In this paper, the finite-frequency fault detection filter design problem is investigated for a class of networked nonlinear systems subject to medium access constraints. The nonlinear system is modeled by Takagi–Sugeno (T–S) fuzzy affine dynamic models, and only one node that includes partial measured information can gain access to the shared transmission medium according to the allocated access probability. Within the stochastic $\scr{H}\_\;/\scr{H}_\infty$ filtering framework, an admissible filter is designed such that, in the finite-frequency domain, the filtering error system is stochastically stable and the fault can be detected using partially available measurements. First, by integrating with S-procedure, the generalized Kalman–Yakubovic–Popov (KYP) lemma is further developed to obtain sufficient conditions guaranteeing the desired finite-frequency performance of the filtering error system. Then, by applying piecewise quadratic Lyapunov functions (PQLFs), Projection lemma, and some con-vexification techniques, the filter design approach is proposed for the constrained networked nonlinear system. It is shown that the filter design problem can be addressed by solving a set of linear matrix inequalities (LMIs). Simulation studies are finally given to illustrate the effectiveness of the proposed design approach.

This paper investigates the problem of asynchronous fault detection filtering design for continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems in finite-frequency domain. The objective is to design an admissible piecewise affine filter such that the asymptotic stability of the filtering error system with the prescribed finite-frequency H−/H∞ performance can be guaranteed. It is assumed that the premise variables of the plant are unmeasurable so that the filter state transition and the plant state transition may be asynchronous. By applying the celebrated S-procedure, the generalized Kalman-Yakubovič-Popov lemma is extended such that the finite-frequency H−/H∞ performance of the fuzzy affine filtering error system is ensured. Furthermore, by utilizing piecewise quadratic Lyapunov functions, Projection lemma, and some matrix inequality linearization techniques, the finite-frequency fault detection filtering design approach is developed for the concerned T-S fuzzy affine dynamic system. It is shown that sufficient conditions for the existence of the fault detection filter are formulated as feasibility of a set of linear matrix inequalities. Finally, the effectiveness and advantages of the proposed design approach are illustrated by simulation studies.

This article focuses on the mean-square consensus control problem for a class of discrete-time multiagent systems (DT-MASs) over time-correlated multistate Markovian fading channels, where the packet loss probability is time-varying and depends on the current channel state. In order to save limited network bandwidth, a compressed coding scheme is developed by preprocessing the measurement output. With the aid of a stochastic Lyapunov–Krasovskii functional, a sufficient condition is first obtained under which the consensus error system is mean-square stable for DT-MASs over identical fading channels. Then, the consensus gain is formulated as the feasible solution to a set of linear matrix inequalities (LMIs) whose dimensions are independent of the number of agents. Furthermore, for the case that agents communicate over nonidentical fading channels, the mean-square consensus problem is transformed into an analyzable edge agreement issue in the mean-square sense by means of properties of the edge Laplacian combined with a mapping technique. Next, a sufficient condition is derived to ensure the mean-square consensus performance, based on which the existence of the controller can be guaranteed by the feasibility of a set of LMIs. Finally, the validity and feasibility of the developed design scheme are shown by two illustrative examples.

This paper investigates the problem of $\mathcal{H}{\_}{-} / \mathcal{H}{\_}{\infty}$ fault detection (FD) filter design for continuous-time polytopic uncertain linear systems in the finite-frequency (FF) domain. By assuming that both disturbances and faults are restricted to FF ranges, we are interested in designing an FD filter such that the resulting filtering error system (FES) is both sensitive to faults and robust against disturbances. By using the generalized Kalman-Yakubovič-Popov (KYP) lemma, Projection lemma, and some elegant convexification procedures, sufficient conditions for synthesis of the FD filter are established by solving an optimization problem in the form of linear matrix inequalities (LMIs). Finally, simulation studies are provided to validate the effectiveness of the proposed filtering approach.

This article is concerned with the finite-frequency $\mathcal {H}_{-}/\mathcal {H}_{\infty }$ memory fault detection filtering problem for discrete-time Takagi–Sugeno fuzzy affine systems with norm-bounded uncertainties. The objective is to design a piecewise affine memory filter by using system historical information such that the resulting closed-loop filtering error system is asymptotically stable with the prescribed finite-frequency $\mathcal {H}_{-}/\mathcal {H}_{\infty }$ performance. Based on the generalized Kalman–Yakubovič–Popov lemma combined with the celebrated $\mathcal {S}$-procedure, new sufficient conditions for the fuzzy affine filtering error system to have the finite-frequency $\mathcal {H}_{-}/\mathcal {H}_{\infty }$ performance are given at first. By further using piecewise fuzzy quadratic Lyapunov functions and Projection lemma, the filtering analysis results for the filtering error system to be asymptotically stable with the prescribed finite-frequency $\mathcal {H}_{-}/\mathcal {H}_{\infty }$ performance are obtained. Then, the filtering synthesis is carried out with the aid of matrix inequality convexification techniques, and the synthesis results are described in terms of linear matrix inequalities. It is further shown that a better filtering performance can be achieved by using more system historical information. Finally, simulation is provided to verify the effectiveness of the proposed approach.

This article is concerned with the ℋ−/ℋ∞ memory fault detection filtering design in finite frequency domain for discrete‐time systems with polytopic uncertainties. Under the assumption that both disturbances and faults are restricted to finite frequency ranges, that is, the low, middle, or high frequency range, an admissible memory fault detection filter is developed by using historical information of the system. It is shown that the asymptotic stability with prescribed ℋ−/ℋ∞ performance of the closed‐loop filtering error system is guaranteed. It is also shown that by using historical system outputs along with parameter‐dependent Lyapunov functions, the better performance of the resulting fault detection filter is obtained. With the aid of Projection lemma, sufficient conditions for the fault detection filtering design are established by solving an optimization problem in the form of a set of linear matrix inequalities (LMIs). Finally, three examples are presented to demonstrate the effectiveness and advantages of the proposed approach.

For a class of stochastic nonlinear systems in pure-feedback form with dead-zone input and multiple time-varying delays, a novel neural network (NN)-based adaptive control approach is presented in this paper through the use of backstepping approach and dynamic surface technique. By choosing proper Lyapunov-Krasovskii functionals, utilizing the characteristic of hyperbolic tangent functions and adopting the function separation technique, difficulties of controller design that introduced by the time-varying delays can be dealt with properly. Moreover, all unknown nonlinear functions are lumped together and approximated by the NN. Additionally, any information over the boundedness of dead-zone parameters is not needed in the process of controller design. The control scheme proposed in this paper ensures the boundedness in probability of all signals in the closed-loop system, besides, excellent performance of arbitrarily small tracking error will be achieved by selecting control parameters appropriately. At last, two numerical simulation examples are provided to verify the validity of the designed algorithm.

Both trajectory tracking (TT) and fin roll reduction (FRR) are fundamental marine applications, and they are usually studied separately in previous studies. Actually, the roll motion often occurs during the trajectory tracking in waves; therefore, they should be studied together. In this work, we consider the trajectory tracking and fin roll reduction of marine vessel as an integral system. It includes three system inputs, namely, the force in surge, the control moment in roll, and the control torque in yaw, while four degrees of freedom (DoF), i.e., position, roll angle and yaw angle are needed to be controlled. Through combining the hierarchical sliding mode approach and neural network technique, a novel control algorithm is proposed. The neural network is introduced to deal with model uncertainty. Lyapunov stability theorem ensures stability of the close-loop system, and various simulations are provided to validate the effectiveness and performance of the proposed algorithm.

A novel nonlinear sliding mode control approach dealing with the formation control of under-actuated ships is presented in this paper. To avoid the singularity problem, state space of the system is partitioned into two regions, with one region bounded for terminal sliding mode control and its complement singular for that. And a linear auxiliary sliding mode controller is designed for system trajectories starting from the complement region. With the application of nonlinear sliding mode control approach and finite-time stability theory, a distributed controller is designed for individual under-actuated ship to achieve the given formation pattern within a finite time. Finally, two simulation examples are provided to verify the effectiveness and performance of the proposed approach.

This paper proposes an event-triggered course-tracking control approach of marine surface vessels based on output reference tracking method. Throughout this work, a reference system, which consists in the continuously controlled version of the system understudy, is employed. Based on the difference between the state of the event-triggered system and that of the reference system, a Lyapunov-like function is defined to guarantee the boundedness of the tracking error. The proposed strategy reduces the amount of energy consumption and computation, thus results in less controller executions, and its feasibility is further verified by the exclusion of Zeno behavior. Effectiveness of the proposed algorithm is further illustrated by simulation results.

In this paper, a novel dynamic surface second order sliding model control method is proposed for course-keeping control of ship in the presence of uncertain errors. The controller is constructed by "dynamic surface control" tech-nique to solve the problems of "explosion of complexity" in the traditional Lyapunov stability theory. A novel second order sliding model control method is proposed in this paper, which is not only capable of strengthening robustness of the system, but also attenuating inherent chattering of classical sliding mode control method effectively. And then the radial basis func-tion neural network approximation technique is used for approximating modeling errors, meanwhile the "minimum learning parameter" technique is used to reduce the computational burden of the algorithm. The controller guarantees that all the close-loop signals are uniform ultimate bounded (UUB) and that the tracking er-rors converge to a small neighborhood of the desired trajectory. Finally, simulation results are given to illustrate the effectiveness of the proposed algorithm.