Fault-tolerant Control Structure Research Articles

Finite-time state-constrained adaptive fault-tolerant control for heavy launch vehicles with input quantization

This paper proposes the finite-time state-constrained adaptive fault-tolerant control method for a class of heavy launch vehicles subject to quantized input signals and actuator faults. Firstly, the attitude dynamic model of the heavy launch vehicles suffering from the actuator faults and disturbances is formulated. Secondly, to deal with the completely unknown nonlinear functions, radial basis function neural networks (RBFNNs) are introduced for approximation and compensation. Meanwhile, the barrier Lyapunov function (BLF) is introduced to ensure that all the states in the closed-loop system are bounded and the constraints of the system states are satisfied. As a result, the finite-time state-constrained adaptive fault-tolerant control structure is constructed for the heavy launch vehicles and the semi-globally practical finite-time stability (SGPFS) of the closed-loop control system is proved. Finally, numerical simulation results show the effectiveness and the satisfactory performance of the proposed control algorithm.

Learning Observer-Based Sensor Fault-Tolerant Control of Distributed Generation in an Islanded Microgrid for Bus Voltage Stability Enhancement.

In order to improve robust operating performance and enhance bus voltage stability, a learning observer-based fault-tolerant control strategy is proposed for the distributed generation in islanded microgrid with sensor faults and uncertain disturbances. Firstly, the output feedback control theory and the linear matrix inequality method are used to design closed-loop controller for the voltage source inverter of distributed generation; secondly, a fault-tolerant model and control structure of the distributed generation in an islanded microgrid with sensor faults is analyzed. By employing the fault output signal conversion filter and proportional derivative type learning observer, the online estimation and real-time compensation of the sensor fault signal are realized. Thirdly, the system synthesis of output feedback control and fault-tolerant control is completed. Finally, the multi-scenario sensor fault scheme simulation experiment verifies that the proposed control strategy has strong sensor fault tolerance and adaptability.

Classification of Single Current Sensor Failures in Fault-Tolerant Induction Motor Drive Using Neural Network Approach

In the modern induction motor (IM) drive system, the fault-tolerant control (FTC) solution is becoming more and more popular. This approach significantly increases the security of the system. To choose the best control strategy, fault detection (FD) and fault classification (FC) methods are required. Current sensors (CS) are one of the measuring devices that can be damaged, which in the case of the drive system with IM precludes the correct operation of vector control structures. Due to the need to ensure current feedback and the operation of flux estimators, it is necessary to immediately compensate for the detected damage and classify its type. In the case of the IM drives, there are individual suggestions regarding methods of classifying the type of CS damage during drive operation. This article proposes the use of the classical multilayer perceptron (MLP) neural network to implement the CS neural fault classifier. The online work of this classifier was coordinated with the active FTC structure, which contained an algorithm for the detection and compensation of failure of one of the two CSs used in the rotor field-oriented control (DRFOC) structure. This article describes this structure and the method of designing the neural fault classifier (NN-FC). The operation of the NN-FC was verified by simulation tests of the drive system with an integrated FTC strategy. These tests showed the high efficiency of the developed fault classifier operating in the post-fault mode after compensating the previously detected CS fault and ensuring uninterrupted operation of the drive system.

A robust and fast active fault tolerant control for quadrotor applications with actuator fault’s unknown statistical characteristics

Fault tolerant control (FTC) methods considering known faults for quadrotor applications are presented; however, unknown statistical faults characteristics particularly for the FTC design is not discussed. This study tackles this problem and presents a robust two-stage unscented Kalman filter (RTSUKF) that is robust against unknown faults and is able to accurately estimate multi-actuator faults with unknown statistical characteristics. The estimated unknown faults are then compensated through second-order sliding mode controllers which are designed for each of the translational and rotational movements. The fast convergence speed of the proposed estimator reduces the instability time interval of the quadrotor after fault occurrence. To discuss the reliability of the proposed method, stability analysis based on the Lyapunov theory is carried out for the whole closed-loop system. Finally, the robustness of the proposed FTC structure against unknown faults with its convergence speed are validated through numerical analysis. The results confirm the effective and good performance of the proposed method compared to another FTC with a conventional two-stage unscented Kalman filter.

Parameter-dependent Lyapunov function based fault estimation and fault-tolerant control for LPV systems

In this paper, an integrated fault estimation (FE) and fault-tolerant control (FTC) method is proposed for linear parameter varying (LPV) systems using the parameter-dependent Lyapunov function. The observer-based estimation and fault-tolerant control structure causes an uncertainty between systems which is called the bi-directional uncertainty. Additionally, with simultaneous actuator and sensor faults, disturbances, the overall performance of the FTC system decreases. To maintain robust stability of closed-loop systems, an unknown input observer fault estimator approach with both state feedback control and sliding mode control is implemented as a solution by solving optimisation using a linear matrix inequality (LMI) formulation. The efficiency of the integrated FE/FTC design is demonstrated using the pitch-axis missile LPV model and simulated to evaluate the merits of the suggested approach.

Accessibility, Observability, and Fault-Tolerant Control Structure Selection of Network Nonlinear Systems

An appropriate control structure is the prerequisite to guarantee the controllability and the observability of a system. This article addresses the issue of fault-tolerant control structure selection (CSS) problem for a class of network nonlinear systems (NNSs) with multidimensional nodes. First, based on differential algebra theory, necessary and sufficient accessibility/observability conditions are established, respectively, for NNSs, which fully reveal the relations between the accessibility/observability and the network structure as well as external inputs/outputs. Second, a fault-tolerant CSS-based recovering strategy is provided that adds minimal inputs/outputs/edges to recover the accessibility and observability of the faulty NNS. Finally, the new strategy is applied to examples of chemical reaction and multiple aircrafts to illustrate the effectiveness of the theoretical results.

Design of an Improved Hybrid FTC for Faults in Aero-Engine Closed-Loop Control System

Due to the aero-engines in a high temperature, pressure, and spool speed environment, the concurrent faults in actuator/sensor combined with degradation of the aero-engine could arise the problem of closed-loop instability during the flight. For this purpose, an improved hybrid fault-tolerant control (FTC) technique has been studied in this paper to deal with simultaneous failure of actuators and sensors associated with health parameters of the typical components’ degradation in aero-engines. The improved hybrid FTC structure combined with the nonlinear thermodynamic component-level (NCL) model-based estimation method merges fault estimation of actuators/sensors and typical components’ degradation estimation process into the FTC process. A robust H ∞ state feedback controller under the disturbance of simultaneous actuator and sensor faults is designed in the proposed method, together with the switching algorithm serving for the fault estimation and improved hybrid FTC channels. In order to show the feasibility of the proposed method, several semiphysical experiments are engaged to illustrate that the improved hybrid FTC structure can save the tolerant-control time and improve performance of the control system.

Memory Augmented Neural Network-Based Intelligent Adaptive Fault Tolerant Control for a Class of Launch Vehicles Using Second-Order Disturbance Observer

This paper focuses on the MANN-based intelligent adaptive fault tolerant control for a class of launch vehicles. Firstly, the attitude dynamic model of the launch vehicles suffering from the actuator faults and disturbances has been formulated. Secondly, the second-order disturbance observer has been designed for the launch vehicle to achieve the exact estimation and compensation of the time-varying disturbances. Meanwhile, the MANN has been introduced as online approximator, suppressing the adverse influence of the unknown nonlinearities. Moreover, several adaptive laws have been proposed to achieve the quick response to the actuator faults and the update of the MANN weights. As a result, the MANN-based intelligent adaptive fault tolerant control structure has been constructed for the launch vehicles. It has been proven that all the signals in the closed-loop system are bounded. Simulation results demonstrate the desired performance and the advantages of the proposed control algorithm.

A Current Sensor Fault Tolerant Control Strategy for PMSM Drive Systems Based on Cri Markers

The paper describes a vector-controlled fault tolerant control (FTC) structure for permanent magnet synchronous motor (PMSM) drives. As a control algorithm, the classical field oriented control was applied. For the proper operation of this drive, minimum two current sensors are necessary, however, in the FTC drives additional redundant transducers are applied. Each measuring sensor, including current sensors, are susceptible to damage and can lead to unstable operation of the drive. The presented control structure, with a diagnostic and compensation system, is robust to the typical current sensor faults—lack of signal, intermittent signal, variable gain, signal noise and signal saturation. The fault detection algorithm is based on the signal method. The fault diagnostic system is tested in two control algorithms—the scalar control and vector control ones—to demonstrate the transient of the faulted signals, detection signals and detection time. After current sensor fault appearance, its influence on the control structure, especially speed transient, is compensated using non-sensitive components. The analysis is presented for all the abovementioned faults for different speed conditions.

SODO Based Reinforcement Learning Anti-Disturbance Fault Toler-Ant Control for a Class of Nonlinear Uncertain Systems With Matched and Mismatched Disturbances

This paper proposes a reinforcement learning anti-disturbance fault tolerant control structure for a class of nonlinear uncertain systems with time varying matched and mismatched disturbances. To deal with the time varying matched and mismatched disturbances, two second order disturbance observers (SODOs) are designed for the inner and outer loop dynamic equations. For the purpose of enhancing the robustness and adaptivity with respect to the system uncertainties, two long short-term memory (LSTM) networks those possesses perfect fitting ability, have been introduced as the critic and actor networks. Moreover, to overcome the difficulty caused by the unknown perturbations of the control effectiveness, several fault tolerant adaptive laws have been designed. Consequently, a novel reinforcement learning anti-disturbance fault tolerant control structure has been established for the concerned disturbed nonlinear uncertain system. Two numerical examples are provided finally, demonstrating the satisfactory performance of the proposed control structure.

Model free adaptive fault-tolerant tracking control for a class of discrete-time systems

This paper investigates the fault-tolerant tracking control issue for a class of discrete-time systems with sensor fault. A novel model-free adaptive fault-tolerant tracking control scheme is proposed by utilizing the dynamic linearization approach, in which only input/output (I/O) data of controlled plant are required. Moreover, due to the characteristics of simple structure and powerful approximation ability, RBF neural network (RBFNN) is introduced to approximate the fault dynamics after the sensor fault is detected. The approximated fault dynamic is utilized to reconstruct the controller. The major feature of this paper is that the whole fault-tolerant tracking controller structure is derived from the dynamic linearization of the ideal controller, rather than the criterion function. Finally, the proposed model-free adaptive fault-tolerant tracking controller is verified to be effective by numerical simulations.

Virtual Current Sensor in the Fault-Tolerant Field-Oriented Control Structure of an Induction Motor Drive.

Designing electrical drives resistant to the failures of chosen sensors has recently become increasingly popular due to the possibility of their use in fault-tolerant control (FTC) systems including drives for electric vehicles. In this article, a virtual current sensor (VCS) based on an algorithmic method for the reconstruction of the induction motor (IM) phase currents after current sensor faults was proposed. This stator current estimator is based only on the measurements of the DC-bus voltage in the intermediate circuit of the voltage-source inverter (VSI) and a rotor speed. This proposal is dedicated to fault-tolerant vector controlled IM drives, where it is necessary to switch to scalar control as a result of damage to the current sensors. The proposed VCS allows further uninterrupted operation of the direct rotor-field oriented control (DRFOC) of the induction motor drive. The stator current estimator has been presented in the form of equations, enabling its practical implementation in a microprocessor system. Simulation studies of the proposed algorithm in an open and closed-loop DRFOC structure are presented under different operation conditions of the drive system. The experimental verification of the proposed method is also presented and the accuracy of the stator current estimation algorithm is analyzed under various operating conditions of the drive system.

Reconfigurable control of flexible joint robot with actuator fault and uncertainty

Abstract This paper presents the fault tolerant control (FTC) of a flexible joint robot using singular perturbation method in order to compensate for the lost performance due to the occurrence of actuator fault and the uncertainty. This FTC is based on Lyapunov redesign principle. The singular perturbation method is used to reduce the dynamic model of the flexible joint robot in a fast and slow subsystem. The time scale reduction of the flexible joint model is carried out when their joint stiffness is large enough and the singular perturbation parameter is set to zero. The fault-tolerant control structure in this paper is based on two parts. The first term described the composite control for the system without defect and without uncertainty which represents the sum between slow and fast controllers. While the second term of the fault tolerant command describes additive control designed to compensate for the fault effect of the actuator on the uncertain system. The additive approach is based on the Lyapunov theorem, which guarantees asymptotic stability despite the presence of actuator defects and the parametric uncertainty. The theoretical results are applied on a robot manipulator with a single flexible joint.

Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive.

This paper describes a Fault Tolerant Control structure for the Induction Motor (IM) drive. We analyzed the influence of current sensor faults on the properties of the vector-controlled IM drive system. As a control algorithm, the Direct Field Oriented Control structure was chosen. For the proper operation of this system and for other vector algorithms, information about the stator currents components is required. It is important to monitor and detect these sensor faults, especially in drives with an increased safety level. We discuss the possibility of the neural network application in detecting stator current sensor faults in the vector control algorithm. Simulation and experimental results for various drive conditions are presented.

Adaptive Fuzzy Tracking Control for Strict-Feedback Markov Jumping Nonlinear Systems With Actuator Failures and Unmodeled Dynamics.

In this paper, an adaptive fuzzy tracking controller is developed for a class of strict-feedback Markovian jumping systems subjected to multisource uncertainties. The unpredictable actuator failures, the unknown nonlinearities, and the unmodeled dynamics are simultaneously taken into consideration, which evolve according to the Markov chain. It is noted that the elements in the transition rate matrix of the Markov chain are not fully available. In virtue of the norm estimation approach, the challenges caused by the complex multiple uncertainties and actuator failures are effectively handled. Furthermore, to compensate for the unavailable switching nonlinearities, the fuzzy logic systems are employed as online approximators. As a result, a novel adaptive fuzzy fault-tolerant tracking control structure is constructed. The sufficient condition is provided to guarantee that the studied system is stochastically stable. Finally, a number of illustrative examples are employed to demonstrate the effectiveness of the proposed methodology.

Actuator Fault Tolerant Control Proposal for PI Controlled SISO Systems

In this work, we develop a control structure which can be added to an existing closed loop in order to mitigate the effect of actuator faults. We analyze the initial performance of the closed loop in terms of robustness and time response under references, faults and measurement noises. In the design of the proposed active fault tolerant control structure, we keep the initial robustness and time response to references. At the same time, we try to improve some performance indices under faults at the cost of a higher control action activity caused by the measurement noises that affect the system. The design of the controller depends on a unique parameter so it can be easily understood. We show that although the response under step faults becomes oscillatory, the active fault tolerant structure reduces the integral of the absolute value of the tracking error under step faults and it attenuates the effect of ramp faults in steady state. Several examples show the goodness and drawbacks of the approach and show some aspects to be considered in the design.

Self-tuned local feedback gain based decentralized fault tolerant control for a class of large-scale nonlinear systems

In this paper, a decentralized fault tolerant control (DFTC) scheme is proposed for a class of large-scale nonlinear systems based on self-tuned local feedback gain against partial loss of actuator effectiveness (PLOAE). Consider a large-scale nonlinear system as a set of interconnected subsystems, a decentralized control method is proposed by employing two radial basis function neural networks (RBFNNs) for the fault-free system. Then, the unknown system is identified using RBFNNs. By establishing a decentralized observer, the derived self-tuned local feedback gain is placed before the proposed decentralized controller to guarantee control performance for the subsystem suffering from PLOAE fault. Finally, simulation examples are provided to demonstrate the effectiveness of the present DFTC scheme. The main contributions of this paper are: i) The unknown large-scale nonlinear system can be identified using locally measured states, so the actuator fault can be handled in its local subsystem. It implies that the performance degradation of the faulty subsystem cannot affect the fault-free subsystems. ii) The estimated effectiveness factor is placed before the proposed decentralized scheme. The fault tolerant control structure is simple since it does not need to be redesigned in the case of PLOAE.

Autonomous Flight of Hexacopter Under Propulsion System Failure

[abstFig src='/00280006/14.jpg' width='250' text='Fault tolerant control structure of hexacopter' ] This paper presents a fault-tolerant approach for the propulsion systems of hexacopters (i.e., rotors and propellers) to overcome failures during outdoor autonomous flight missions. In this study, we used an explicit control allocation method for each stopped motor, and an asymmetrical motor rotation arrangement is applied in order to guarantee the controllability of the yaw. Finally, the developed fault diagnosis and isolation system is tested during a global-positioning-system-based autonomous flight of a hexacopter with a failed motor.

Active fault-tolerant linear parameter varying control for the pitch actuator of wind turbines

This paper proposes a fault-tolerant control method using the virtual actuator for the problem of the pitch actuator failure. The simplified second-order transfer function of the pitch actuator is transformed into the identification equation using the Euler transformation. For the purpose of fault estimation, a least-squares batch identification algorithm is adopted to estimate the time-varying natural frequency and damping ratio. Then, according to the convex decomposition theory, the nonlinear model of wind turbines is converted to a linear parameter variation model. The fault-tolerant control structure of the virtual actuator is obtained by applying the state feedback law. In the light of the polyhedral approximation theory, an infinite number of linear matrix inequalities can be transformed into a finite number of linear matrix inequalities. The feedback controller is obtained after solving finite linear matrix inequalities. Finally, the simulation results show that the fault-tolerant control method with the fault estimation is capable of handling the pitch actuator failure and maintaining a constant power output.

Fault Tolerant Design for Permanent Magnet Synchronous Motor using Fuzzy Speed Controller

In recent years, permanent magnet synchronous motors (PMSM) are making significant inroads because of robustness, high efficiency and widely used in industrial applications. However, the un-modeled dynamics, the eventual mistakes and the strong nonlinearity diminish the motor performance quality. Indeed, since its synthesis is based on heuristic knowledge, linguistic description to perform a task and does not require a system model, the fuzzy logic control (FLC) idea is successfully applied to motor systems. Despite these advantages, the occurrence of failure may dramatically degrade the system performance and may even result in catastrophic system collapse. Therefore, in order to overcome with the above drawbacks in this study a new fault tolerant control (FTC) structure based FLC is designed to improve PMSM drives currents and speed controls during healthy and faulty conditions. Numerical results are given to highlight and validate the proposed control strategies.