Sensor location for discrete mode observability of switching linear systems with unknown inputs

This paper deals with the problem of additional sensor placement in order to recover the discrete mode observability of switching structured linear systems with unknown inputs. Such a property is quite important for designing control laws, observers, fault detection and isolation schemes (when the fault occurrence implies a commutation between two modes), and so on. The proposed method, based on a graph-theoretic approach, assumes only the knowledge of the system’s structure. We express, in graphical terms, new necessary and sufficient conditions for discrete mode generic observability. When these conditions are not satisfied, we propose a sensor placement procedure which allows us to recover the mode observability. Our approach can be implemented by classical and quite simple graph-theory algorithms.     

Determining the characteristics of systems with multiple inputs and multiple outputs

A simple and computationally efficient method of determining the transfer function and other characteristics of systems with multiple inputs and multiple outputs (MIMO systems) described by autoregression equations is proposed. The method is based on the use of the discrete Fourier transformation. The algorithm is highly suitable for computer implementation. The efficiency and simplicity of the method are illustrated using the example of a system with three inputs and three outputs. The proposed concept may be applied to systems described by the autoregression sliding mean.     

不确定时滞系统执行器故障模式下的满意容错控制

针对含有不确定参数的离散时滞系统,在执行器增益故障情况下,研究了含时滞记忆的状态反馈满意容错控制器的设计问题.在采用合理的执行器故障描述条件下,分别给出了无外界扰动输入时含有时滞记忆和无时滞记忆状态反馈鲁棒容错控制器的存在条件;进一步给出了在H∞扰动衰减指标约束下,含有时滞记忆和无时滞记忆状态反馈鲁棒容错控制器的设计方法.仿真算例验证了该方法的有效性.     

Sensor deployment for fault diagnosis using a new discrete optimization algorithm

Optimal allocation of the sensor in a wireless sensor network (WSN) is required to have a satisfactory fault diagnosis within the system. In fact, the sensor nodes in the network should be located in an arrangement to maximize the failure diagnosis. In this paper, the sensor deployment optimization to diagnose the distributed failures in a wireless unmanned aerial vehicles (UAVs) network has been studied. In this way, a novel evolutionary optimization algorithm inspired by the gases Brownian and turbulent rotational motion is utilized which is called Discrete Gases Brownian Motion Optimization (DGBMO) algorithm. An integer linear programming (ILP) approach is used to formulate the sensor deployment. Then the sensor deployment optimization is solved by DGBMO as well as generic ILP solvers and Boolean satisfiability-based ILP solvers. The results show that DGBMO is suitable for sensor disposition optimization especially in large-sized UAV networks.     

线性系统考虑执行器故障的鲁棒控制

针对线性定常系统，提出了考虑执行器故障的鲁棒控制器设计问题。利用更一般、更实际的执行器故障模型，给出了系统输出渐近跟踪参考输入信号的鲁棒控制存在的充分条件。通过求解线性矩阵不等式（LMI）完成状态反馈控制器的设计。数例仿真验证了本文提出设计方法的可行性，并且通过所设计的鲁棒控制系统与不考虑故障控制系统的比较，进一步说明对系统进行鲁棒设计的必要性。     

Formal approach to fault diagnosis in distributed discrete event systems with OBDD

In this paper, we study the fault diagnosis problem for distributed discrete event systems. The model assumes that the system is composed of distributed components which are modeled in labeled Petri nets and interact with each other via sets of common resources (places). Further, a component’s own access to a common resource is an observable event. Based on the diagnoser approach proposed by Sampath et al., a distributed fault diagnosis algorithm with communication is presented. The distributed algorithm assumes that the local diagnosis process can exchange messages upon the occurrence of observable events. We prove the distributed diagnosis algorithm is correct in the sense that it recovers the same diagnostic information as the centralized diagnosis algorithm. Furthermore, we introduce the ordered binary decision diagrams (OBDD) in order to manage the state explosion problem in state estimation of the system.     

Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions

Partial PCA based on principal component analysis (PCA) with ideas borrowed from parity relations is a useful method in fault isolation (J. Gertler, W. Li, Y. Huang, T.J. McAvoy, Isolation enhanced principal component analysis, AIChE Journal 45(2) (1999) 323–334). By performing PCA on subsets of variables, a set of structured residuals can be obtained in the same way as structured parity relations. The structured residuals are utilized in composing an isolation scheme for sensor and actuator faults, according to a properly designed incidence matrix. To overcome the limitations of PCA, nonlinear approaches based on generalized PCA (GPCA) and nonlinear PCA (NPCA) are proposed. The nonlinear methods are demonstrated on an artificial 2×2 system while simulation studies on the Tennessee Eastman process illustrate the linear method and some extensions.     

Sensor bias fault diagnosis in a class of nonlinear systems

This note describes a robust sensor bias fault diagnosis architecture for dynamic systems represented by a class of nonlinear discrete-time models. The nonlinearity in the system nominal model is assumed to be a function of inputs and outputs only. Specifically, this note uses adaptive techniques to estimate an unknown sensor bias in the presence of modeling uncertainties. A simulation example is presented to illustrate the methodology. The robustness, sensitivity and stability properties of the bias fault diagnosis architecture are rigorously analyzed     

Observer design for discrete systems with unknown exogenous inputs

A design procedure is developed for determining optimal discrete observers for estimating system states and unknown exogenous system inputs. This procedure is based on augmenting a standard system observer with an input model. The augmented model is then transformed into the discrete z-domain to determine relevant input/output transfer function matrices. The transfer function matrices are used to develop transfer function relationships between unknown exogenous inputs and the observer estimate of these inputs. It is shown that the optimal observer gains can be determined by implementing the observer as a Fisher filter. An example of the procedure is demonstrated with a third-order point-mass tracking filter     

Observers for linear discrete multivariable systems with inaccessible inputs

The problem of observing the state vector of a discrete-time multivariable system subjected to inaccessible inputs is considered. Using a polynomial-type approximation for these inputs, a set of necessary and sufficient conditions that can be readily applied to determine the existence of an observer is obtained. Computer simulation results for a third-order two-input two-output system are included.     

Simultaneous fault detection and control for switched systems with actuator faults

This paper is concerned with the fault detection and control problem for discrete-time switched systems. The actuator faults, especially ‘outage cases’, are considered. The detector/controller is designed simultaneously such that the closed-loop system switches under an average dwell time, and when a fault is detected, an alarm is generated and then the controller is switched to allow the norm of the states of the subsystem to increase within the acceptable limits. Thus, a switching strategy which combines average dwell time switching with event-driven switching is proposed. Under this switching strategy, the attention is focused on designing the detector/controller such that estimation errors between residual signals and faults are minimised for the fulfillment of fault detection objectives; simultaneously, the closed-loop system becomes asymptotically stable for the fulfillment of control objectives. A two-step procedure is adopted to obtain the solutions through satisfying a set of linear matrix inequalities. An example comprising of three cases is considered. Through these cases, it is demonstrated that the fault detection and control for switched systems using a two-stage switching strategy and asynchronous switching are feasible.     

Generation of candidates' tree for the fault diagnosis of discrete event systems

This paper presents a discrete event model-based approach for Fault Detection and Isolation of manufacturing systems. This approach considers a system as a set of independent Plant Elements (PEs). Each PE is composed of a set of interrelated Parts of Plant (PoPs) modeled by a Moore automaton. Each PoP model is only aware of its local behavior. The degraded and faulty behaviors are added to each PoP model in order to obtain extended PoP ones. An extrapolation of Gaussian learning is realized to obtain acceptable temporal intervals between the time occurrences of correlated events. Finally based on the PoP extended models and the links between them, a fault candidates' tree is established for each plant element. This candidates' tree corresponds to a local on-line fault event occurrence observer, called diagnoser. Thus, the diagnosis decision is distributed on each plant element. An application example is used to illustrate the approach.     

Adaptive actuator fault compensation for linear systems with matching and unmatching uncertainties

The problem of process systems subject to actuator faults (partial loss of actuator effectiveness) is considered. An active fault compensation control law is designed that utilizes compensation in a way that accounts for matching and unmatching uncertainties and the occurrence of actuator faults. The main idea is designing the robust compensation controller to guarantee closed-loop stability in the presence of faults, based on a neural network representation of the fault dynamics. Changes in the system due to faults are modeled as unknown nonlinear functions. The updating control law is derived such that all the parameters of the closed-loop system are bounded. An output feedback controller is used to the “healthy” system and the adaptive feedback controller is used to compensate for the effect of the dynamics caused by the fault. The advantage of fault compensation is the dynamics caused by faults can be accommodated online. The proposed design method is illustrated on a three-tank system.     

Extended dissipativity-based non-fragile control for multi-area power systems with actuator fault

This paper addresses the issue of reliable load frequency control design of an uncertain multi-area power system with constant time delays and disturbances via non-fragile sampled-data control approach. In particular, the parameter uncertainties are assumed to be randomly occurring which are described by the Bernoulli distributed sequences. By constructing a suitable Lyapunov–Krasovskii functional together with Wirtinger-based inequality, a new set of sufficient conditions in terms of linear matrix inequalities is obtained to ensure the asymptotic stability and extended dissipativity of the multi-area power system not only when all actuators are operational, but also in case of some actuator failures. Finally, simulation results are conducted to validate the effectiveness of the proposed control design technique.     

Designing fault detection observers for linear systems with mismatched unknown inputs

Algebraic unknown input observers (UIOs) that have been previously reported in the literature can be constructed under the assumption that linear systems with unknown inputs satisfy the so-called observer matching condition. This condition restricts practical applications of UIOs for fault detection and isolation (FDI). We present an algebraic design for fault detection observers (FDOs) for the case in which the observer matching condition is not satisfied. To loosen the restriction imposed by the observer matching condition, the UIO design method combined with the unknown input modeling technique is proposed to design an FDO that decouples the effect of mismatched unknown inputs. To do this, first, unknown inputs that denote the faults of no interest and process disturbances are decomposed into algebraically rejectable unknown inputs and modeled unknown inputs such that the observer matching condition is satisfied. Under the assumption that mismatched unknown inputs are deterministic and can be expressed as the responses of fictitious autonomous dynamical systems, an augmented system is obtained by combining the original system model with the unknown input model. Finally, through the design technique of a UIO for the augmented system, a reduced-order FDO is constructed to estimate an augmented state vector that consists of both the original state variables and the augmentative state variables. The estimated state is then used to generate the residual, which should be designed to be insensitive to unknown inputs while being sensitive to the faults of interest. Two numerical examples are provided to show the usefulness and the feasibility of the presented approach.     

Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults

The goal of this paper is to describe a novel fault tolerant tracking control (FTTC) strategy based on robust fault estimation and compensation of simultaneous actuator and sensor faults. Within the framework of fault tolerant control (FTC) the challenge is to develop an FTTC design strategy for nonlinear systems to tolerate simultaneous actuator and sensor faults that have bounded first time derivatives. The main contribution of this paper is the proposal of a new architecture based on a combination of actuator and sensor Takagi-Sugeno (T-S) proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators together with a T-S dynamic output feedback control (TSDOFC) capable of time-varying reference tracking. Within this architecture the design freedom for each of the T-S estimators and the control system are available separately with an important consequence on robust L ₂ norm fault estimation and robust L ₂ norm closed-loop tracking performance. The FTTC strategy is illustrated using a nonlinear inverted pendulum example with time-varying tracking of a moving linear position reference.     

Periodic tracking adaptive control for multivariable systems having more outputs than inputs

This note presents an adaptive control algorithm for multivariable systems in which the number of outputs is greater than the number of inputs. The algorithm can force the outputs to track arbitrary given reference signals periodically. This is the best tracking performance for systems lacking output function controllability. It has been shown that the tracking period is the upper bound on the controllability index of the controlled system. The proposed algorithm is applicable to multivariable systems with arbitrary interactor matrix but no knowledge of the interactor matrix is required.