem using the outdegree information of all agents. In contrast to the existing results, this paper presents a new technique to solve the DOC problem for multiple heterogeneous linear systems over weighted-unbalanced communication networks. Finally, numerical simulations are presented to illustrate the effectiveness of the designed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the practical fixed-time event-triggered consensus problem of multi-agent nonholonomic systems with dynamic disturbances. The continuous communications can be avoided in the update of controller. By converting the nonholonomic chained systems into two subsystems, a switching control algorithm was proposed to deal with the coupling problem in systems. Compared with the event-triggered fixed-time consensus problem of first-order or second-order linear systems, the research of nonholonomic chained systems is more challenging due to the inherent nonlinearity of these systems. Moreover, the convergence time of fixed-time consensus problem is independent of initial states, which is more practical than finite-time consensus problem. Furthermore, it is proved that there is no Zeno behavior under the fixed-time event-triggered consensus control strategies. The effectiveness of the proposed control algorithms is verified by numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, the Chebyshev polynomials method is applied to solve a space-time variable fractional order integro-differential equation. Using operational matrices of Chebyshev polynomials furnished from the Caputo–Prabhakar sense and also suitable collocation points, the variable fractional order integro-differential equation would be converted to the system of algebraic equations. The main aim of the Chebyshev polynomials method is to derive four kinds of operational matrices of Chebyshev polynomials. With such operational matrices, an equation is transformed into the products of several dependent matrices, which can also be viewed as the system of linear equations after dispersing the variables. An error bound is proved for the approximate solution obtained by the proposed method. Finally, some numerical examples are presented to demonstrate the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with event-triggered and guaranteed cost finite-time control for switched systems with dynamic quantized output feedback and exogenous disturbances. First, one common strategy is superseded by one mode-dependent event-triggered strategy, which means each subsystem possesses its own event-triggered substrategy. Second, considering the hardship of obtained system state information, a dynamic quantized output feedback controller is used to stabilize the switched system. Third, by using the methods of multiple Lyapunov functionals, free-weighting matrices and the average dwell time for the switched system, the sufficient condition of finite-time boundedness (FTB) is obtained for the switched system; and a synthesis procedure is provided to obtain the dynamic output feedback controller gain, event-triggered strategy parameters and an upper bound of the quadratic performance function. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For the recently developed quasi-reversible BCH codes with long lengths and high error-correcting capability, this paper is aimed at proposing a new and faster decoding procedure. It consists of four steps: 1) compute the consecutive syndromes; 2) calculate the syndrome functions by the forward and backward recursions; 3) solve a linear subsystem together with one matrix multiplication in order to find an error-locator polynomial; 4) determine the errors from the obtained polynomial by using the root-finding algorithm. This procedure, especially in Steps 2 and 3, differs greatly from the conventional procedures, which determine an error-locator polynomial directly from solving a linear system with the aid of the consecutive syndromes. The key idea behind this decoding technique is that the computational complexity of such a small subsystem instead of an originally large linear system can be significantly reduced, although there are additional forward and backward syndrome calculations with low complexity increasing. Finally, the illustrative examples and numerical simulations can be helpful to demonstrate the accuracy and efficacy of the presented decoding technique at different error-correcting capabilities. [ABSTRACT FROM AUTHOR]

In this paper, a new method to manage the stabilization and control problems of n-dimensional linear systems plus dead time, which includes one, two, or three unstable poles, is proposed. The control methodology proposed in this work is an Observer-based Proportional-Integral-Derivative (PID) strategy, where an observer and a PID controller are used to relocate the original unstable open-loop poles to stabilize the resultant closed-loop system. The observer provides an adequate estimation of the delayed-free variables and the PID uses the delay-free variables estimated by the proposed observer. Also, step-tracking is achieved in the overall control scheme. Necessary and sufficient conditions are presented to ensure closed-loop stability based on the open loop parameters of the system. The observer-based PID strategy considers five to seven constant parameters to obtain a stable closed-loop system. A general procedure to implement the proposed control strategy is presented and its performance is evaluated by means of numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the cooperative output regulation problem for a class of heterogeneous linear multi-agent systems under undirected communication topological graph. First, an adaptive event-triggered protocol is introduced to achieve the consensus problem for leader-following system. Then, two fully distributed event-triggered adaptive control protocols are designed. That is, the state feedback and dynamic output feedback controllers, which does not need global information about network topology of system. Under the proposed two control protocols, the cooperative output regulation problem can be solved and no Zeno behavior happens for each agent. Finally, a numerical simulation is used to corroborate the validity of the conclusions under two control laws. Moreover, a comparative experiment is carried out to highlight the advantages of the control protocol designed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of constraint management in Linear Periodic (LP) systems using Reference Governors (RG). First, we introduce the periodic-invariant maximal output admissible sets for LP systems. We extend the earlier results in the literature to Lyapunov stable LP systems with output constraints, which arise in RG applications. We show that, while the invariant sets for these systems may not be finitely determined, a finitely-determined inner approximation, which is periodically invariant, can be obtained by constraint tightening. We then analyze the geometric and algebraic relationship between these sets and show that these sets are related via simple transformations, implying that it suffices to compute only one of them for real-time applications. This greatly reduces the memory burden of RG (or other similar constraint management strategies), at the expense of an increase in processing requirements. We present a thorough analysis of this trade-off. In the second part of this paper, we present two RG formulations, and discuss their properties and algorithms for their computation. Numerical simulations demonstrate the efficacy of the approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the development and application of optimally efficient numerical methods for the simulation of vascular tumour growth. This model used involves the flow and interaction of four different, but coupled, phases which are each treated as incompressible fluids, Hubbard and Byrne (2013). A finite volume scheme is used to approximate mass conservation, with conforming finite element schemes to approximate momentum conservation and an associated equation. The principal contribution of this paper is the development of a novel block preconditioner for solving the linear systems arising from the discrete momentum equations at each time step. In particular, the preconditioned system has both a solution time and a memory requirement that is shown to scale almost linearly with the problem size. [ABSTRACT FROM AUTHOR]

In this paper, the notion of sign-consensus for the Lipschitz nonlinear multi-agents over a signed graph is introduced and controller design conditions are provided for realizing consensus among signs of states of agents. The previous works on sign-consensus are limited to the linear systems only. The attainment of sign-consensus for the nonlinear multi-agent systems is a non-trivial control problem, for which Lipschitz nonlinear dynamics are reformulated using the linear parameter varying (LPV) transformation. To guarantee the sign-consensus in a network of multiple agents, fully-distributed adaptive protocol is applied using reformulated Lipschitz nonlinear dynamics, and two design conditions are provided based on the Lipschitz constant oriented and LPV-based approaches. The first approach is comparatively less computationally complex while LPV approach holds significance for developing less conservative sign-consensus control scheme. To the best of our knowledge, distributed sign consensus methods for Lipschitz systems, based on LPV formulation, have been analyzed for the first time. The proposed approach is adaptive and can be regarded as fully-distributed, as it does not require the central knowledge of graph properties. Further, results are extended for nonlinear systems undergoing disturbances and ultimate boundedness of sign-consensus error is guaranteed. Numerical simulations on groups of Chua's circuits and mechanical oscillators are presented to validate the results. [ABSTRACT FROM AUTHOR]

In this paper, a novel event-triggered observer is proposed to decrease the information exchange in continuous-time linear networked systems. The structure of the proposed observer consists of two main parts. The first one, called the event-triggered mechanism (ETM), is placed by the system and is used to determine when the measured output of the system must be sent through the network. The ETM consists of a traditional Luenberger observer, an event-triggered local observer, and an event-detector. The second part is a copy of the event-triggered local observer which is used to estimate the system state using the transmitted data from the sensors. Under the observability assumption, it is proved that the error between the system state and its estimation is asymptotically limited to a predetermined bound. Moreover, it is guaranteed that the Zeno behaviour is avoided. Numerical simulations are provided to demonstrate the design procedure of the proposed event-triggered observer. [ABSTRACT FROM AUTHOR]

In this study, a class of linear system, which is with no exogenous input and suffered from constant measurement delay and uncertain model-parameter errors, is under consideration. To combat both the parametric uncertainties and constant measurement delay, a novel robust state estimator is proposed. Accounting for the constant measurement delay, a clever approach is utilised to expand the state vector and the system model is converted into an augmented delay-free model. Considering the deterioration of estimation performance caused by stochastic model-parameter errors, the sensitivity penalisation function of model-parameter errors is defined and introduced into the objective function of the regularised least-squares (RLS) problem, whose solution is the standard Kalman filter. Furthermore, by restricting the range of introduced parameter, the objective function of the modified RLS problem is converted into a strict convex function. Then, the recursive procedure of the proposed estimator is derived. The asymptotic stability conditions of the proposed estimator and the conditions for boundness of the estimation error matrix are given. Numerical simulations show the effectiveness of the estimator proposed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a self-reaction point absorber (PA) wave energy converter is studied, where wave energy is collected by the relative motion of the on-board plate and the buoy. The purpose of this study is to increase the relative displacement to obtain the maximum power. A semi-active inerter (SAI) is applied in the system, which is controlled by a force-tracking (FT) strategy. Two parts are required in the FT strategy: a target active control law; and a proper control law to adjust the inertance to track the active force (AF). The target control law is obtained by the full-state feedback of the state-space model of a two-body PA. The control law to adjust the inertance is to saturate the AF between the maximal and minimal semi-active force of an SAI. Both linear time-invariant system and linear time-varying system are studied. Power absorption is improved in both two systems by the application of SAIs. Moreover, the influence of dimensionless parameters on the ratio of power absorption improvement in the considered linear time-varying system is studied. Numerical simulation shows that the ratio of power absorption improvement is most sensitive to the changes of mass ratio of the on-board plate and the buoy. [ABSTRACT FROM AUTHOR]

In this paper, a discrete active disturbance rejection control (DADRC) scheme is proposed to manipulate linear input–output systems with zero dynamics. The DADRC is formulated according to the relative degree of the system output and is designed to track a reference and cancel both internal uncertainties and external disturbances. A parameter tuning method is also presented based on a part of the model information. The robustness and effectiveness of the designed scheme are demonstrated via numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates controllability and observability of multi-agent systems, in which all the agents adopt identical general linear dynamics and the interconnection topologies are switching. For controllability, criteria are established by virtue of the switching sequence and the constructed subspace sequence, respectively. Furthermore, controllability is considered from the viewpoint of graph theory, and distance partition and almost equitable partition are introduced into switching topologies to quantitatively analyse the controllable state set of the system. For observability, sufficient and/or necessary conditions are presented in terms of the system matrices and the associated invariant subspace. Finally, some numerical simulations are worked out to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

We present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in decoupled update steps. Because in the uncertain-input setting neither the marginal likelihood nor the posterior distribution of the unknowns is tractable, we develop an approximation approach based on variational Bayes. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as Hammerstein models, blind system identification, and cascaded linear systems. This connection allows us to build a systematic procedure that applies effectively to all the aforementioned problems, as shown in the numerical simulations presented in the paper. [ABSTRACT FROM AUTHOR]

Abstract Dealing with high-dimensional dataset has always been an important problem and feature selection is one of useful tools. In this paper, we develop a new filter based supervised feature selection method by combining Trace Ratio Criterion of Linear Discriminant Analysis (TRC-LDA) and group sparsity regularization. The filter based supervised feature selection method is a classifier-independent method while the TRC-LDA criterion is a recently developed criterion for dimensionality reduction that can well preserve discriminative information of dataset. However, there are seldom methods by utilizing TRC-LDA criterion for feature selection. On the other hand, imposing the l2,0-norm to the projection matrix of TRC-LDA will force some rows in it to be zero while keep other rows nonzero making the index of nonzero rows to be the selected features, however, l 2,0 -nom minimizing problem is NP-hard and intractable. To solve the above problem, in this paper, we develop a new method, namely, Trace Ratio Criterion Discriminative Feature Selection (TRC-DFS), for feature selection. The proposed TRC-DFS has imposed l 2,1 -norm, i.e. an approximation of l 2,0 -norm, to the projection matrix W of TRC-LDA to achieve feature selection. As a result, the proposed TRC-DFS can both achieve feature selection as well as capture the discriminative structure of data. We also extend the proposed method with l 2, p -norm (0 < p < 1) regularization to grasp more sparse property and develop an iterative approach to calculate the optimal solution, which is rigorously proved to be converged. Extensive simulations based on several real-world datasets verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract Twin extreme learning machine (TELM) is an efficient and effective method for pattern classification, based on widely known extreme learning machine (ELM). However, TELM is mainly used to deal with supervised learning problems. In this paper, we extend TELM to handle semi-supervised learning problems and propose a novel Laplacian twin extreme learning machine (LapTELM), which simultaneously trains two related and paired semi-supervised ELMs with two nonparallel separating planes for the final classification. The proposed method exploits the geometry structure property of the unlabeled samples and incorporates it as a manifold regularization term. This allows LapTELM to reap the benefits of fully exploring the plentiful unlabeled samples while retaining the learning ability and efficiency of TELM. Moreover, the paper shows that semi-supervised and supervised TELM can form an unified learning framework. Compared with several mainstream semi-supervised learning methods, the experimental results on the synthetic and several real-world data sets verify the effectiveness and efficiency of LapTELM. [ABSTRACT FROM AUTHOR]

In this paper, we aim to introduce an innovative non-asymptotic and robust estimation method based on the observable canonical form by means of a set of auxiliary modulating dynamical systems. The latter auxiliary systems are given by the controllable canonical form with zero initial condition. The proposed method can be applied to many kinds of linear and nonlinear systems. In this paper, it is applied to estimate the states and the output's derivatives for linear singular systems with multiple inputs and multiple outputs in noisy environment. First, the considered singular system is transformed into a form similar to the Brunovsky's observable canonical form with the injection of the inputs' and outputs' derivatives. Second, algebraic integral formulas are obtained both for the state variables and the outputs' derivatives. After giving solutions of the required auxiliary systems, error analysis in discrete noisy case is addressed, where a provided noise error bound can be used to select design parameters. In the end, numerical simulations are given to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The concepts of convolution and deconvolution are well known in the field of physical measurement. In particular, they are of interest in the field of metrology, since they can positively influence the performance of the measurement. Numerous mathematical models and computer developments dedicated to convolution and deconvolution have emerged, enabling a more efficient use of experimental data; this in sectors as different as biology, astronomy, manufacturing and energy industries. The subject finds today a new topicality because it has been made accessible to a large public for applications such as processing photographic images. The purpose of this paper is to take into account some recent evolutions such as the introduction of convolution methods in international test standards. Thus, its first part delivers a few reminders of some associated definitions. They concern linear systems properties, and integral transforms. If convolution, in most cases, does not create major calculation problems, deconvolution on the contrary is an inverse problem, and as such needs more attention. The principles of some of the methods available today are exposed. In the third part, illustrations are given on recent examples of applications, belonging to the domain of electrical energy networks and photographic enhancement. [ABSTRACT FROM AUTHOR]

This paper presents the development of a data-based model predictive control method for a semi-active suspension system with air springs and continuous damping. A continuous damping controller (CDC) has been devised for the system to alter its damping coefficient in real-time, capable of reducing the impact of external road disturbances. In this research, the damping force has been split up into a nominal force and a controllable additional force, allowing the system to be modelled as a linear time-invariant system, despite the inherent nonlinearity of air spring suspension systems. In consideration of such constraints given by the damper, a model predictive controller (MPC) has been devised with the goal of improving ride comfort. Additionally, Gaussian process regression (GPR) has been used to compensate for output estimation errors arising from model parameter uncertainties. The semi-active suspension system also featured a multichamber air spring with three available modes of stiffness. Hence, a stiffness controller has been designed to select an appropriate mode based on the predicted vehicle states given by the MPC using road preview information and a reduced full-car model. The proposed algorithm has been verified using computer simulations. The results showed that compensations for model errors made with GPR significantly improves ride comfort even in the presence of parameter uncertainties. Additionally, both the damping controller and stiffness mode selector were successfully implemented into an actual test vehicle. Vehicle test results showed the proposed algorithm to be robust and effective in enhancing ride comfort and reducing vehicle pitch motion. [ABSTRACT FROM AUTHOR]

This paper studies a class of distributed online convex optimization problems for heterogeneous linear multi-agent systems. Agents in a network, knowing only their own outputs, need to minimize the time-varying costs through neighboring interaction subject to time-varying coupled inequality constraints. Based on the saddle-point technique, we design a continuous-time distributed controller which is shown to achieve constant regret bound and sublinear fit bound, matching those of the standard centralized online method. We further extend the control law to the event-triggered communication mechanism and show that the constant regret bound and sublinear fit bound are still achieved while reducing the communication frequency. Additionally, we study the situation of communication noise, i.e., the agent's measurement of the relative states of its neighbors is disturbed by a noise. It is shown that, if the noise is not excessive, the regret and fit bounds are unaffected, which indicates the controller's noise-tolerance capability to some extent. Finally, a numerical simulation is provided to support the theoretical conclusions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the output-feedback consensus problem of linear multi-agent systems on undirected graphs. Our focus is on reducing sensing and communication burdens between agents. Based on relative output measurements, a dynamic output-feedback (DOF) protocol is proposed, which, by leveraging the structure of the closed-loop dynamics, integrates a hybrid time/event-triggered sampling mechanism such that only intermittent interaction between agents is needed. Consensus conditions are established by the Lyapunov method and numerical simulations are provided for illustrating the effectiveness of the protocol. Compared with the existing results, the proposed DOF protocol has the following merits: (1) they rely on relative output measurements but do not need absolute measurements about agents; (2) they only require agents to intermittently interact with neighbors and thus can greatly reduce sensing and communication burdens between agents; and (3) the designed event-triggering conditions do not need to perform extra reconstruction operations that are computationally demanding. [ABSTRACT FROM AUTHOR]