Fault-tolerant Control Algorithm Research Articles

Verification of the MLP network-based current sensor fault classifier for vector-controlled AC motor drives

In modern drive systems, the aim is to ensure their operational safety. Damage can occur not only to the components of the motor itself but also to the power electronic devices included in the frequency converter and the sensors in the measurement circuit. Critical damage to the electric drive that makes its further exploitation impossible can be prevented by using fault-tolerant control (FTC) algorithms. These algorithms are very often combined with diagnostic methods that assess the degree and type of damage. In this paper, a fault classification algorithm using an artificial neural network (ANN) is analysed for stator phase current sensors in AC motor drives. The authors confirm that the investigated classification algorithm works equally well on two different AC motors without the need for significant modifications, such as retraining the neural network when transferring the algorithm to another object. The method uses a stator current estimator to replace faulty sensor measurements in a vector control structure. The measured and estimated currents are then subjected to a classification process using a multilayer perceptron (MLP), which has the advantage of a small structure size compared to deep learning structures. The uniqueness of the method lies in the use of data in the training set that are not dependent on the parameters of a specific motor. Four types of current sensor faults were studied, namely total signal loss, gain error, offset, and signal saturation. Simulations were performed in a MATLAB/SIMULINK environment for drive systems with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). The results show that the algorithm correctly evaluates the type of damage in more than 99.6% of cases regardless of the type of motor. Therefore, the results presented here may help to develop universal diagnostic methods that will work on a wide variety of motors.

FAULT-TOLERANT CONTROL ALGORITHM FOR AUTOMOTIVE ELECTRONIC MECHANICAL BRAKING RAMP ASSIST

The electronic mechanical braking system directly affects the driving safety of automobiles, but there are auxiliary control methods. Therefore, a fault-tolerant control algorithm for automotive electronic mechanical braking ramp assistance is proposed. The faults of the electronic mechanical braking ramp assistance system and select sensor faults are analyzed as the main research object. By introducing a BP neural network, the longitudinal speed of the vehicle, the wheel speed, the difference between the target slip rate and the current slip rate, and the rate of change of the difference are taken as the network input. Online learning is used to adjust and update the weight coefficients of each layer of the network, output fault diagnosis results and optimized PI controller parameters, and achieve fault-tolerant control. Three different types of sensor fault signals were artificially added in the experimental testing. After the proposed fault-tolerant control method, the controlled signals were used to replace the fault signals, ensuring the normal driving of the car.

A robust fault-tolerant control algorithm based on a novel modeling method for quadrotor application with accurate fault estimation

A robust fault-tolerant control algorithm based on a novel modeling method for quadrotor application with accurate fault estimation

Fault-tolerant control for satellite autonomous rendezvous with quality characteristics and actuator uncertainties

An autonomous fault detection and fault-tolerant control solution combining Model Predictive Control (MPC) and Label Distributed Learning (LDL) is developed for the position tracking and attitude synchronization problems in autonomous satellite rendezvous and docking. The proposed algorithm can deal with center of mass (CM) variations and time-varying mass characteristics due to satellite fuel consumption, actuator saturation and fault, and external disturbances. Firstly, a six-degree-of-freedom (6-DOF) satellite translation-rotation coupling model was established to express the relative motion between the satellite and the target, taking the above factors into account. Subsequently, an on-board autonomous closed-loop fault-tolerant control algorithm using MPC is proposed, in which an LDL-based fault detection mechanism is embedded. The scheme specifies that propulsion faults are estimated by calculating the difference between the MPC's prediction of the satellite's future state and the measured change in the actual state. And the mapping matrix between the commanded force/torque and the actual force of the propulsion system is updated in real time to achieve autonomous fault detection and compensation on board the satellite. Among them, the LDL-based fault detection scheme is trained offline. It requires only simple matrix operations for on-orbit use, which is low-cost and suitable for resource-limited space environments. The additional creation of a time-varying disturbance observer allows the estimation and compensation of unmodelled errors and external disturbances. The stability of the proposed fault-tolerant control algorithm is rigorously demonstrated using Lyapunov analysis. Finally, numerical simulations show that the proposed method can successfully achieve autonomous satellite docking under different combinations of thruster faults.

A unified SVPWM fault tolerant control algorithm for single leg fault reconstruction topology of two-level inverter

To improve the reliability of Two-level three phase voltage source inverters, a uniform fault tolerant strategy based on space vector pulse width modulation is proposed for different leg faults. The reconstructed topologies of inverters with different bridge arm faults are different, which makes the basic voltage vector phase of each reconstructed topology inconsistent, resulting in different calculations. Therefore, the coordinate transformation is applied to place the basic voltage vectors of each reconstructed topology on the synchronous stationary αβ coordinate system so that the calculations of the reconstructed topology under different bridge arm faults are unified, thus reducing the complexity of fault-tolerant control. Aiming at the three-phase current asymmetry caused by the neutral point voltage oscillation in inverter topology reconstruction, a transient compensation method of neutral point voltage offset for the α-axis component of the reference voltage vector is introduced to suppress the adverse effects. The compensation method directly offers a neutral point voltage offset value after Clarke transformation and corrects the α-axis component of the reference voltage vector, avoiding the integral calculation in the conventional voltage compensation algorithm. The correctness and effectiveness of the proposed fault-tolerant control strategy are verified experimentally.

Progressive Optimal Fault-Tolerant Control Combining Active and Passive Control Manners

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified, and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. Simulation results verified the feasibility of the proposed algorithm and the validity of the proposed theory.

A novel algorithm for open switch fault detection and fault tolerant control of interleaved DC‐DC boost converters

AbstractThis paper presents a novel algorithm for fault diagnosis and fault tolerant control of interleaved boost converters (IBCs) in the presence of single or double simultaneous open‐circuit faults (OCFs) in power switches. An innovative diagnosis signal (recorded by a cheap current sensor) will be introduced whose waveform mainly depends on the healthy or faulty condition of the converter. Since the diagnosis signal is periodic in the steady‐state operation of the converters, its Fourier Series coefficients, together with the duty cycle of the converter are used as distinguishing features for fault diagnosis of the OCFs in the converter. The well‐known Kalman filter is utilized for robust estimation of the Fourier‐based features. Finally, the modulation of the remaining healthy phases is rearranged in a way that the pre‐fault performance recovers. The proposed algorithm is verified in a laboratory four‐phase IBC setup in which the experimental results show its satisfactory performance. Also, the structure of the proposed method is straightforward and can be implemented in the same microcontroller which is used for voltage and current regulation of the converter.

Adaptive fuzzy finite-time output feedback fault-tolerant control for MIMO stochastic nonlinear systems with non-affine nonlinear faults

This paper investigates adaptive fuzzy finite-time output feedback fault-tolerant tracking control problem for multiple-input multiple-output (MIMO) stochastic non-strict feedback nonlinear systems with non-affine nonlinear faults and states unmeasured. Firstly, high-gain fuzzy state observers are constructed to overcome the state unmeasured problem in the system. Secondly, the mean-value theorem and input compensation techniques are employed to decouple non-affine nonlinear faults, and the resulting unknown control coefficients problem is handled using Nussbaum functions. In addition, dynamic surface control techniques and error compensation mechanisms are considered in the controller design, which effectively decreases the computational complexity of the control scheme. By combining the adaptive backstepping technique with finite-time control, a novel adaptive fuzzy dynamic surface finite-time output feedback fault-tolerant tracking control algorithm is proposed. The designed controller guarantees that all signals in the stochastic systems are semi-global finite-time stable in probability (SGFSP) as well as the tracking errors can converge to a small neighborhood within the origin in finite-time. Finally, two simulation examples are introduced to verify the effectiveness of the suggested control strategy.

A Novel Failure-Distribution-Dependent Non-Fragile $H_\infty$ Fault-Tolerant Load Frequency Control for Faulty Multi-Area Power Systems

This paper studies the non-fragile <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> fault-tolerant load frequency control problem for multi-area power systems with actuator failures. At first, considering the distribution characteristics of partial failures, a stochastic distribution model is established for actuator partial failures. Next, for the perturbations of controller gains, not only the additive uncertainties but also the multiplicative uncertainties are incorporated into the fault-tolerant controller design. The coupling items which hardly directly solved have turned into a W-problem in fault-tolerant load frequency control. Furthermore, a novel failure-distribution-dependent non-fragile <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> fault-tolerant load frequency control algorithm is given by using linear matrix inequalities technology and Lyapunov-Krasovskii functional method. Finally, a numerical example with a three-area power system are given to show the effectiveness of the proposed methods.

Adaptive optimal fault-tolerant vibration control and sensitivity analysis of semi-active suspension based on a self-powered magneto-rheological damper

To reuse the energy dissipated by vehicle suspension, a semi-active suspension with a self-powered magneto-rheological damper is proposed. An electromechanical coupling model of self-powered semi-active suspension is established. The energy conversion efficiency is defined and investigated by changing the electrical parameters. By considering unmodeled dynamics and perturbation values, an adaptive optimal fault-tolerant control algorithm is proposed to ensure the vibration-isolation performance. The robust index of the adaptive optimal fault-tolerant control algorithm is constructed using the Lyapunov equation and evaluated by changing the key parameters. The sensitivity of the key parameters to the damping force is investigated using a grey relation analysis approach. Furthermore, multi-objective optimization between the vibration-isolation capability and energy harvesting is conducted. Via analysis, the proposed suspension can harvest more energy near the second resonance range. Compared to passive control and self-powered mode, the adaptive optimal control algorithm mitigates vibration more significantly in the time and frequency domains, respectively, under stochastic excitation. The robust index is most sensitive to inductance and the diameter of the magnetism cylinder. The length of the damping channel and the diameter of the magnetism cylinder influence the sensitivity of key parameters to the damping force most obviously.

Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying Iteration Lengths.

This article presents an adaptive iterative learning fault-tolerant control algorithm for state constrained nonlinear systems with randomly varying iteration lengths subjected to actuator faults. First, the modified parameters updating laws are designed through a new defined tracking error to handle the randomly varying iteration lengths. Second, the radial basis function neural network method is used to deal with the time-iteration-dependent unknown nonlinearity, and a barrier Lyapunov function is given to cope with the state constraint. Finally, a new barrier composite energy function is used to achieve the tracking error convergence of the presented control algorithm along the iteration axis with the state constraint and then followed with the extension to the high-order case. A simulation for a single-link manipulator is given to illustrate the effectiveness of the theoretical studies.

Prescribed-time fault-tolerant control for the formation of quadrotors based on fully-actuated system approaches

This paper investigates the problem of prescribed-time fault-tolerant control for the formation of quadrotors by utilising the fully-actuated system approach. Since not all quadrotors can directly access the virtual leader's state, a distributed prescribed-time observer is designed to estimate the state of a virtual leader. Leveraging this observer, a horizontal position controller that combines the fully-actuated system approach with the prescribed-time method, as well as altitude and attitude fault-tolerant controller, are proposed. In the design of control law, two distinct sets of controllers are designed: asymptotically stable controller and prescribed-time controller, each tailored to specific control objectives. The proposed distributed prescribed-time observer can ensure that the estimation error of the leader's state reaches zero within any specified time interval. Finally, the numerical simulation is conducted to demonstrate the effectiveness of the proposed fault-tolerant formation control algorithm.

Adaptive neural network control of non‐affine multi‐agent systems with actuator fault and input saturation

AbstractAn adaptive neural network fault‐tolerant control method is proposed for the non‐affine multi‐agent systems with actuator failure and input saturation. Through the transformation of the original multi‐agent systems, an equivalent model is proposed. Considering that the signal input of multi‐agent systems is limited, this article will solve the input saturation problem. But in a real system, the actuator would probably fail. Therefore, a fault tolerant control algorithm is proposed to solve the fault problem in the systems. In this article, an adaptive distributed controller is designed to eliminate the uncertainty of the systems. At the same time, the integral barrier Lyapunov function is selected to verify the stability. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.

Prescribed Time Fault-Tolerant Affine Formation Control for Multi-Agent Systems with Double-Integrator Dynamics

There is an increasing interest in the affine formation control of multi-agent systems, because it can change the centroid, orientation and scale of the formation by controlling only a few leaders. In this paper, the fault-tolerant affine formation control problem is addressed for double-integrator multi-agent systems with partial loss of efficiency and bias faults. Firstly, in order to track the leaders with dynamically changing accelerations, an acceleration observer with prescribed time convergence is proposed, which can estimate the ideal acceleration for each follower. Then, based on the acceleration observer, a fault-tolerant control algorithm is given. A new Lyapunov function candidate is constructed, based on which a sufficient condition to achieve the control objective is derived. Theoretical analysis shows that the formation tracking error can converge to zero within a prescribed time, and remain in a small neighborhood of zero after that time. Finally, numerical simulations are given to show the effectiveness of the proposed algorithm and compare it with existing results.

Fault tolerant vibration control of constrained flexible beam systems with uncertain parameters and actuator failures

This work presents an adaptive fault-tolerant boundary control algorithm for a flexible beam system with actuator failures, external disturbances, and uncertain system parameters. Firstly, partial differential equations with boundary conditions are employed to model the system, and an adaptive compensation method is introduced to solve the issue that the overall number of faults can be infinite, encompassing complete and partial faults. By constructing the barrier Lyapunov function, a fault-tolerant boundary controller is designed to dampen beam vibrations and ensure that all states of flexible beam systems remain within the constraint domain. Simultaneously, the uniformly bounded stability of the flexible beam system is proved by Lyapunov's direct approach. Lastly, the validity of the presented algorithm is demonstrated through simulation results.

Path following fault-tolerant control of distributed drive autonomous unmanned vehicle via adaptive terminal sliding mode

The distributed drive autonomous unmanned vehicle (DDAUV) with unknown fault input of steering system will seriously deviate from the desired path and affect driving safety. This paper proposes a fault estimation and fault-tolerant control (FTC) algorithm, which can improve the comprehensive performance of lateral stability and path following through the torque redundancy characteristics of steering motor and in-wheel motors (IWMs). Firstly, Bayesian algorithm is applied for fault estimation with the prior estimation based on model and posterior observation by sensors. Once steering system fault occurs, the path following model will be reconstructed, and the linear quadratic regulator (LQR) algorithm can dynamically adjust desired front wheel steering angle for path following compensation. Besides, the torque vectoring control of IWMs is applied to further compensate considering the saturation of steering motor. Adaptive terminal sliding mode (ATSM) algorithm is employed to calculate desired additional yaw-moment where the weight matrix of ATSM is adjusted dynamically based on cooperative game theory. Furthermore, an oriented torque distribution method is designed to generate required additional yaw-moment. Finally, the CarSim-MATLAB/Simulink co-simulation and HIL experiment are carried out to test the effectiveness. The results indicate that the proposed FTC algorithm can ensure the lateral stability and improve the path following accuracy with steering system fault.

Pseudo-partial-derivative information-driven adaptive fault-tolerant tracking control for discrete-time systems

The fault-tolerant tracking control problem is studied for the discrete-time systems with actuator faults. To lessen adverse impacts of actuator fault, a PPD information-driven fault estimation algorithm is established to adaptively estimate actuator fault information online, which avoids the additional construction and training process of neural network. With the aid of the adaptive fault compensation, a model-free adaptive fault-tolerant tracking control algorithm is constructed to ensure that the expected output reference trajectory can be tracked by system output. Moreover, only input and output data are employed throughout the design process, system dynamics are not demanded. Ultimately, the availability of developed strategy is proved through a simulation.

Adaptive fault-tolerant control for non-minimum phase hypersonic vehicles based on adaptive dynamic programming

In this paper, a novel adaptive Fault-Tolerant Control (FTC) strategy is proposed for non-minimum phase Hypersonic Vehicles (HSVs) that are affected by actuator faults and parameter uncertainties. The strategy is based on the output redefinition method and Adaptive Dynamic Programming (ADP). The intelligent FTC scheme consists of two main parts: a basic fault-tolerant and stable controller and an ADP-based supplementary controller. In the basic FTC part, an output redefinition approach is designed to make zero-dynamics stable with respect to the new output. Then, Ideal Internal Dynamic (IID) is obtained using an optimal bounded inversion approach, and a tracking controller is designed for the new output to realize output tracking of the non-minimum phase HSV system. For the ADP-based compensation control part, an Action-Dependent Heuristic Dynamic Programming (ADHDP) adopting an actor-critic learning structure is utilized to further optimize the tracking performance of the HSV control system. Finally, simulation results are provided to verify the effectiveness and efficiency of the proposed FTC algorithm.

Distributed Adaptive Fixed-Time Fault-Tolerant Formation Control for Heterogeneous Multiagent Systems With a Leader of Unknown Input.

In this article, the distributed adaptive fixed-time output time-varying formation tracking issue of heterogeneous multiagent systems (MASs) with actuator faults is addressed, in which the followers suffer from loss-of-effectiveness actuator faults, and the leader has unknown bounded input. To solve the above issue, a distributed fixed-time observer is constructed with the leader's unknown input, by which each follower can obtain the leader's states in a predesigned time. Then, based on the observer and the desired formation vector, a local adaptive fixed-time fault-tolerant formation control algorithm is proposed for each follower with the help of time-varying gains to make up for the influence of actuator faults. Furthermore, it is proven that the designed controller can satisfactorily accomplish the considered task of the heterogeneous MASs by using the Lyapunov stability theory. Specifically, the obtained upper bound of the convergence time only depends on a few controller parameters. Finally, a simulation example is implemented to validate the efficiency of the analytical results.

A Spatial Learning-Based Fault Tolerant Lateral Tracking Control for Autonomous Driving

In this paper, a spatial learning-based fault-tolerant control strategy is proposed for precise lateral tracking of autonomous vehicles subject to dynamical uncertainties, external disturbances as well as actuator failures. In order to facilitate the controller design, the uncertain vehicle dynamics are firstly transformed into a parametric form in the space domain, where the system uncertainties are reorganized and combined into the parametric and input distribution uncertainties. Furthermore, considering the under-actuated property of the vehicle dynamics, a novel technique in dealing with the non-square input distribution matrix is employed, in which a pseudo-like inverse matrix and a robust term are introduced into the controller to compensate the mismatch between the number of inputs and outputs. Then the proposed spatial learning-based fault tolerant control algorithm is developed, which is equipped with two adaptive parametric updating laws to estimate the parametric uncertainties and the multiplicative actuator faults correspondingly. Consequently, the convergence of the control algorithm is analyzed rigorously under the framework of composite energy function. Case studies verify the feasibility and effectiveness of the proposed control scheme.