A fault tolerant control framework for periodic switched non-linear systems

An observer-based fault tolerant control (FTC) framework is proposed for a class of periodic switched non-linear systems (PSNS) without full state measurements. Two kinds of faults are considered: continuous faults that affect each mode during its dwell period; and discrete faults that affect the switching sequence. Under the average dwell time scheme, the proposed FTC framework can maintain the stability of overall PSNS in spite of these two kinds of fault. A switched reluctance motor example is taken to illustrate the efficiency of the proposed method.     

Adaptive neural model-based fault tolerant control for multi-variable processes

An adaptive neural network model-based fault tolerant control approach for unknown non-linear multi-variable dynamic systems is proposed. A multi-layer Perceptron network is used as the process model and is adapted on-line using the extended Kalman filter to learn changes in process dynamics. In this way, the adaptive model will learn the post-fault dynamics caused by actuator or component faults. Then, the inversion of the neural model is used as a controller to maintain the system stability and control performance after fault occurrence. The convergence of the model inversion control is proved using Lyapunov method. The proposed method is applied to the simulation of a two-input two-output continuous-stirred tank reactor to demonstrate the effectiveness of the approach. Several actuator and component faults are simulated on the continuously stirred tank reactor process when the system is under the proposed fault tolerant control. The results have shown a fast recovery of tracking performance and the maintained stability.     

Robust adaptive fault estimation and fault tolerant control for quadrotor attitude systems

ABSTRACT

This paper addresses the problem of fault estimation and fault tolerant control for quadrotor unmanned aerial vehicle. Firstly, a robust adaptive fault estimation observer (AFEO) is proposed to achieve fault estimation of quadrotor with actuator fault in the presence of external disturbances and parameter uncertainties. Furthermore, based on the estimation of fault, a dynamic output feedback fault tolerant controller (DOFFTC) is designed to stabilise the closed-loop system with faults and uncertainties. Sufficient conditions for the existence of both AFEO and DOFFTC are given in terms of linear matrix inequalities. Finally, simulation results are presented to illustrate the effectiveness of the proposed strategy.     

Sensor fault tolerant control using sliding mode observers

Previous work has considered the use of sliding mode observers for fault detection and isolation (FDI) in uncertain linear systems whereby the unknown faults are reconstructed by appropriate processing of the so-called equivalent output error injection. The paper builds on this work and considers such a scheme within the broader context of fault tolerant control. Specifically, by correcting the faulty measurement by an estimate of the fault obtained from the sliding mode FDI scheme, good closed-loop performance is still maintained. An example of such a scheme, which has been implemented in real-time on a laboratory dc motor rig, is described.     

Robust adaptive fault tolerant control for a process with actuator faults

This paper studies design and implementation of an enhanced multivariable adaptive control scheme for an uncertain nonlinear process exposed to actuator faults. For adaptive fault compensation, a model reference adaptive control (MRAC) strategy is utilized as main controller. A new adaptation algorithm making possible to improve transient performance of adaptive control is integrated to the controller. With the help of further modifications, some restrictive conditions on multivariable adaptive design are relaxed so that the system requires less plant information. The resulting controller has a simpler structure than the other matrix factorization based controllers. At the final stage of design, a robust adaptive control scheme is obtained with consideration of practical implementation problems such as sensor noises, external disturbances and unmodeled​ system dynamics. It is proved that the controller guarantees closed-loop signal boundedness and asymptotic output tracking. Real-time experiment results acquired from quadruple tank benchmark system are presented in order to exhibit the effectiveness of the proposed scheme.     

Immersion and invariance based fault tolerant adaptive spacecraft attitude control

In this paper, an immersion and invariance (I&I) adaptive fault tolerant satellite attitude tracking control scheme is proposed. The proposed controller is capable of track the desired trajectory in the presence of unknown actuator multiplicative faults and unknown inertial matrix. Also based on Lyapunov direct method, all closed loop signals are proven to be globally asymptotically stable. The main advantage of this controller is improving closed loop performance while maintaining stability in the presence of unknown actuator faults. This method does not rely on certainty equivalence principle so it can be used to control the transient response of overall closed loop system by means of controlling the parameter estimation behavior which is not possible in traditional adaptive control. Numerical simulations are performed to demonstrate the effectiveness of proposed control scheme.     

Sensorless fault tolerant control for induction motors

In this paper, a sensorless fault tolerant controller for induction motors is developed. In the proposed approach, a robust controller based on backstepping strategy is designed in order to compensate for both the load torque disturbance and the rotor resistance variation caused by the broken rotor bars faults. The proposed approach needs neither fault detection and isolation schemes nor controller re-design. Moreover, to avoid the use of speed and flux sensors, a second order sliding mode observer is introduced to estimate the flux and the speed. The observer converges in a finite time and leads to good estimates of the flux and the speed even in the presence of the rotor resistance variation and the load torque disturbance. Since the observer converges in the finite time, the stability of the closed-loop system (controller with observer) is shown in two steps. First, the boundedness of the closed-loop system trajectories before the convergence of the observer is proved. Second, the convergence of the closed-loop system trajectories is proved after the convergence of the observer. To highlight the efficiency and applicability of the proposed control scheme, simulation and experimental results are conducted for a 1.5 kW induction motor.     

Microcomputer based adaptive digital control for slow non-linear processes: least squares approach

Determining the tuning coefficients of a classical three term digital controller for slow delay-time processes is a laborious task. The results obtained using the least squares method of determining the tuning coefficients of a classical three term controller, employed for adaptive control of a slow second-order process with delay-lime, are described. The method requires limited and possibly inaccurate information on the parameters of the process. The adaptability of the algorithm is also demonstrated.     

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.     

Fuzzy adaptive nonlinear sensor‐fault tolerant control for a quadrotor unmanned aerial vehicle

In this paper, a novel fuzzy adaptive nonlinear fault tolerant control design scheme is proposed for attitude dynamics of quadrotor UAV subjected to four sensor faults (bias, drift, loss of accuracy, loss of effectiveness). The sensor faults in Euler angle loop are transformed equivalently into a mismatched uncertainty vector, and other unknown items involving faults, uncertain parameters and external disturbances in angular velocity loop are lumped into an unknown nonlinear function vector. Fuzzy logic systems with adaptive parameters are used to approximate the mismatched uncertainty and lumped nonlinear function vectors. Dynamic surface control is applied to design the fault tolerant controller, and sliding mode control is introduced to improve the control accuracy. All signals of the closed‐loop control system are proved to be semi‐global uniformly ultimately bounded. Simulations demonstrate the effectiveness of the proposed approach for sensor faults.     

可重构模块机器人分散容错控制

针对可重构模块机器人的执行器故障,提出一种基于自适应模糊系统的分散被动容错控制方法.该方法不需要机器人动力学模型与模块之间的信息交换,模块控制器分别采用间接和直接自适应方法设计,自适应参数的更新律基于Lyapunov稳定性理论设计,保证了系统的稳定性和H∞跟踪性能.数值仿真结果表明了所提出方法的有效性.     

Robust fast adaptive fault estimation and tolerant control for T-S fuzzy systems with interval time-varying delay

This paper studies the problem of fault estimation (FE) and the active fault tolerant control (FTC) for Takagi–Sugeno (T-S) fuzzy systems with interval time-varying delay and norm-bounded external disturbance. Based on the fast adaptive fault estimation (FAFE) algorithm, our attention focuses on designing an adaptive observer-based controller to guarantee the filtering error system to be asymptotically stable and satisfy theH_∞ performance index. By constructing a new Lyapunov–Krasovskii functional including the information of the lower and upper delay bounds, the sufficient delay-dependent conditions have been established to guarantee the existence of adaptive observer-based controller in terms of linear matrix inequalities (LMIs). Compared with the constant delay and time-varying delay, the interval time-varying delay is the less conservative form. Furthermore, we make full use of the information of the delay and no terms are ignored when the stability of the system is analysed. In addition, the results for the systems with time-varying structured uncertainties are established. The results of the active FTC are showed in terms of LMIs. Finally, two examples are given to verify the effectiveness of the proposed method.     

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.     

Positive invariant sets for fault tolerant multisensor control schemes

This article deals with fault tolerant multisensor control schemes for systems with linear dynamics. Positive invariance is a common analysis and control design tool for systems affected by bounded constraints and disturbances. This article revisits the construction of ε-approximations of minimal robust positive invariant sets for linear systems upon contractive set-iterations. The cases of switching between different sets of disturbances and the inclusion of a predefined region of the state space are treated in detail. All these results are used in multisensor control schemes which have to deal with specific problems originated by the switching between different estimators and by the presence of faults in some of the sensors. The construction of positive invariant sets for different operating regimes provides, in this context, effective fault detection information. Within the same framework, global stability of the switching strategies can be assured if the invariant sets topology allows the exclusive selection of estimates obtained from healthy sensors.     

Differential geometry based active fault tolerant control for aircraft

This work shows how to use a differential geometry tool to design a novel nonlinear active fault tolerant flight control system for aircraft. The proposed control scheme consists of two main subsystems: a controller, which is designed for the nominal plant, and a fault detection and diagnosis module, which provides fault estimation. A further feedback loop exploits the fault estimation to accommodate faults affecting the system. The estimate convergence and the stability of the active fault tolerant flight controller are theoretically proved. Finally, high fidelity simulations show the effectiveness of the scheme.     

Active fault tolerant control for switched positive linear systems

The main aim of this paper is to design an active fault tolerant controller for switched positive linear systems. A theorem is proved for fault and state estimation of switched positive linear systems in terms of matrix inequality by considering average dwell‐time approach. By utilizing the theorem results, not only a fast and exact estimation of fault and state is obtained but also the positivity of state estimation is ensured. The feasibility problem is solved by formulating it into a special sequential optimization problem subject to LMI constraints. Based on the fault estimation information, an observer‐based fault tolerant control guaranties the stability and positivity of the closed‐loop system. Finally, a practical example including a data communication network is presented to illustrate the efficiency of the proposed method.     

非线性系统主动容错控制综述

以故障发生部位为分类视角,分别从传感器故障、执行器故障及其他部件故障3个方面,较为详细地对近5年非线性系统主动容错控制的研究进展情况进行了归纳和总结,重点讨论了执行器故障的容错控制问题,并对所存在的问题与未来的发展趋势进行了探讨。     

航空发动机容错控制系统设计

航空发动机是一个结构复杂、非线性强的多变量控制对象.控制系统的高可靠性是最重要的技术指标之一.而容错控制为提高系统的可靠性、可维护性和有效性开辟了一条新途径.文中介绍了基于航空发动机容错控制的基本原理和方法,研究了控制系统在反馈信号故障情况下的容错控制问题,该系统可以对失效的检测数据做出实时估计并反馈信号,从而达到对故障容错的目的.     

Actuator fault monitoring and fault tolerant control in distillation columns

This paper presents, from a practical viewpoint, an investigation of real-time actuator fault detection, propagation and accommodation in distillation columns. Addressing faults in industrial processes, coupled with the growing demand for higher performance, improved safety and reliability necessitates implementation of less complex alternative control strategies in the events of malfunctions in actuators, sensors and or other system components. This work demonstrates frugality in the design and implementation of fault tolerant control system by integrating fault detection and diagnosis techniques with simple active restructurable feedback controllers and with backup feedback signals and switchable reference points to accommodate actuator fault in distillation columns based on a priori assessed control structures. A multivariate statistical process monitoring based fault detection and diagnosis technique through dynamic principal components analysis is integrated with one-point control or alternative control structure for prompt and effective fault detection, isolation and accommodation. The work also investigates effects of disturbances on fault propagation and detection. Specifically, the reflux and vapor boil-up control strategy used for a binary distillation column during normal operation is switched to one point control of the more valued product by utilizing the remaining healthy actuator. The proposed approach was implemented on two distillation processes: a simulated methanol-water separation column and the benchmark Shell standard heavy oil fractionation process to assess its effectiveness.     

Active fault tolerant control using state-space self-tuning control approach

This paper presents a new fault tolerant control scheme for unknown multivariable stochastic systems by modifying the conventional state-space self-tuning control approach. For the detection of faults, a quantitative criterion is developed by comparing the innovation process errors occurring in the Kalman filter estimation algorithm, which, for faulty system recovery, a weighting matrix resetting technique is developed by adjusting and resetting the covariance matrices of the parameter estimate obtained in the Kalman filter estimation algorithm to improve the parameter estimation of the faulty systems. The proposed method can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection. The modified state-space self-tuning control scheme can be applied to the multivariable stochastic faulty system without requiring prior knowledge of system parameters and noise properties.