Search Results (38)

This paper addresses the problem of extended dissipativity analysis for uncertain neutral-type semi-Markovian jump systems. Two novel parameter-dependent, free-matrix-based integral inequalities are proposed by introducing some adjustable parameters, from which some existing integral inequalities can be covered, such as traditional free-matrix-based integral inequalities and Wirtinger-based integral inequalities. A significant advancement lies in the incomplete slack matrices, with some zero components in these inequalities removed, leading to fully coupled system information. An innovative condition for extended dissipativity is derived, specifically tailored to the systems under investigation and based on the newly formulated inequalities. To demonstrate the efficacy and superiority of the methodologies, two numerical examples are meticulously provided. Full article

This paper aims to find optimal feedback policies for the tracking control of Markovian jump Boolean control networks (MJBCNs) over a finite horizon. The tracking objective is a predetermined time-varying trajectory with a finite length. To minimize the expected total tracking error between the output trajectory of MJBCN and the reference trajectory, an algorithm is proposed to determine the optimal policy for the system. Furthermore, considering the penalty for control input changes, a new objective function is obtained by weighted summing the total tracking error with the total variation of control input. Certain optimal policies sre designed using an algorithm to minimize the expectation of the new objective function. Finally, the methodology is applied to two simplified biological models to demonstrate its effectiveness. Full article

Driven by the rapid development of modern industrial applications, multi-agent systems (MASs), integrating computational and physical resources, have become increasingly important in recent years. However, the performance of MASs can be easily compromised by malicious false data injection attacks (FDIAs) due to the inherent vulnerability of the cyber layer. This work focuses on an event-triggered framework for secure reconstruction and consensus control in MASs subject to both sensor and actuator attacks. First, we introduce a class of Takagi–Sugeno fuzzy multi-agent systems that relax the traditional Lipschitz condition and incorporate realistic system dynamics by considering parameter variations governed by Markovian jump principles. Second, an adaptive fuzzy estimator is developed for the simultaneous reconstruction of states and attacks in MASs. The derived estimates are utilized to design an attack-resilient consensus control strategy that compensates for the effects of FDIAs on the closed-loop consensus error dynamics. Meanwhile, the sufficient conditions for the convergence of both estimation and consensus errors are presented and rigorously proved. Finally, evaluation results on an experimental platform through multiple truck-trailer systems are provided to demonstrate the effectiveness and performance of the proposed approach. Full article

In modern industrial applications, production quality, system performance, process reliability, and safety have received considerable attention. This article proposes a dynamic event-triggered attack estimator for Markovian jump stochastic systems susceptible to actuator deception attacks. Utilizing the developed estimator, the presented attack-tolerant control strategy can tolerate the effects of such attacks and ensure the mean-square convergence of the overall closed-loop system. A dynamic event-triggered mechanism is implemented on the sensor side to optimize communication efficiency. To address the potential threat of deception attacks, a plug-and-play (PnP) secure monitoring and control architecture is introduced. This architecture facilitates the seamless integration of the designed attack-tolerant controller with the nominal feedback controller, thereby enhancing system security without requiring significant modifications to the existing control structure. The practicality and effectiveness of the proposed approaches are demonstrated through experimental results on a switched boost converter circuit. Full article

This paper studies the output feedback $H\infty$ control problem of event-triggered Markov-type networked control systems. Firstly, a new Lyapunov–Krasovskii functional is constructed, which contains an event-triggered scheme, Markovian jump system, and quantified information. Secondly, the upper bound of the weak infinitesimal generation operator of the Lyapunov–Krasovskii function is estimated by combining Wirtinger’s-based integral inequality and reciprocally convex inequality. Finally, based on the Lyapunov stability theory, the closed-loop stability criterion of event-triggered Markov-type networked control systems and the design method of the output feedback $H\infty$ controller for the disturbance attenuation level $\gamma$ are given in the terms of linear matrix inequalities. The effectiveness and superiority of the proposed method are verified using three numerical examples. Full article

This paper concentrates on the finite-time $H_{\infty }$ control problem for a type of stochastic discrete-time Markovian jump systems, characterized by time-delay and partly unknown transition probabilities. Initially, a stochastic finite-time (SFT) $H_{\infty }$ state feedback controller and an SFT $H_{\infty }$ observer-based state feedback controller are constructed to realize the closed-loop control of systems. Then, based on the Lyapunov–Krasovskii functional (LKF) method, some sufficient conditions are established to guarantee that closed-loop systems (CLSs) satisfy SFT boundedness and SFT $H_{\infty }$ boundedness. Furthermore, the controller gains are obtained with the use of the linear matrix inequality (LMI) approach. In the end, numerical examples reveal the reasonableness and effectiveness of the proposed designing schemes. Full article

This study addresses the problem of strictly dissipative stabilization for continuous-time Markovian jump systems (MJSs) with external disturbances and generally uncertain transition rates that contain completely unknown transition rates and their bound values. A stabilization condition is obtained to guarantee strict dissipativity for the MJSs with partial knowledge in terms of the transition rates. To reduce the conservativity of the proposed condition, we used a boundary condition related to the bounds of the transition rate with slack variables. Finally, two simulation results are presented to describe the feasibility of the proposed controller. Full article

Random terms in many natural and social science systems have distinct Markovian characteristics, such as Markov jump-taking values in a finite or countable set, and Wiener process-taking values in a continuous set. In general, these systems can be seen as Markov-process-driven systems, which can be used to describe more complex phenomena. In this paper, a discrete-time stochastic linear system driven by a homogeneous Markov process is studied, and the corresponding linear quadratic (LQ) optimal control problem for this system is solved. Firstly, the relations between the well-posedness of LQ problems and some linear matrix inequality (LMI) conditions are established. Then, based on the equivalence between the solvability of the generalized difference Riccati equation (GDRE) and the LMI condition, it is proven that the solvability of the GDRE is sufficient and necessary for the well-posedness of the LQ problem. Moreover, the solvability of the GDRE and the feasibility of the LMI condition are established, and it is proven that the LQ problem is attainable through a certain feedback control when any of the four conditions is satisfied, and the optimal feedback control of the LQ problem is given using the properties of homogeneous Markov processes and the smoothness of the conditional expectation. Finally, a practical example is used to illustrate the validity of the theory. Full article

This paper provides a detailed analysis of the output voltage/current tracking control of a PWM DCDC converter that has been modeled as a Markov jump system. In order to achieve that, a dynamic sensorless strategy is proposed to perform active disturbance rejection control. As a convex optimization problem, a novel reformulation of the problem is provided to compute optimal control. Accordingly, necessary less conservative conditions are established via Linear Matrix Inequalities. First, a sensorless active disturbance rejection design is proposed. Then, to carry out the control process, a robust dynamic observer–predictive controller approach is introduced. Meanwhile, the PWM DC-DC switching power converters are examined as discrete-time Markovian switching systems. Considering that the system is subject to modeling uncertainties, time delays, and load variations as external disturbances, and by taking partial input saturation into account, the Lyapunov–Krasovskii function is used to construct the required feasibility frame and less conservative stability conditions. As a result, the proposed design provides an efficient control strategy with disturbance rejection and time-delay compensation capabilities and maintains robust performance with respect to constraints. Finally, a PWM DC-DC power converter simulation study is performed in different scenarios, and the obtained results are illustrated in detail to demonstrate the effectiveness of the proposed approach. Full article

This work focuses on the design of a unified control law, which enhances the accuracy of both the disturbance estimation and stabilization of nonlinear T-S fuzzy semi-Markovian jump systems. In detail, a proportional-integral observer based equivalent-input-disturbance (PIO-EID) approach is considered to model and develop the controller. The PIO approach includes a variable for relaxation in the system design along with an additional term for integration to improve the flexibility of the design and endurance of the system. The proposed stability criteria are formulated in the form of matrix inequalities using Lyapunov theory and depend on the sojourn time for robust control design. Final analyses are performed using MATLAB software with simulations to endorse the theoretical findings of this paper. Full article

In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples. Full article

In a networked control system scenario, the packet dropout is usually modeled by a time-invariant (homogeneous) Markov chain (MC) process. However, from a practical point of view, the probabilities of packet loss can vary in time and/or probability parameter dependency. Therefore, to design a fault detection filter (FDF) implemented in a semi-reliable communication network, it is important to consider the variation in time of the network parameters, by assuming the more accurate scenario provided by a nonhomogeneous jump system. Such a premise can be properly taken into account within the linear parameter varying (LPV) framework. In this sense, this paper proposes a new design method of ${\mathcal{H}}_{\infty }$ gain-scheduled FDF for Markov jump linear systems under the assumption of a nonhomogeneous MC. To illustrate the applicability of the theoretical solution, a numerical simulation is presented. Full article

This paper deals with the problems of finite-time boundedness (FTB) and $H_{\infty }$ FTB for time-delay Markovian jump systems with a partially unknown transition rate. First of all, sufficient conditions are provided, ensuring the FTB and $H_{\infty }$ FTB of systems given by linear matrix inequalities (LMIs). A new type of partially delay-dependent controller (PDDC) is designed so that the resulting closed-loop systems are finite-time bounded and satisfy a given $H_{\infty }$ disturbance attenuation level. The PDDC contains both non-time-delay and time-delay states, though not happening at the same time, which is related to the probability distribution of the Bernoulli variable. Furthermore, the PDDC is extended to two other cases; one does not contain the Bernoulli variable, and the other experiences a disordering phenomenon. Finally, three numerical examples are used to show the effectiveness of the proposed approaches. Full article

This paper presents two new fault models for networked systems. These fault models are more realistic and generalized for networked systems in the sense that they can represent the effects of fault at the node and network levels. At the network layer, the uncertain effects of the network lines are modeled using a Markov chain with complex transition probabilities simultaneously with the stochastic behavior of the network using a Bernoulli process. A new output feedback-based controller, which is two-mode dependent and considers network uncertainties and output measurements for gain calculation, is presented. Using the tools of robust control and stochastic stability, linear matrix inequality-based sufficient conditions are derived. The proposed controller successfully maintains the system’s performance by tolerating the effects of simultaneous sensor and actuator faults, ensuring the stability of networked loops. Simulation results verify the applicability of the presented fault-tolerant control against multiple simultaneous faults. Full article

This paper studies the position regulation problems of an Autonomous Underwater Vehicle (AUV) subject to external disturbances that may have abrupt variations due to some events, e.g., water flow hitting nearby underwater structures. The disturbing forces may frequently exceed the actuator capacities, necessitating a constrained optimization of control inputs over a future time horizon. However, the AUV dynamics and the parameters of the disturbance models are unknown. Estimating the Markovian processes of the disturbances is challenging since it is entangled with uncertainties from AUV dynamics. As opposed to a single-Markovian description, this paper formulates the disturbed AUV as an unknown Markovian-Jump Linear System (MJLS) by augmenting the AUV state with the unknown disturbance state. Based on an observer network and an embedded solver, this paper proposes a reinforcement learning approach, Disturbance-Attenuation-net (MDA–net), for attenuating Markovian-jump disturbances and stabilizing the disturbed AUV. MDA–net is trained based on the sensitivity analysis of the optimality conditions and is able to estimate the disturbance and its transition dynamics based on observations of AUV states and control inputs online. Extensive numerical simulations of position regulation problems and preliminary experiments in a tank testbed have shown that the proposed MDA–net outperforms the existing DOB–net and a classical approach, Robust Integral of Sign of Error (RISE). Full article