Synchronization of complex dynamical networks with random coupling delay and actuator faults

This paper addresses the issue of passivity-based synchronization problem for a family of Markovian jump neutral complex dynamical networks (NCDNs) with coupling delay and actuator faults. Also, by considering the effect of random fluctuation in complex dynamical network systems, the occurrence of coupling delay are taken in terms of a stochastic distribution, which obeys the Bernoulli distribution. To handle the fault effects in actuators of proposed complex network systems, an actuator fault model is considered. The main objective of this paper is to develop a robust state feedback controller such that for all possible actuator failures and random coupling delays, all nodes of the proposed Markovian jump NCDNs is globally asymptotically synchronized to the reference node in mean square sense and guarantee the output strict passivity performance. By developing a suitable Lyapunov–Krasovskii functional and utilizing the Wirtinger-based integral inequality, the required a set of sufficient conditions for the synchronization of proposed system is established in form of linear matrix inequalities. Finally, three numerical examples including a 3-dimensional Lorenz chaotic model are provided to demonstrate the correctness and superiority of the proposed control scheme.     

Robust Takagi-Sugeno fuzzy control for fractional order hydro-turbine governing system

A robust fuzzy control method for fractional order hydro-turbine governing system (FOHGS) in the presence of random disturbances is investigated in this paper. Firstly, the mathematical model of FOHGS is introduced, and based on Takagi-Sugeno (T-S) fuzzy rules, the generalized T-S fuzzy model of FOHGS is presented. Secondly, based on fractional order Lyapunov stability theory, a novel T-S fuzzy control method is designed for the stability control of FOHGS. Thirdly, the relatively loose sufficient stability condition is acquired, which could be transformed into a group of linear matrix inequalities (LMIs) via Schur complement as well as the strict mathematical derivation is given. Furthermore, the control method could resist random disturbances, which shows the good robustness. Simulation results indicate the designed fractional order T-S fuzzy control scheme works well compared with the existing method.     

Global exponential stability of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays

In this paper, a class of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays is investigated. The jumping parameters are modeled as a continuous-time finite-state Markov chain. At first, the existence of equilibrium point for the addressed neural networks is studied. By utilizing the Lyapunov stability theory, stochastic analysis theory and linear matrix inequality (LMI) technique, new delay-dependent stability criteria are presented in terms of linear matrix inequalities to guarantee the neural networks to be globally exponentially stable in the mean square. Numerical simulations are carried out to illustrate the main results.     

基于执行器部分失效的网络化控制系统完整性研究

针对一类具有马尔可夫特性时延的网络化控制系统,考虑系统参数不确定性的影响,基于时延T-S模糊模型建立数学模型,通过构造离散Lyapunov函数,推证出了确保网络化控制系统在执行器部分失效时具有鲁棒完整性的充分条件,并以求解LMIs给出容错控制器的设计方法.最后通过仿真验证了文中所述方法的可行性和有效性.     

New stability and stabilization conditions for nonlinear systems with time-varying delay based on delay-partitioning approach

This paper focuses on stability analysis and stabilization of nonlinear systems with interval time-varying delay, modeled by Takagi-Sugeno (T-S) fuzzy approach. To achieve more relaxation in the feasibility region, delay-partitioning approach is used for all integral terms in the Lyapunov-Krasovskii functional (LKF). A fuzzy Lyapunov function is proposed instead of non-integral term in LKF, and moreover, some slack matrices variables are offered to enlarge the design space. By doing this, new delay-dependent stability criteria are obtained. During the derivation of stability conditions, Jensen’s integral inequality is applied to deal with integral terms. Furthermore, in this paper the problem of controller design via the parallel distributed compensation (PDC) scheme is studied. Stability and stabilization conditions with less conservative are achieved in terms of linear matrix inequality (LMI). Finally, two numerical examples are presented to show the effectiveness of the proposed results.     

Event-triggered reliable control for fuzzy Markovian jump systems with mismatched membership functions

The problem of event-triggered reliable control for fuzzy Markovian jump system (FMJS) with mismatched membership functions (MMFs) is addressed. Based on the mode-dependent reliable control and event-triggered communication scheme, the stability conditions and control design procedure are formulated. More precisely, a general actuator-failure is designed such that the FMJS is reliable in the sense of stochastically stable and reduce the utilization of network resources. Furthermore, the improved MMFs are introduced to reduce the conservativeness of obtained results. Finally, simulation results indicate the effectiveness of the proposed methodology.     

A novel scheme for current sensor faults diagnosis in the stator of a DFIG described by a T-S fuzzy model

Diagnosis of current sensor faults (CSF) for doubly fed induction generators (DFIGs) is of paramount importance for the reliable power generation of DFIG-based wind turbines (WT). In this paper, a new scheme is developed for current sensors faults diagnosis in the stator of a DFIG-based WT. The nonlinear model of the DFIG is first transformed into an equivalent Takagi-Sugeno (T-S) fuzzy model. Secondly, using this model, a novel fault detection and isolation (FDI) algorithm is proposed. This algorithm is based on a bank of Luenberger observers for residuals generation combined with a new proposed residual vector. Furthermore, a new binary decision logic is used for CSF isolation. Stability analysis of the observer bank is analyzed using a Lyapunov theorem, which allows deriving sufficient stability conditions by solving a system of Linear Matrix Inequalities (LMIs). A simulation study is carried out to assess the performance and the effectiveness of the new FDI scheme.     

气动位置伺服系统的T-S型模糊控制研究

针对气动位置伺服系统特性随工作点位置和负载变化而变化的特点,提出采用基于Takagi-Sugeno模型(T-S模型)的模糊控制。以比例流量阀控摆动气缸位置伺服系统为例,研究了建立气动位置伺服系统的T-S型模糊控制器的一些关键技术,包括前提变量的确定、子系统的划分、模糊子集隶属函数的确定以及局部控制器的设计等。试验结果表明,采用所建T-S型模糊控制器,在较大范围内的任意负载下,执行元件行程范围内的任意位置,系统都具有良好的响应特性     

Mixed H∞ and passive filtering for switched Takagi-Sugeno fuzzy systems with average dwell time

This paper investigates the mixed H_∞ and passive filtering problem for switched Takagi-Sugeno (T-S) fuzzy systems with average dwell time (ADT) in both continuous-time and discrete-time contexts. To deal with this problem, a new performance index is proposed for switched systems. This new performance index can be viewed as the mixed weighted H_∞ and passivity performance index. Based on this new performance index, the weighted H_∞ filtering problem and the passive filtering problem for switched T-S fuzzy systems can be solved in a unified framework. Combining the multiple Lyapunov functions approach with a matrix decoupling technique, new sufficient conditions for the existence of mixed weighted H_∞ and passive filters are obtained for switched T-S fuzzy systems. All these conditions are expressed in terms of linear matrix inequalities (LMIs). The desired filters can be constructed by solving these LMIs. Finally, numerical examples and practical examples are provided.     

A bounded-error approach to actuator fault diagnosis and remaining useful life prognosis of Takagi-Sugeno fuzzy systems

The paper presents an actuator fault diagnosis and prognosis scheme for Takagi-Sugeno fuzzy system. It overcomes the drawbacks of the approaches presented in the relevant literature by taking into consideration the problem of the robustness. The proposed approach consists of an actuator fault estimator which provides knowledge about faults with their uncertainty intervals. Thus, the fault detection mechanism relies on an acceptable threshold imposed on these intervals. Once the fault is detected, the remaining useful life of an actuator is predicted what is expressed in the so-called time-to-failure. Similarly as in the case of fault estimation, the remaining useful life is provided as an uncertainty interval. It should be notated that previously reported approaches to Takagi-Sugeno systems cannot either provide the uncertainty of fault estimates for remaining useful life. To tackle the development of the above schemes the theory of quadratic boundedness and feasible parameter set-based estimation are employed. The final part of the paper portrays a case study which clearly exhibits the performance of the proposed approach.     

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach.     

T-S region-based fuzzy control with multiple performance constraints

Takagi-Sugeno fuzzy control problems with minimizing H2/Hinfinity norm are investigated in this paper. A redesigned T-S fuzzy model and controller are called a T-S Region-based Fuzzy Model (TSRFM) and a T-S Region-based Fuzzy Controller (TSRFC), respectively, which are derived from the fuzzy region concept and the robust control technique. The fuzzy region concept is used to divide the general plant rules into several fuzzy regions and the robust control technique is used to stabilize all plant rules of each fuzzy region. In this case, the stability conditions with H2/Hinfinity performance are derived from Lyapunov criterion, which are expressed in terms of LMIs. For the fuzzy model involving large plant rules, the proposed idea greatly reduces the total number of LMIs and controller rules so that TSRFC is easy to implement with simple hardware. Although the controller rules are reduced, TSRFC is able to provide performance as good as former designs.     

Extended dissipative state estimation for uncertain discrete-time Markov jump neural networks with mixed time delays

This paper is concerned with the problem of extended dissipativity-based state estimation for uncertain discrete-time Markov jump neural networks with finite piecewise homogeneous Markov chain and mixed time delays. The aim of this paper is to present a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative. A triple-summable term is introduced in the constructed Lyapunov function and the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term. The extended dissipativity criterion is derived in form of linear matrix inequalities. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.     

Delay-dependent fuzzy static output feedback control for discrete-time fuzzy stochastic systems with distributed time-varying delays

This paper is concerned with the delay-dependent H(∞) fuzzy static output feedback control scheme for discrete-time Takagi-Sugeno (T-S) fuzzy stochastic systems with distributed time-varying delays. To begin with, the T-S fuzzy stochastic system is transformed to an equivalent switching fuzzy stochastic system. Then, based on novel matrix decoupling technique, improved free-weighting matrix technique and piecewise Lyapunov-Krasovskii function (PLKF), a new delay-dependent H(∞) fuzzy static output feedback controller design approach is first derived for the switching fuzzy stochastic system. Some drawbacks existing in the previous papers such as matrix equalities constraint, coordinate transformation, the same output matrices, diagonal structure constraint on Lyapunov matrices and BMI problem have been eliminated. Since only a set of LMIs is involved, the controller parameters can be solved directly by the Matlab LMI toolbox. Finally, two examples are provided to illustrate the validity of the proposed method.     

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults.     

Event-triggered control design of linear networked systems with quantizations

This paper is concerned with the control design problem of event-triggered networked systems with both state and control input quantizations. Firstly, an innovative delay system model is proposed that describes the network conditions, state and control input quantizations, and event-triggering mechanism in a unified framework. Secondly, based on this model, the criteria for the asymptotical stability analysis and control synthesis of event-triggered networked control systems are established in terms of linear matrix inequalities (LMIs). Simulation results are given to illustrate the effectiveness of the proposed method.     

Robust fault-tolerant control for networked control systems subject to random delays via static-output feedback

This paper focuses on the problem of fault-tolerant controller (FTC) design for uncertain networked control systems (NCSs) with random delays and actuator faults. A new fault model is proposed to represent more class of actuator faults. More precisely, the NCSs with random delays and the possible actuator faults are modeled as a Markovian jump system (MJS) with incomplete transition probabilities (TPs) and then LMI-based sufficient conditions are derived to ensure the stochastic stability of the closed-loop system. The sufficient conditions are constructed to synthesize the mode-dependent static-output feedback (SOF) control laws. Feasibility and reliability of the proposed FTC against actuator faults are indicated through simulation results.     

Actuator fault tolerant control based on probabilistic ultimate bounds

In this work we introduce a novel set-based fault tolerant control scheme for linear systems under Gaussian disturbances. In the proposed strategy, actuator faults are detected and diagnosed when residual trajectories enter and remain in certain sets that are computed as probabilistic ultimate bounds. After a fault is diagnosed, the control scheme is reconfigured to take into account the corresponding actuator failure and preserve certain closed loop features. We show that our strategy can detect and diagnose the different faults considered with an arbitrarily small probability of misdetection.     

某越野汽车磁流变半主动悬架变论域模糊控制

为了解决半主动悬架传统变论域模糊控制器过度依赖经验规则的问题,提出了一种基于模糊神经网络的变论域T-S模糊控制策略。首先,根据磁流变减振器阻尼特性的实验结果,建立基于自适应模糊神经网络的减振器阻尼力模型及1/2车辆半主动悬架动力学模型;其次,建立悬架系统T-S模糊控制器,同时为了实时调节T-S模糊控制器变量的论域,采用模糊神经网络结构描述伸缩因子的变化。仿真结果表明,笔者提出的变论域模糊控制策略能够有效提高车辆行驶平顺性和操作稳定性。