Proposed Fault Tolerant Control Method Research Articles

Switch Short-Circuit Fault-Tolerant Control of Five-Phase Open-Winding Motor Drive With a Floating Capacitor

This paper proposes a switch short-circuit (SC) fault-tolerant control (FTC) method for the open-winding topology with a floating capacitor, where the main inverter is supplied by a DC source, and the floating inverter is merely supplied by a capacitor. The SC faults are divided into two types and analyzed in detail. For the fault of Type I, the proposed reconfiguration strategy can isolate the faulty device to ensure stable operation. For the fault of Type II, the control of zero-axis voltage in the topology is introduced to replace the control of the faulty bridge. Besides, the fault-tolerant power smoothing strategy is applied to suppress the low-frequency ripples of the floating capacitor voltage and input current from the DC source, which further improves the torque quality. Compared with the previous method, the proposed FTC method does not require additional hardware costs to convert the short-circuit to an open-circuit. Meanwhile, the motor can still give out rated torque even after the fault. The performance of the proposed FTC method is verified experimentally in an open-winding five-phase induction machine system.

A fault-tolerant control strategy to estimate and compensate the temperature sensor bias in supermarket refrigeration systems

The paper proposes a data-driven fault-tolerant control (FTC) strategy to construct and accommodate the bias on ambient temperature measurements in supermarket refrigeration systems. The bias, which is caused by direct or indirect exposure of the sensor to the sun, can have a significant impact on the refrigeration system’s energy consumption. Based on analysis of the real data a comprehensive model of the bias is developed and then used to generate realistic scenarios for testing the proposed FTC method. The FTC method uses a feed forward Neural Network (NN) as a black box model. The model is trained by active injection of perturbation signals during the night operations. During the Monte-Carlo tests, the strategy was implemented in a Plug & Play manner, demonstrating that substantial energy savings can be achieved during summer periods.

Dynamic learning-based fault tolerant control for robotic manipulators with actuator faults

In this paper, a learning-based active fault-tolerant control (FTC) scheme for robot manipulators with uncertainties and actuator faults is proposed. Unlike traditional FTC methods, with dynamic learning theory, both uncertainties and actuator faults can be accurately identified/learned by radial basis function networks. Based on the learned knowledge, dynamical classifiers and experience-based controllers corresponding to different fault modes are constructed. With the help of dynamical classifiers, fault detection and isolation can be obtained rapidly and accurately, and the correct experience-based controller (instead of the controller reconfigured online) corresponding to the current fault system is selected to compensate for faults, and superior control performance is achieved, even in the presence of faults. The simulation studies demonstrate the feasibility of the proposed FTC method.

Single current sensor based fault tolerant control of interior permanent magnet synchronous machine for drive applications

This paper presents an integrated method for current sensor fault detection and fault tolerant control (FTC) for traction interior permanent magnet synchronous motor (IPMSM). The proposed current sensor fault detection method is based on detecting changes in the d-q axis current. The FTC is based on d-q axis current estimation from the reference d-q axis current and the phase current measured by the surviving current sensor. The current estimation process is independent of machine parameters. Hence the estimation is robust and requires less computational cost. The effectiveness of the FTC method is verified by the transient analysis. Such FTC is suitable for electric vehicle traction applications to ensure non-stop control operation of the drive in the entire range of speed. The efficacy of the proposed FTC method is tested through extensive simulations in MATLAB/Simulink environment. The real-time applicability of the proposed FTC method using the cost-effective digital signal processor (DSP) is verified on Texas Instruments© TMSF28379D through the processor in loop (PIL) simulation model.

A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

In this paper, we investigate the use of Model Predictive Control (MPC) applications for quasi-Linear Parameter Varying (qLPV) systems subject to faults along the input channels. We propose a Fault Tolerant Control (FTC) mechanism based on a robust state-feedback MPC synthesis, considering polytopic inclusions. In order to alleviate the numerical burden of the robust min-max procedure, we use small prediction horizons, in such a way that the solution becomes viable for real-time systems. The FTC system is able to tolerate time-varying saturation of the actuator, which may happen due to malfunctions. Recursive feasibility and poly-quadratic stability guarantees are ensured through the synthesis of adequate terminal ingredients. Accordingly, we present a catalogue of three different LMI remedies, considering: (a) parameter-independent ingredients, (b) a parameter-dependent terms and (c) a parameter-dependent maps that take into account bounded rates of parameter variation. An autonomous driving car example is used to illustrate the performances of the proposed technique, which is compared to other MPCs from the literature. The proposed FTC method is able to ensure good performances, obtained with reduced computational demand.

A novel sliding mode fault‐tolerant control strategy for variable‐mass quadrotor

AbstractIn this article, we investigate a fault‐tolerant control (FTC) for a class of quadrotors with variable mass and actuator faults. Firstly, a mathematical model of quadrotors considered variable‐mass factor is established based on Euler Newton's formula. Secondly, an adaptive estimation algorithm based on a fault observer is designed to achieve the estimations of variable mass and actuator fault, respectively. With the aim of obtaining satisfactory trajectory tracking performance, utilizing the variable mass estimation, a nonsingular terminal sliding mode controller is designed to track desired position. Meanwhile, utilizing the information of actuator fault estimation, an integral sliding mode controller is designed to track desired attitude. Finally, by comparison with the existing sliding mode control (SMC) and other FTC algorithm, the results show that the proposed FTC method has better tracking performance in the case of variable mass, fault and disturbance.

Short-Circuit Fault-Tolerant Control Without Constraint on the D-Axis Armature Magnetomotive Force for Five-Phase PMSM

In this article, we investigate the short-circuit (SC) fault-tolerant control (FTC) method for a five-phase permanent-magnet synchronous machine (PMSM) with surface-mounted permanent magnets. By relieving the constraint of zero <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis armature magnetomotive force (MMF) and restraining the backward-rotating MMF components to be zero, round-rotating armature MMF with maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -axis armature MMF is achieved, which enables five-phase PMSM to output maximum smooth torque with lower losses and higher efficiency under SC fault condition. To ensure smooth postfault operation in full-speed range, the influence of winding resistance on SC current is further considered, which improves low-speed operation performance. The proposed FTC method features sinusoidal currents with equal amplitude, which ensures better control simplicity and postfault thermal uniformity between phases. The finite-element analysis and experiments are carried out to verify the proposed method.

Adaptive fault-tolerant control for nonlinear high-order fully-actuated systems

In this paper, an adaptive fault-tolerant control (FTC) strategy is proposed for a class of high-order fully-actuated (HOFA) systems. A pre-closed-loop treatment is presented to simplify the observer design for the nonlinear system with actuator faults but it leads to a new problem that faults may not satisfy the observation matching conditions. Thus, an intermediate variable observer is introduced to adaptively estimate system states and actuator faults simultaneously. Then the influence of observation errors on system stability is analyzed by analyzing matrix singular value perturbation. Under the proposed adaptive FTC method, the closed-loop global uniformly ultimately bounded stability is achieved by Lyapunov synthesis. Finally, the effectiveness of the proposed FTC method is verified by simulations.

Fault-Tolerant Control of Skid Steering Vehicles Based on Meta-Reinforcement Learning with Situation Embedding

Meta-reinforcement learning (meta-RL), used in the fault-tolerant control (FTC) problem, learns a meta-trained model from a set of fault situations that have a high-level similarity. However, in the real world, skid-steering vehicles might experience different types of fault situations. The use of a single initial meta-trained model limits the ability to learn different types of fault situations that do not possess a strong similarity. In this paper, we propose a novel FTC method to mitigate this limitation, by meta-training multiple initial meta-trained models and selecting the most suitable model to adapt to the fault situation. The proposed FTC method is based on the meta deep deterministic policy gradient (meta-DDPG) algorithm, which includes an offline stage and an online stage. In the offline stage, we first train multiple meta-trained models corresponding to different types of fault situations, and then a situation embedding model is trained with the state-transition data generated from meta-trained models. In the online stage, the most suitable meta-trained model is selected to adapt to the current fault situation. The simulation results demonstrate that the proposed FTC method allows skid-steering vehicles to adapt to different types of fault situations stably, while requiring significantly fewer fine-tuning steps than the baseline.

Metalearning-Based Fault-Tolerant Control for Skid Steering Vehicles under Actuator Fault Conditions.

Using reinforcement learning (RL) for torque distribution of skid steering vehicles has attracted increasing attention recently. Various RL-based torque distribution methods have been proposed to deal with this classical vehicle control problem, achieving a better performance than traditional control methods. However, most RL-based methods focus only on improving the performance of skid steering vehicles, while actuator faults that may lead to unsafe conditions or catastrophic events are frequently omitted in existing control schemes. This study proposes a meta-RL-based fault-tolerant control (FTC) method to improve the tracking performance of vehicles in the case of actuator faults. Based on meta deep deterministic policy gradient (meta-DDPG), the proposed FTC method has a representative gradient-based metalearning algorithm workflow, which includes an offline stage and an online stage. In the offline stage, an experience replay buffer with various actuator faults is constructed to provide data for training the metatraining model; then, the metatrained model is used to develop an online meta-RL update method to quickly adapt its control policy to actuator fault conditions. Simulations of four scenarios demonstrate that the proposed FTC method can achieve a high performance and adapt to actuator fault conditions stably.

Neural-network-based fault-tolerant control for nonlinear systems subjected to faults and saturations

This paper investigates a novel strategy which can address the fault-tolerant control (FTC) problem for nonlinear strict-feedback systems containing actuator saturation, unknown external disturbances, and faults related to actuators and components. In such method, the unknown dynamics including faults and disturbances are approximated by resorting to Neural-Networks (NNs) technique. Meanwhile, a back-stepping technique is employed to build a fault-tolerant controller. It should be stressed that the main advantage of this strategy is that the NN weights are updated online based on gradient descent (GD) algorithm by minimizing the cost function with respect to NNs approximation error rather than regarding weights as adaptive parameters, which are designed according to Lyapunov theory. In addition, the convergence proof of NN weights and the stability proof of the proposed FTC method are given. Finally, simulation is performed to demonstrate the effectiveness of the proposed strategy in dealing with unknown external disturbances, actuator saturation and the faults related to the components and actuators, simultaneously.

Model Predictive Fault-Tolerant Tracking Control for PDF Control Systems With Packet Losses

In this article, a fault-tolerant tracking control strategy is investigated for nonlinear probability density function (PDF) control systems with the actuator fault, uncertainties, unknown disturbance, and random packet losses. The control input signal dropout and measurement signal dropouts are described as the independent Bernoulli distribution. An adaptive fault diagnosis (FD) observer based on the Lyapunov function is given to simultaneously estimate the fault, disturbance, and state with packet losses. Different from the traditional robust fault-tolerant control (FTC), a new active fault-tolerant tracking controller is designed based on the model predictive control framework, which has better adaptive fault-tolerant performance. Finally, the validity of the proposed FTC method has been proved by a simulation study of a papermaking process.

Fault-Tolerant Control for Systems With Unmatched Actuator Faults and Disturbances

A fault-tolerant control (FTC) scheme for a class of nonlinear systems with unmatched actuator redundancy and unmatched disturbances is proposed in this article. A methodology to construct unified smooth sliding mode control laws and update laws are proposed such that the equivalent injections of the first-order time derivatives of the unmatched actuator faults and unmatched disturbances can appear in the unmatched channels. The unmatched actuator faults and unmatched disturbances are completely canceled by these equivalent injections. Based on this methodology and using the backstepping design procedure, a set of smooth FTC sliding surfaces, FTC laws and update laws are then designed. With the help of the FTC law selecting mechanism, the output tracking errors of the closed-loop FTC system converge to zero asymptotically, and time-varying faults and disturbances are reconstructed. A simulation example is presented to illustrate the effectiveness of the proposed FTC method.

An Enhanced Fault Tolerant Control Against Current Sensor Failures in Induction Motor Drive by Applying Space Vector

In this paper, an enhanced active fault-tolerant control (FTC) is proposed to solve a current sensor failure in the induction motor drive (IMD) using two current sensors. The proposed FTC method applies only one observer to diagnose the faults and reconfigure the control signals by the space stator current. The diagnosis function is made up of a comparison algorithm between the measured current space vector and the estimated space vector. Then, incorrect feedback stator currents are replaced by the estimated values in the reconfiguration function. The amplitude of a healthy measured current is applied to adjusted the accuracy of estimated current signals. The IMD uses the field-oriented control (FOC) technique to control the speed and torque. The effectiveness in stabilizing the IMD system when a current sensor error occurs is verified by various simulations in the Matlab-Simulink environment This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Polynomial Observer-Based Controller Synthesis and Fault-Tolerant Control for Tracking Optimal Power of Wind Energy Conversion Systems

This article proposes a new approach to design a fault-tolerant control (FTC) scheme for tracking the optimal power of wind energy conversion systems (WECSs). In this article, the considered fault will not only impact on actuator but also sensors. As the fault severely affects the performance of WECSs, the FTC are required to be worked accurately and effectively. The polynomial observer, as a part of the proposed FTC system, is synthesized to estimate the aerodynamic torque, electromagnetic torque, and fault simultaneously without using sensors to measure. The information of these parameters is sent back to the LQR (Linear Quadratic Regular) controller of WECSs. Both fault and aerodynamic torque in this study are unnecessary to fulfil any constraint. It should be noted that WECSs is reconstructed to a new form based on the descriptor technique, then the observer will design for this new form instead of the original system. Based on Lyapunov methodology and with the aid of SOS (Sum-Of-Square) technique, the conditions for polynomial observer design are derived in the main theorems. Finally, the simulation results have proved the effectiveness and merit of the proposed FTC method.

Robust reliable fault tolerant control of islanded microgrids using augmented backstepping control

This study proposes a novel fault tolerant voltage control method considering actuator faults and disturbances by using the backstepping control augmented by a new differentiator for islanded microgrids (MGs). Existing voltage control methods are designed based on the ideal condition that the distributed generations' actuators work healthily with the assumption of the absence of faults and disturbances, whereas MGs are exposed to the actuator faults including partial loss of effectiveness and biased faults. The proposed controller robustly regulates the MG voltages irrespective of the actuator faults. In contrast to existing methods, the controller has considered both actuator faults and loads with harmonic/interharmonic currents, which does not need to know the exact model of faults and frequency of harmonic and interharmonic of MG loads. This feature enables the MG to work properly, even at the lowest level, instead of the system completely collapsing. Therefore, it improves the reliability of the MG system. The MATLAB/SimPowerSystems toolbox has verified the validity of the proposed fault tolerant control method. Compared with effective methods, both the theoretical and simulation results show that the proposed method has better, robust, resilient, acceptable, and desirable performance, with respect to the unknown faults, actuator faults, non-linear loads, and disturbances.

Fault-Tolerant Control for Electric Vehicles With Independently Driven in-Wheel Motors Considering Individual Driver Steering Characteristics

This paper presents a fault-tolerant control (FTC) method for electric vehicles with independently driven in-wheel-motors (IWM) considering individual human driver steering characteristics. Both the human driver steering model and the vehicle lateral dynamic model are formed for the FTC strategy design while taking into account modeling inaccuracies. Based on the driver-vehicle system model, a dual-loop, sliding-mode controller is designed to deal with the IWM fault and accomplish vehicle motion control in a customized way with explicit respect to individual driver steering characteristics. The proposed control strategy contains two layers: the first layer calculates a reference heading angle aiming to reduce the tracking error induced by the IWM actuator fault; the second layer computes the additional yaw moment needed to track the reference heading angle and suppress the influence of the modeling errors. Such an individualized FTC control method can specifically assist the human driver in post-fault vehicle motion control while reducing the physical and mental workloads of the human driver. Simulation results of the proposed FTC method using MATLAB/Simulink-CarSim demonstrate that it can accomplish the expected post-fault vehicle motion control while providing appropriate control assistance to different drivers in an individualized and cooperative way.

Dynamic output feedback-based fault-tolerant control design for T-S fuzzy systems with model uncertainties

This paper is concerned with fault-tolerant control (FTC) for a class of nonlinear discrete-time systems. A dynamic output feedback controller is designed for Takagi-Sugeno (T-S) fuzzy systems with state and input parameter uncertainties, exogenous disturbances, measurement noise and multiple intermittent faults. Compared with the existing research results, the proposed controller has a wider application range. The stability of the closed-loop systems is guaranteed by H∞ performance and sufficient conditions are proposed for the considered system with multiple sensor faults, multiple actuator faults, multiple sensor and actuator faults, respectively, by solving linear matrix inequalities (LMIs). Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed FTC method in the paper.

Adaptive sensor fault-tolerant control for a class of multi-input multi-output nonlinear systems: Adaptive first-order filter-based dynamic surface control approach

This paper is concerned with the adaptive fault-tolerant control (FTC) problem for a class of multivariable nonlinear systems with external disturbances, modeling errors and time-varying sensor faults. The bias, drift, loss of accuracy and loss of effectiveness faults can be effectively accommodated by this scheme. The dynamic surface control (DSC) technique and adaptive first-order filters are brought together to design an adaptive FTC scheme which can reduce significantly the computational burden and improve further the control performance. The adaptation laws are constructed using novel low-pass filter based modification terms which enable under high learning or modification gains to achieve robust, fast and high-accuracy estimation without incurring undesired high-frequency oscillations. It is proved that all signals in the closed-loop system are uniformly ultimately bounded and the tracking-errors can be made arbitrary close to zero. Simulation results are provided to verify the effectiveness and superiority of the proposed FTC method.

Fault-tolerant power extraction strategy for photovoltaic energy systems

Photovoltaic (PV) arrays are subject to various types of environmental disturbances and component-related faults that affect their normal operation and result in a considerable energy loss. The nonlinear current-voltage (I-V) characteristic curve of the PV array prevents the detection and isolation of the faults and also makes the tracking of the maximum power operating point (MPP) more difficult. Fault detection and identification (FDI) techniques methods have been proposed to detect the presence of faults and isolate them. Many maximum power point tracking (MPPT) methods have been proposed to find the best operating point in the presence of disturbed environmental conditions. However, existing FDI methods do not consider the tracking of the MPP in faulted operating conditions, and available MPP tracking methods do not consider the occurrence of faults in the PV system. The objective of this study is to propose a fault-tolerant control (FTC) strategy to detect the presence of abnormal operating conditions and reconfigure the MPPT procedure to search for the new suboptimal operating point. The FDI method is based on monitoring the PV panel generated power for the presence of abrupt changes; the MPPT reconfiguration is based on a combination between Incremental Conductance (IncCond) Algorithm and an Improved Current-based Particle Swarm Optimization (ICPSO) tracking technique. Simulation and experimental results show an excellent performance of the proposed FTC method in the presence of various types of faults.