Tolerant Control Method Research Articles

Finite-Time Extended State Observe Based Fault Tolerant Control for Autonomous Underwater Vehicle with Unknown Thruster Fault

This paper investigates the problem of fault tolerant control (FTC) for autonomous underwater vehicles (AUVs) with multiple thrusters in the presence of current disturbances, thruster faults, and modelling uncertainty. This paper focuses on the problems of reducing the energy consumption caused by the chattering of control signals and improving the tracking accuracy of an AUV operating in deep-sea environments. In view of the problem of large energy consumption in some other methods, a fault tolerant control method for multiple-thruster AUVs based on a finite-time extended state observer (FTESO) is proposed. More specifically, a FTESO based on an integral sliding mode surface is designed to estimate the generalized uncertainty compounded using current disturbances, thruster faults, and modelling uncertainty. The fast finite-time uniformly ultimately bounded stability of the proposed FTESO is analyzed. Then, based on the estimated value of FTESO, an FTC method based on non-singular fast terminal sliding mode surfaces is developed for AUVs. The finite-time convergence of the closed-loop control system is proved theoretically. In this design, two different sliding mode surfaces are used to design FTESO and FTC, in order to avoid the appearance of singularities. Moreover, a parameter adjustment method is designed to improve tracking accuracy. Finally, comparative numerical simulations show that the proposed control scheme is effective at reducing energy consumption and improving tracking accuracy.

Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks

This paper proposed neural networks to continuously provide alternative constructed signals for vehicle and wheel speed sensors utilized for the Anti-Lock Braking System (ABS), which serves as the fault tolerant control method. These alternative constructed signals are used for two purposes. The first is to generate residual signals, and the second is to be adopted instead of isolated faulty signals. The residual signal is generated by extracting the difference between the alternative constructed signals and the corresponding actual signals. These residual signals serve as an indication of fault occurrence and to express that fault severity. Whenever a fault occurrence is detected and diagnosed in one of the sensor’s signals, the faulty signal is isolated and replaced by the corresponding constructed signal to maintain the system's normal behavior under a faulty condition. The range of data covered under the proposed estimating neural networks is huge, continuous in time, and not sampled. In this work, the range of the data lies between [50 to 120 km/h] when the braking is started. That cannot be performed by any available method. These models' training process is based on the Levenberg-Marquardt (LM) algorithm, implemented and tested by MATLAB/Simulink. The results show that these models can accurately map the measured data into the desired output through the best-fit functions. The fast response of the trained models makes them suitable for real-time alternative signals for fault-tolerant purposes for speed sensors during hard or panic braking.

Impact angle constrained fuzzy adaptive fault tolerant IGC method for Ski-to-Turn missiles with unsteady aerodynamics and multiple disturbances

An impact angle constrained fuzzy adaptive fault tolerant integrated guidance and control method for Ski-to-Turn (STT) missiles subject to unsteady aerodynamics and multiple disturbances is proposed. Unsteady aerodynamics appears when flight vehicles are in a transonic state or confronted with unstable airflow. Meanwhile, actuator failures and multisource model uncertainties are introduced. However, the boundaries of these multisource uncertainties are assumed unknown. The target is assumed to execute high maneuver movement which is unknown to the missile. Furthermore, impact angle constraint puts forward higher requirements for the interception accuracy of the integrated guidance and control (IGC) method. The impact angle constraint and the precise interception are established as the object of the IGC method. Then, the boundaries of the lumped disturbances are estimated, and several fuzzy logic systems are introduced to compensate the unknown nonlinearities and uncertainties. Next, a series of adaptive laws are developed so that the undesirable effects arising from unsteady aerodynamics, actuator failures and unknown uncertainties could be suppressed. Consequently, an impact angle constrained fuzzy adaptive fault tolerant IGC method with three loops is constructed and a perfect hit-to-kill interception with specified impact angle can be implemented. Eventually, the numerical simulations are conducted to verify the effectiveness and superiority of the proposed method.

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.

Dynamic and sensor fault tolerant control for an intensified heat-exchanger/reactor

In this paper, an active fault tolerant control system is proposed and applied to a new intensified heat exchanger/reactor system. This method consists of an adaptive observer-based fault detection, isolation, and identification scheme and a control law redesign method based on the backstepping approach. The objective of the application of the fault tolerant control system is to ensure the safety and productivity of this intensified heat exchanger/reactor even in the presence of a fault. Both parameter and sensor faults are considered. The effectiveness of the fault tolerant control method based on adaptive observers is validated by simulations on the heat exchanger/reactor system.

Synergetic learning structure-based neuro-optimal fault tolerant control for unknown nonlinear systems

In this paper, a synergetic learning structure-based neuro-optimal fault tolerant control (SLSNOFTC) method is proposed for unknown nonlinear continuous-time systems with actuator failures. Under the framework of the synergetic learning structure (SLS), the optimal control input and the actuator failure are viewed as two subsystems. Then, the fault tolerant control (FTC) problem can be regarded as a two-player zero-sum differential game according to the game theory. A radial basis function neural network-based identifier, which uses the measured input/output data, is constructed to identify the completely unknown system dynamics. To develop the SLSNOFTC method, the Hamilton–Jacobi–Isaacs equation is solved by an asymptotically stable critic neural network (ASCNN) which is composed of cooperative adaptive tuning laws. Besides, with the help of the Lyapunov stability analysis, the identification error, the weight error of ASCNN, and all signals of closed-loop system are guaranteed to be converged to zero asymptotically, rather than uniformly ultimately bounded. Numerical simulation examples further verify the effectiveness and reliability of the proposed method.

Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function

Abstract Research on the geometric tolerance control method of precision machinery based on image modeling and new saturation function. First, image-based modeling techniques provide a new way to control the saturation function with a dynamic boundary layer from a computer graphics perspective. Then, it proposes a method to represent the dimensional and geometric tolerances of precision machines using changes in the position of control points of geometric elements. Finally, the main content of the tolerance analysis method based on Monte Carlo simulation is proposed, that is, the position of the azimuth control point of all geometric elements on the measurement chain is determined experimentally by using a random number generator. Experiments have shown that the nominal distance between the control points is the nominal size of the geometric elements, and by comparing the variation of the control points with the nominal distance of the control points, it is easy to determine the relationship between tolerance and tolerance. accuracy.

A switching-free multiple-model approach for adaptive FTC of non-Lipschitz nonlinear uncertain systems under persistent actuator failures

A novel actuator failure compensation scheme is proposed for affine nonlinear uncertain systems (not necessarily Lipschitz) subject to persistent/intermittent actuator faults/failures unknown in time, magnitude and pattern. The proposed control methodology satisfies the nonlinear separation principle through a modular backstepping control. The controller is then augmented with multiple estimation models to estimate failure induced parametric uncertainties and unknown system parameters. The output transient performance at start up and post-failure instances, is improved on account of a two layer adaptation which enhances the convergence speed and accuracy of parameter estimates. The proposed fault tolerant control (FTC) method yields a faithful accommodation of uncertain finite as well as infinite/intermittent/persistent actuator failures while ensuring satisfactory output transient and steady state performances. Further, compared to existing multiple model based adaptive fault tolerant control design for nonlinear systems, the proposed methodology circumvents the issues of stability due to switching between different models and utilizes a minimum number of estimation models for parameter estimation without compromising on the output performance. Consequently, the computational burden is also reduced. Compared to multiple model adaptive control based FTC strategies proposed earlier which assume finite actuator failures and Lipschitz nonlinear system, the proposed method is applicable to both Lipschitz and non-Lipschitz nonlinear systems affected by intermittent actuator failures. Using the concepts from stability analysis in random nonlinear impulsive systems, the L∞ and L2 bounds on tracking error for all future time are derived in the case of intermittent/persistent actuator failures obtained using the proposed fault-tolerant controller. The improvement of output transient performance in the proposed control scheme in comparison with controller with single identifier, is theoretically proved and quantified.

A Dynamic-State-Estimator-Based Tolerance Control Method Against Cyberattack and Erroneous Measured Data for Power Systems

Cyberattacks against measured variables and faulty metering devices are among the most important threats to modern systems that should be detected and isolated, and the control actions based on the measured variables should be appropriate in order to keep the power system security. This article proposes a dynamic-state-estimation-based cyberattack-tolerant control method for modern power systems. The proposed method involves two new schemes: one is for dynamically detecting the cyberattack and the other isolates the location of the attack. These schemes are based on dynamic observer designs that can eliminate the effects of unknown inputs. This article also proposes a fault-tolerant control technique using these observer-based detection and isolation schemes. The proposed method can accurately track dynamic states, detecting both cyberattack against measured variables and faulty measuring devices, and isolating the cyberattack and fault locations. The results verify its superiority in comparison with other techniques.

Model-based detection and isolation of the wheel slippage and actuator faults of a holonomic mobile robot

PurposeMobile robots may perform very critical tasks under difficult operating conditions. Faults encountered during their tasks may cause the task to be interrupted or failed completely. In the active fault tolerant control methods, it is very important not only to detect the faults that occur in the robot, but also to isolate these faults to develop a fault recovery strategy that is suitable for that specific type of fault. This study aims to develop a model-based fault detection and isolation method for wheel slippage and motor performance degradation that may occur in wheeled mobile robots.Design/methodology/approachIn the proposed method, wheel speeds can be estimated via the dynamic model of the mobile robot, which includes a friction model between the wheel and the ground. Four residual signals are obtained from the differences between the estimated states and the measured states of the mobile robot. Mobile robot’s faults are detected by using these signals. Also, two different residual signals are generated from the calculation of the traction forces with two different procedures. These six residual signals are then used to isolate possible wheel slippage and performance degradation in a motor.FindingsThe proposed method for diagnosing wheel slip and performance degradation in motors are tested by moving the robot in various directions. According to the data obtained from the test results, a logic table is created to isolate these two faults from each other. Thanks to the created logic table, slippage in any wheel and performance degradation in any motor can be detected and isolated.Originality/valueTwo different recovery strategies are needed to recover temporary wheel slippage and permanent motor faults. Therefore, it is important to isolate these two faults that create similar symptoms in robot’s general movement. Thanks to the method proposed in this study, it is not only possible to isolate the slipping wheel with respect to the non-slipping wheels or to isolate the faulty motor from the non-faulty ones, but also to isolate these two different fault types from each other.

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.

Active Fault-Tolerant Control for Near-Space Hypersonic Vehicles

Due to the harsh working environment, Near-Space Hypersonic Vehicles (NSHVs) have the characteristics of frequent faults, which seriously affect flight safety. However, most researches focus on active fault-tolerant control for actuator faults. In order to fill the gap of active fault-tolerant control for sensor faults, this paper presents an Active Fault-Tolerant Control (AFTC) strategy for NSHVs based on Active Disturbance Rejection Control (ADRC) combined with fault diagnosis and evaluation. With the proposed AFTC strategy, both sensor faults and actuator faults can be compensated within 0.5 s. Wavelet packet decomposition and Kernel Extreme Learning Machine (KELM) are associated to ensure the high accuracy and real-time ability of fault diagnosis. Simulation results show that the proposed fault diagnosis method can significantly reduce the divergence of diagnosis results by up to 98%. The fault information is used to generate tolerant compensation, which is combined with the ADRC to achieve AFTC. Statistical results indicate that AFTC has significantly lower static error than ADRC. The proposed AFTC method endows NSHVs with the ability to complete missions even when various types of faults appear. Its advantages are demonstrated in comparison with other fault diagnosis and tolerant control methods.

Fault tolerant motion planning and control of space manipulators with free-swinging joint failure based on controllable degree

Space manipulators are usually used to assist or replace the astronaut to execute on-orbit tasks. However, due to the burdensome tasks and harsh space environment, free-swinging joint failure is prone to happen, causing the task to be unable to continue. Once the joint failure occurs, the base and free-swinging joint belong to underactuated units, so their trajectories have to be controlled by the active joint. To execute on-orbit tasks successfully after the joint failure occurs, a fault tolerant motion planning and control method (FTMPC) of the space manipulator based on controllable degree is proposed. First, based on the dynamics model of space manipulators, the dynamics coupling relationship between the active joint and the underactuated unit is established. Second, according to the inertia matrix, Coriolis and centrifugal terms of the dynamics model, a controllable degree index of the active joint over the underactuated unit is constructed. Third, according to the specific task requirement and controlled object, the FTMPC based on the controllable degree is designed. The FTMPC is applied according to the number of active joints and underactuated units and whether there is a dynamic singularity during the task execution. The simulation is carried out with a 7-DOF space manipulator, verifying the correctness and effectiveness of the FTMPC. The method has two advantages: 1) Coriolis and centrifugal terms are both considered for the first time in evaluating the control ability of active joints, making the evaluation result more in line with the motion characteristics of space manipulators; 2) A FTMPC based on the controllable degree is proposed, completing the task without dynamic singularity or huge torque on the active joint.

Adaptive Observer Based Fault Tolerant Control for Sensor and Actuator Faults in Wind Turbines.

The installed wind energy generation capacity has been increasing dramatically all over the world. However, most wind turbines are installed in hostile environments, where regular operation needs to be ensured by effective fault tolerant control methods. An adaptive observer-based fault tolerant control scheme is proposed in this article to address the sensor and actuator faults that usually occur on the core subsystems of wind turbines. The fast adaptive fault estimation (FAFE) algorithm is adopted in the adaptive observers to accurately and rapidly located the faults. Based on the states and faults estimated by the adaptive observers, the state feedback fault tolerant controllers are designed to stabilize the system and compensate for the faults. The gain matrices of the controllers are calculated by the pole placement method. Simulation results illustrate that the proposed fault tolerant control scheme with the FAFE algorithm stabilizes the faulty system effectively and performs better than the baseline on the benchmark model of wind turbines.

A Novel Active Fault-Tolerant Tracking Control for Robot Manipulators with Finite-Time Stability.

Many terminal sliding mode controllers (TSMCs) have been suggested to obtain exact tracking control of robotic manipulators in finite time. The ordinary method is based on TSMCs that secure trajectory tracking under the assumptions such as the known robot dynamic model and the determined upper boundary of uncertain components. Despite tracking errors that tend to zero in finite time, the weakness of TSMCs is chattering, slow convergence speed, and the need for the exact robot dynamic model. Few studies are handling the weakness of TSMCs by using the combination between TSMCs and finite-time observers. In this paper, we present a novel finite-time fault tolerance control (FTC) method for robotic manipulators. A finite-time fault detection observer (FTFDO) is proposed to estimate all uncertainties, external disturbances, and faults accurately and on time. From the estimated information of FTFDO, a novel finite-time FTC method is developed based on a new finite-time terminal sliding surface and a new finite-time reaching control law. Thanks to this approach, the proposed FTC method provides a fast convergence speed for both observation error and control error in finite time. The operation of the robot system is guaranteed with expected performance even in case of faults, including high tracking accuracy, small chattering behavior in control input signals, and fast transient response with the variation of disturbances, uncertainties, or faults. The stability and finite-time convergence of the proposed control system are verified that they are strictly guaranteed by Lyapunov theory and finite-time control theory. The simulation performance for a FARA robotic manipulator proves the proposed control theory’s correctness and effectiveness.

Diagnosis and Tolerant Control Methods for an Open-Switch Fault in a Vienna Rectifier

In this article, fault diagnosis and tolerant control methods are proposed for an open-switch fault in a Vienna rectifier. The proposed diagnosis method uses only the input currents to detect an open-switch fault without any additional hardware. In the proposed tolerant control, the offset voltage is used to restore the input currents when an open-switch fault occurs in a Vienna rectifier. In addition, the proposed tolerant control considers the balance of the neutral-point voltage. This method can improve the total harmonic distortion (THD) of the current and mitigate the dc-link voltage ripple. The effectiveness of the proposed open-switch fault diagnosis and tolerant control method is verified by simulated and experimental results.

Trajectory tracking of tail-sitter aircraft by ℓ1 adaptive fault tolerant control

This paper proposes an &#x2113;<inf>1</inf> adaptive fault tolerant control method for trajectory tracking of tail-sitter aircraft in the state of motor loss fault. The tail-sitter model considers the uncertainties produced by the features of nonlinearities and couplings which cause difficulties in control. An &#x2113;<inf>1</inf> adaptive controller is designed to reduce the position and attitude error when actuators have faults. A reference trajectory containing large maneuver flight transitions is designed, which makes it even harder for the &#x2113;<inf>1</inf> controller to track accurately. Compensators are designed to assist &#x2113;<inf>1</inf> adaptive controller tracking of the reference trajectory. The stability of the &#x2113;<inf>1</inf> adaptive controller including compensators is proved. Finally, the simulation results are used to analyse the effectiveness of the proposed controller. Compared to the H<inf>&#x221E;</inf> controller, the &#x2113;<inf>1</inf> adaptive controller with compensators has better performance in position control and attitude control under fault tolerance state even when the aircraft conducts large maneuver. Besides, as the &#x2113;<inf>1</inf> adaptive control method separates feedback control and adaptive law design, the response speed of the whole system is improved.

Constrained Active Fault Tolerant Control Based on Active Fault Diagnosis and Interpolation Optimization.

A new active fault tolerant control scheme based on active fault diagnosis is proposed to address the component/actuator faults for systems with state and input constraints. Firstly, the active fault diagnosis is composed of diagnostic observers, constant auxiliary signals, and separation hyperplanes, all of which are designed offline. In online applications, only a single diagnostic observer is activated to achieve fault detection and isolation. Compared with the traditional multi-observer parallel diagnosis methods, such a design is beneficial to improve the diagnostic efficiency. Secondly, the active fault tolerant control is composed of outer fault tolerant control, inner fault tolerant control and a linear-programming-based interpolation control algorithm. The inner fault tolerant control is determined offline and satisfies the prescribed optimal control performance requirement. The outer fault tolerant control is used to enlarge the feasible region, and it needs to be determined online together with the interpolation optimization. In online applications, the updated state estimates trigger the adjustment of the interpolation algorithm, which in turn enables control reconfiguration by implicitly optimizing the dynamic convex combination of outer fault tolerant control and inner fault tolerant control. This control scheme contributes to further reducing the computational effort of traditional constrained predictive fault tolerant control methods. In addition, each pair of inner fault tolerant control and diagnostic observer is designed integratedly to suppress the robust interaction influences between estimation error and control error. The soft constraint method is further integrated to handle some cases that lead to constraint violations. The effectiveness of these designs is finally validated by a case study of a wastewater treatment plant model.

Distributed secondary consensus fault tolerant control method for voltage and frequency restoration and power sharing control in multi-agent microgrid

This paper proposes a distributed secondary consensus fault-tolerant control (FTC) method for the multi-agent microgrid (MG). The proposed controller is applied to compensate for the errors in the system frequency and voltage profiles in the presence of MG faults. The proposed controller also enables the system to ensure an accurate sharing of the active and reactive power among the connected distributed generations (DGs) in MG. The consensus-based controller depends on the information transferring between MG neighbor agents through a graph communication network. The proposed secondary controller improves the primary controllers' performance, including droop control and inner voltage and current controllers. The proposed controller has been verified using a hypothetical multi-agent MG system in MATLAB/Simulink environment. A comparative analysis between the results obtained from applying the proposed controller and the primary droop control method (DCM). The proposed controller's performance is evaluated using the controller response time, the maximum frequency deviation, and the steady-state restoration time. The results show the proposed secondary FTC method's effectiveness in mitigating the faults' effect in multi-agent MG.

Decentralized Fault Detection and Fault-Tolerant Control for Nonlinear Interconnected Systems

The nonlinear interconnected system is a complex and important system in daily production and life in general. Due to the interconnection influence between subsystems and external disturbance factors, the system is prone to failure. For this kind of system, a decentralized fault detection and fault tolerant control method is proposed here. Compared with the traditional control scheme, this paper designs a subsystem communication protocol to reduce the information exchange between subsystems. Based on this communication protocol, a fault detection scheme is then designed. Due to the existence of a fault detection threshold in this scheme, the system can detect the fault in time without missing it or having a false alarm. Under the assumed condition, the adaptive control rate is obtained by establishing the adaptive approximation model to approximate the upper bound of the fault, and the subsystem adaptively adjusts the control rate according to the fault condition, so that the system can quickly recover to stability. Finally, a simulation program is used to verify the proposed method.