Fault‐tolerant fusion‐based MPC with sensor recovery for constrained LPV systems

In this paper, we present a robust fault‐tolerant control scheme for constrained multisensor linear parameter‐varying systems, subject to bounded disturbances, that utilises multiple sensor fusion. The closed‐loop scheme consists of a tube model predictive control‐based feedback tracking controller and sensor‐estimate fusion strategy, which allows for the reintegration of previously faulty sensors. The active fault‐tolerant fusion‐based mechanism tracks the healthy‐faulty transitions of suitable residual variables by means of set separation and precomputed transition times. The sensor‐estimate pairings are then reconfigured based on available healthy sensors. Under the proposed scheme, robust preservation of closed‐loop system boundedness is guaranteed for a wide range of sensor fault situations. An example is presented to illustrate the performance of the fault‐tolerant control strategy.     

Unknown input modeling and robust fault diagnosis using black box observers

A key issue that needs to be addressed while performing fault diagnosis using black box models is that of robustness against abrupt changes in unknown inputs. A fundamental difficulty with the robust FDI design approaches available in the literature is that they require some a priori knowledge of the model for unmeasured disturbances or modeling uncertainty. In this work, we propose a novel approach for modeling abrupt changes in unmeasured disturbances when innovation form of state space model (i.e. black box observer) is used for fault diagnosis. A disturbance coupling matrix is developed using singular value decomposition of the extended observability matrix and further used to formulate a robust fault diagnosis scheme based on generalized likelihood ratio test. The proposed modeling approach does not require any a priori knowledge of how these faults affect the system dynamics. To isolate sensor and actuator biases from step jumps in unmeasured disturbances, a statistically rigorous method is developed for distinguishing between faults modeled using different number of parameters. Simulation studies on a heavy oil fractionator example show that the proposed FDI methodology based on identified models can be used to effectively distinguish between sensor biases, actuator biases and other soft faults caused by changes in unmeasured disturbance variables. The fault tolerant control scheme, which makes use of the proposed robust FDI methodology, gives significantly better control performance than conventional controllers when soft faults occur. The experimental evaluation of the proposed FDI methodology on a laboratory scale stirred tank temperature control set-up corroborates these conclusions.     

Multisensor switching control strategy with fault tolerance guarantees

In this paper we propose a novel fault tolerant multisensor switching strategy for feedback control. Each sensor of the proposed multisensor scheme has an associated state estimator which, together with a state feedback gain, is able to individually stabilise the closed-loop system. At each instant of time, the switching strategy selects the sensor-estimator pair that provides the best closed-loop performance, as measured by a control-performance criterion. We establish closed-loop stability of the resulting switching scheme under normal (fault-free) operating conditions. More importantly, we show that closed-loop stability is preserved in the presence of faulty sensors if a set of conditions on the system parameters (such as bounds on the sensor noises, maximum and minimum values of the reference signal, etc.) is satisfied. This result enhances and broadens the applicability of the proposed multisensor scheme since it provides guaranteed properties such as fault tolerance and robust closed-loop stability under sensor fault. The results are applied to the problem of automotive longitudinal control.     

A virtual actuator and sensor approach for fault tolerant control of LPV systems

In this paper, a fault tolerant control (FTC) strategy using virtual actuators and sensors for linear parameter varying (LPV) systems is proposed. The main idea of this FTC method, initially developed for LTI systems, is to reconfigure the control loop such that the nominal controller could still be used without need of retuning it. The plant with the faulty actuator/sensor is modified adding the virtual actuator/sensor block that masks the actuator/sensor fault. The suggested technique is an active FTC strategy that reconfigures the virtual actuator/sensor on-line taking into account faults and operating point changes. The stability of the reconfigured control loop is guaranteed if the faulty plant is stabilizable/detectable. The LPV virtual actuator/sensor is designed using polytopic LPV techniques and linear matrix inequalities (LMIs). A two-tank system simulator is used to assess the performance of the proposed method. In particular, it is shown that the application of the proposed technique results in an improvement, in terms of performance, with respect to the LTI counterpart.     

Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller

In this paper, a new active fault tolerant control (AFTC) methodology is proposed based on a state estimation scheme for fault detection and identification (FDI) to deal with the potential problems due to possible fault scenarios. A bank of adaptive unscented Kalman filters (AUKFs) is used as a core of FDI module. The AUKF approach alleviates the inflexibility of the conventional UKF due to constant covariance set up, leading to probable divergence. A fuzzy-based decision making (FDM) algorithm is introduced to diagnose sensor and/or actuator faults. The proposed FDI approach is utilized to recursively correct the measurement vector and the model used for both state estimation and output prediction in a model predictive control (MPC) formulation. Robustness of the proposed FTC system, H_∞ optimal robust controller and MPC are combined via a fuzzy switch that is used for switching between MPC and robust controller such that FTC system is able to maintain the offset free behavior in the face of abrupt changes in model parameters and unmeasured disturbances. This methodology is applied on benchmark three-tank system; the proposed FTC approach facilitates recovery of the closed loop performance after the faults have been isolated leading to an offset free behavior in the presence of sensor/actuator faults that can be either abrupt or drift change in biases. Analysis of the simulation results reveals that the proposed approach provides an effective method for treating faults (biases/drifts in sensors/actuators, changes in model parameters and unmeasured disturbances) under the unified framework of robust fault tolerant control.     

Robust fault detection and isolation in stochastic systems

This article outlines the formulation of a robust fault detection and isolation (FDI) scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves estimating sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the proposed robust FDI system.     

A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.     

Sliding‐mode fault‐tolerant control using the control allocation scheme

This paper is devoted to the design of a novel fault‐tolerant control (FTC) using the combination of a robust sliding‐mode control (SMC) strategy and a control allocation (CA) algorithm, referred to as a CA‐based sliding‐mode FTC (SMFTC). The proposed SMFTC can also be considered a modular‐design control strategy. In this approach, first, a high‐level SMC, designed without detailed knowledge of systems' actuators/effectors, commands a vector of virtual control signals to meet the overall control objectives. Then, a CA algorithm distributes the virtual control efforts among the healthy actuators/effectors using the real‐time information obtained from a fault detection and reconstruction mechanism. As the underlying system is not assumed to have a rank‐deficient input matrix, the control allocator module is visible to the SMC module resulting in an uncertainty. Hence, the virtual control, in this scheme, is designed to be robust against uncertainties emanating from the visibility of the control allocator to the controller and imperfections in the estimated effectiveness gain. The proposed CA‐based SMFTC scheme is a unified FTC, which does not need to reconfigure the control system in the case of actuator fault or failure. Additionally, to cope with actuator saturation limits, a novel redistributed pseudoinverse‐based CA mechanism is proposed. The effectiveness of the proposed schemes is discussed with a numerical example.     

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.     

Fault tolerant control using virtual actuators and set‐separation detection principles

In this paper we combine a set‐separation approach to fault detection and identification (FDI), recently proposed by the authors, with the virtual actuator approach to controller reconfiguration of Steffen and Lunze. The FDI approach is based on the separation of sets that characterize the system operation under different actuator fault situations that can occur in the plant. The derivation of these sets takes into account the closed‐loop system reconfigured by means of the virtual actuator under all considered actuator faults. Analytic conditions in terms of closed‐loop system parameters and bounds on external signals can be deduced from the required set separation which, in turn, guarantees closed‐loop stability, setpoint tracking, and optimal performance properties of the scheme under all considered fault situations. Thus, the main contribution of this paper is twofold. First, it provides an integrated strategy for fault tolerant control by adapting two existing techniques for FDI and for controller reconfiguration to work in combined form. Second, and more importantly, it endows the resulting combined scheme with guaranteed closed‐loop stability, setpoint tracking and optimal performance properties under actuator faults and in the presence of disturbances. Copyright © 2011 John Wiley & Sons, Ltd.     

Intelligent state estimation for fault tolerant nonlinear predictive control

There is growing realization that on-line model maintenance is the key to realizing long term benefits of a predictive control scheme. In this work, a novel intelligent nonlinear state estimation strategy is proposed, which keeps diagnosing the root cause(s) of the plant model mismatch by isolating the subset of active faults (abrupt changes in parameters/disturbances, biases in sensors/actuators, actuator/sensor failures) and auto-corrects the model on-line so as to accommodate the isolated faults/failures. To carry out the task of fault diagnosis in multivariate nonlinear time varying systems, we propose a nonlinear version of the generalized likelihood ratio (GLR) based fault diagnosis and identification (FDI) scheme (NL-GLR). An active fault tolerant NMPC (FTNMPC) scheme is developed that makes use of the fault/failure location and magnitude estimates generated by NL-GLR to correct the state estimator and prediction model used in NMPC formulation. This facilitates application of the fault tolerant scheme to nonlinear and time varying processes including batch and semi-batch processes. The advantages of the proposed intelligent state estimation and FTNMPC schemes are demonstrated by conducting simulation studies on a benchmark CSTR system, which exhibits input multiplicity and change in the sign of steady state gain, and a fed batch bioreactor, which exhibits strongly nonlinear dynamics. By simulating a regulatory control problem associated with an unstable nonlinear system given by Chen and Allgower [H. Chen, F. Allgower, A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability, Automatica 34(10) (1998) 1205–1217], we also demonstrate that the proposed intelligent state estimation strategy can be used to maintain asymptotic closed loop stability in the face of abrupt changes in model parameters. Analysis of the simulation results reveals that the proposed approach provides a comprehensive method for treating both faults (biases/drifts in sensors/actuators/model parameters) and failures (sensor/ actuator failures) under the unified framework of fault tolerant nonlinear predictive control.     

Fault-tolerant model predictive control of a direct methanol-fuel cell system with actuator faults

This paper investigates fault tolerant model predictive control (MPC) of a direct methanol fuel cell (DMFC) system with several faults in the methanol feeding pump. An active FTMPC strategy with a hierarchal structural design is developed. The focus here is on fault detection and isolation (FDI) and the implementation of fault-tolerant strategies within the control algorithm. To this end, a model-based FDI scheme with virtual sensors is first developed by means of the real-time diagnosis of fault occurrence during operation. Thereby, several faults in the methanol pump are characterized and the information integrated into the MPC algorithm in each fault case. Strategies are presented to reconfigure the active fault-tolerant MPC to keep the DMFC system stable in case of a feeding failure. Moreover, economic, stability and lifetime characteristics are also integrated into the active fault-tolerant MPC. The proposed FDI and FTMPC scheme is tested experimentally in a DMFC test rig with a 5-cell DMFC stack to demonstrate the effectiveness and robustness of the designed approach. Several fault scenarios with the FTMPC are shown. Particularly in the case of fuel cells, fault tolerance is necessary to meet the goals of long-lasting system stability and efficiency.     

Fault tolerant control for joint structure in PEMS high speed maglev train

The PEMS high speed maglev train, which features a permanent magnet inside an electromagnet, is a new kind of maglev train for long distance intercity transportation. The joint structure, which consists of two single levitation sub‐systems, is the fundamental levitation unit. Two kinds of faults are considered and corresponding fault tolerant control strategies are proposed. The first fault condition is when a gap sensor that is part of a single levitation system is faulty. For this kind of fault, a fault tolerant control strategy based on signal reconfiguration is proposed. The second fault condition is when the whole of a single levitation sub‐system is faulty. Under this condition, a faulty model is firstly established, then a fault tolerant control strategy is designed. When this kind of fault is detected, a switch from the normal controller to the fault tolerant controller can make the faulty system stable.     

Multisensor fusion fault tolerant control

In this paper, a multisensor fusion fault tolerant control system with fault detection and identification via set separation is presented. The fault detection and identification unit verifies that for each sensor–estimator combination, the estimation tracking errors lie inside pre-computed sets and discards faulty sensors when their associated estimation tracking errors leave the sets. An active fault tolerant controller is obtained, where the remaining healthy estimates are combined using a technique based on the optimal fusion criterion in the linear minimum-variance sense. The fused estimates are then used to implement a state feedback tracking controller. We ensure closed-loop stability and performance under the occurrence of abrupt sensor faults. Experimental validation, illustrating the multisensor fusion fault tolerant control strategy is included.     

约束多传感器线性系统的RMPC容错控制

针对一类约束多传感器线性故障系统,提出了一种基于鲁棒预测控制策略的容错控制方案．首先为多传感器线性系统设计了观测器,然后离线设计不变集列,使得时变的状态估计误差存在于相应的不变集列中,利用不变集的理论提出了一种新的故障检测的方法,最后基于鲁棒预测控制策略为故障系统设计了容错控制器,给出了闭环系统鲁棒稳定性的证明．仿真结果证明了方法的可行性。     

基于嵌套自适应观测器的 连铸结晶器振动位移系统有限时间容错控制

针对连铸结晶器振动位移系统存在伺服电机驱动单元等执行器故障和负载转矩扰动问题, 本文提出一种基于嵌套自适应观测器的有限时间容错策略. 首先, 设计一种嵌套自适应观测器在线估计由执行器故障和负载转矩扰动构成的综合不确定项; 其次, 采用分层设计与终端滑模相结合的方法, 分别对位移子系统和电流环子系统设计全阶滑模控制器(FOSMC)和终端滑模控制器补偿综合不确定项, 并通过引入一阶低通滤波器来提高控制信号的连续性. 理论分析表明, 本文所提容错控制策略能够保证闭环系统所有状态有限时间稳定; 最后, 通过仿真对比研究验证了本文所提控制策略的有效性.     

LPV Model-Based Tracking Control and Robust Sensor Fault Diagnosis for a Quadrotor UAV

This work is dedicated to the design of a robust fault detection and tracking controller system for a UAV subject to external disturbances. First, a quadrotor modelled as a Linear Parameter Varying (LPV) system is considered as a target to design and to illustrate the proposed methodologies. In order to perform fault detection and isolation, a robust LPV observer is designed. Sufficient conditions to guarantee asymptotic stability and robustness against disturbance are given by a set of feasible Linear Matrix Inequalities (LMIs). Furthermore, the observer gains are designed with a desired dynamic by considering pole placement based on LMI regions. Then, a fault detection and isolation scheme is considered by mean of an observer bank in order to detect and isolate sensor faults. Second, a feedback controller is designed by considering a comparator integrator control scheme. The goal is to design a robust controller, such that the UAV tracks some reference positions. Finally, some simulations in fault-free and faulty operations are considered on the quadrotor system.     

Fault tolerant control of a proton exchange membrane fuel cell using Takagi–Sugeno virtual actuators

In this paper, a fault tolerant control (FTC) strategy for proton exchange membrane (PEM) fuel cells based on the use of virtual actuators and the Takagi–Sugeno (TS) approach is proposed. The overall solution relies on adding a virtual actuator in the control loop to hide the fault from the controller point of view, allowing it to see the same plant as before the fault, such that the stability and some desired performances are preserved. The proposed methodology is based on the use of a reference model, where the resulting nonlinear error model is brought to a Takagi–Sugeno form using a gridding approach. The TS model is suitable for designing a controller using linear matrix inequalities (LMI)-based techniques, such that the resulting closed-loop error system is stable with poles placed in some desired region of the complex plane. Simulation results are used to show the effectiveness of the proposed approach.