Data-driven Fault-tolerant Control Research Articles

Fault-Tolerant Control for Aircraft with Structural Damage Using Sparse Online Gaussian Process Regression

AbstractThis paper presents the design of data-driven fault-tolerant control using sparse online Gaussian process regression (SOGPR) to stabilize an aircraft with left-wing damage. The structural damage causes changes in mass, moment of inertia, center of gravity, and aerodynamic coefficients. These parameter variations deteriorate the performance of model-based nonlinear control methods. Hence, Gaussian process-based nonlinear dynamic inversion (GP-NDI) is proposed to compensate for uncertainties in situations of structural damage. Unlike parametric adaptive control approaches, Gaussian process regression is a non-parametric method that does not need prior information about uncertainties. And the proposed method implements SOGPR to reduce computational time and memory by incrementally updating the mean and variance. To compensate for the error in the estimated uncertainty, a robust control input is designed. In addition, a weighted delete score is used to improve the transient response. Numerical simulation results are compared with model reference adaptive control (MRAC) and nonlinear disturbance observer (NDO) to analyze a stabilizing and tracking performance in a structural damage situation.

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments.

A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

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.

Data-driven fault-tolerant control for nonlinear systems with output saturation

This paper is focused on data-driven fault-tolerant control for nonlinear systems with output saturation. A modified observer with measurement output is built to approximate sensor fault. According to this approximation, corresponding data-driven fault-tolerant controller is proposed by utilizing optimization criteria and adaptive dynamic programming method. Consequently, a triggering rule is constructed by employing instantaneous and average measurement output. Resorting to the Lyapunov method, the resulted error system is uniformly ultimately bounded. The effectiveness of the presented approach is demonstrated by an example comparison.

Adaptive data-driven fault-tolerant control for nonlinear systems: Koopman-based virtual actuator approach

This paper proposes an adaptive data-driven fault-tolerant control scheme using the Koopman operator for unknown dynamics subjected to nonlinearities, time-varying loss of effectiveness, and additive actuator faults. The main objective of this method is to design a virtual actuator to hide actuator faults from the view of the system’s nominal controller without having any prior knowledge about the system’s underlying dynamics. The designed virtual actuator is placed between the faulty plant and the nominal controller of the system to keep the dynamical system’s performance consistent before and after the occurrence of actuator faults. Based on the Koopman operator theory, an equivalent Koopman predictor is first obtained using the process data only, without knowing the governing equations of the underlying dynamics. Koopman operator is an infinite-dimensional, linear operator which takes the nonlinear process data into an infinite-dimensional feature space where the dynamic data correlations have linear behavior. Next, based on the approximated system’s Koopman operator, a virtual actuator is designed and implemented without knowing the system’s nominal controller. Needless to use a separate fault detection, isolation, and identification module to perform fault-tolerant control, the current method leverages the adaptive framework to keep the system’s desired performance in facing time-varying additive and loss of effectiveness actuator faults. Finally, the approach’s efficacy is demonstrated using simulation on a two-link manipulator benchmark, and a comparison study is presented.

A Blockchain-Based Data-Driven Fault-Tolerant Control System for Smart Factories in Industry 4.0

Modern technologies and data-driven approaches have enabled fault-tolerant controllers in Industry 4.0 smart factories to detect, identify, and mitigate anomalies in real-time with a high level of accuracy. However, this has also presented new challenges and requirements for cybersecurity, data analytics, and computational complexity for Data-Driven Fault-Tolerant Controllers (DD-FTC) in smart factories. To address these issues, a Blockchain-Based Data-Driven Fault-Tolerant Control (BB-DD-FTC) framework for smart factories is proposed in this paper. Blockchain ensures the integrity of data logs via its immutable ledger and decentralized architecture. Moreover, the blockchain smart contract functionality, embedded with a Data-Driven Intrusion Detection System (DD-IDS), and reconfiguration conditions, realizes DD-FTC and undertakes the mitigation response in case of cyber-attacks. The DD-IDS mechanism utilizes the principal component analysis technique and observer models, trained via neural networks, to detect an attack and identify the compromised component. The Tennessee Eastman (TE) industrial benchmark process is considered a case study to investigate the performance of the proposed framework. Two kinds of integrity attacks are applied to the sensors of the TE process with simulation results demonstrating the effectiveness of the method in mitigating the adversarial effect of the applied attacks on the overall system performance. As feedback delays can negatively impact performance, a detailed delay analysis is performed using network calculus. The security advantages and limitations of the proposed method are finally highlighted in the performed security analysis. The results are encouraging for the wider adoption of the control-over-the-blockchain concept.

Fault-tolerant control of a hydraulic servo actuator via adaptive dynamic programming

<abstract><p>The fault-tolerant control problem of a hydraulic servo actuator in the presence of actuator faults is studied utilizing adaptive dynamic programming. This task is challenging because of unknown system dynamics, uncertain disturbances or unmeasurable system states of such highly nonlinear systems in real applications. The aim is to achieve asymptotic tracking and actuator faults compensation by minimizing some predefined performance index. The discrete-time algebraic Riccati equation is iteratively solved by the adaptive dynamic programming approach. For practical reasons, adaptive dynamic programming techniques and fault compensation are integrated to iteratively compute an approximated optimal fault-tolerant control using real-time input/output data without any a priori knowledge of the system dynamics and unmeasurable states. As a result, a fault-tolerant control of hydraulic servo actuator is then designed based on adaptive dynamic programming via output feedback. Also, the convergence analysis of a data-driven fault-tolerant control is theoretically shown as well. Finally, intensive simulation results are given to prove the validity and merits of the developed data-driven fault-tolerant control strategy.</p></abstract>

Data-driven fault-tolerant control with fuzzy-rules equivalent model for a class of unknown discrete-time MIMO systems and complex coupling

The problem of fault-tolerant control for a class of discrete-time multi-input–multi-output systems is addressed with actuator faults and complex-coupling property of controlled plants. To avoid the non-causality problem, the equivalent model is established by the proposed coupling-matrix with the fractional input–output data and Multi-input fuzzy rules emulated networks (MiFREN) while the diagonally dominant condition is completely omitted and actuator faults are imitated by the coupling-matrix. Then, another MiFREN is utilized as the direct adaptive controller and the learning law is established to ensure closed-loop performance through rigorous mathematical analysis. The effectiveness of the proposed controller is validated by the numerical system and the activated sludge treatment processes (ASTP) over the comparison results in dealing with complex-coupling, unknown actuator faults, noisy measurements and undefined disturbances.

Data-driven fault-tolerant control for unmanned aerial vehicles without using identification model

Unmanned aerial vehicle (UAV)’s fault-control problem was studied in this paper, and data-driven fault-tolerant control scheme was developed for acceleration tracking control of UAV in order to cope with the uncertainties induced by aerodynamic damage. A linear UAV dynamic model was given with reasonable assumptions, and the acceleration tracking control for UAV was converted to solving an infinite-horizon optimal control problem. The augmented algebraic Riccati equation (ARE) is derived, and its solution stability is proved based on Lyapunov theory. The data-driven control algorithm is further derived for online solving of the augmented ARE with only using flight data. The proposed algorithm is based on experience replay of flight data rather than model knowledge, so it greatly reduces the effect of uncertainties induced by aerodynamic damage on the flight control system for UAVs. Finally, the effectiveness of developed algorithm is verified through the numerical simulations under different uncertainties induced by aerodynamic damage.

Adaptive data-driven fault-tolerant control strategy for optimal power extraction in presence of broken rotor bars in wind turbine

Broken rotor bars of a Squirrel Cage Induction Generator (SCIG) impact significantly the quality and quantity of produced electrical energy from Wind Energy Conversion Systems (WECS) because: (1) they change the characteristics of SCIG entailing the invalidity of the designed Maximum Power Point Trucking (MPPT) control and pitch angle control, (2) they increase mechanical and thermal stresses, as well as the harmonic content in stator currents. Therefore, this paper proposes an efficient fault tolerant control strategy in order to mitigate the aforementioned consequences. This strategy is based on a hybrid (mechanic-electric) fault tolerant control (FTC) allowing obtaining optimal power extraction in presence of broken rotor bars. A failure (one or two broken bars) is detected in early stage based on the use of a data-driven (machine learning) approach. The latter uses a discriminative feature space (frequency and magnitude of stator harmonic currents) in order to represent and separate the normal operation mode from faulty modes (broken bars). Simulations using MATLAB demonstrated a nominal current in healthy rotor bars in wind turbine operation zone-3 and new optimal power extraction despite one or two broken bars.

Data-Driven Fault-Tolerant Control for Discrete-Time Systems based on LMI

Actuator failure of control systems is one of the main factors causing instability and performance degradation. Over the past few decades, fault-tolerant control strategies have been introduced to improve the performance of control systems against failure. The current fault-tolerant control approaches are mostly model-based, however, model identification is time-costly and costly. In this paper, a data-driven fault-tolerant control method for discrete-time systems with actuator faults is designed. Using the input and output data collected directly from the system, the data-driven method develops the fault-tolerant control strategy for discrete-time systems with unknown or complex model. Moreover, the stability condition of the strategy is derived based on linear matrix inequality (LMI). Finally, a simulation is presented to demonstrate the effectiveness of the data-driven fault-tolerant control strategy, using an aircraft dynamic system model with actuator failure.

Data-Driven Approach to Accommodating Multiple Simultaneous Sensor Faults in Variable-Gain PID Systems

This paper is concerned with the problem of data-driven fault-tolerant control for multiple simultaneous sensor drift faults in variable-gain digital PID systems with very large time constants and long dead time, which are exceedingly common characteristics of process control systems. No existing data-driven residual generation method can allow building (with low computational costs) residual variables independent of the state of these systems, and meanwhile guarantee that each of the sensor faults is mapped uniquely and entirely onto the associated residual variable. To solve the aforementioned technical difficulty, a novel residual generation technique is devised via the dead time as well as the coefficients and state of the variable-gain PID controller. On this basis, a methodology is developed for the purpose of the full-decoupling estimation of several sensor malfunctions from the residual variables. Finally, a resulting data-driven approach to compensate for the aforesaid faults is applied to a dual-chamber electric heating furnace (which is a typical process plant), so that the effectiveness and advantages of the proposed methods are verified by experiment.

Data-driven compensation method for sensor drift faults in digital PID systems with unknown dynamics

This paper investigates the problem of compensating for the slow-changing sensor drift failures occurring in digital PID systems with unknown dynamics. It is extremely difficult to ensure the tracking performance of the faulty systems, due to the system models unavailable, the measurements corrupted by the faults, and the impact of the sensor failures propagated under feedback control. Therefore, the existing data-driven fault-tolerant control (FTC) methods are not capable of dealing with the above problem. Contrary to the current state-of-the-art, a novel residual generator structure is devised. Furthermore, its data-driven realization is accomplished, along with the convergence analysis of the proposed closed-loop recursive identification algorithm. On this basis, such failures can be estimated continuously from the residual signals, using an iterative estimation procedure, the convergence of whose mean-square estimation error is theoretically proven. As a result, the effect of the aforementioned failures on the tracking properties is eliminated with the aid of a fault detection mechanism and based on the tracking error signal corrected with the estimated faults. Finally, the effectiveness and merits of the resultant data-driven FTC algorithm are validated by the continuous stirred tank heater benchmark process.

A data-driven fault-tolerant control design of linear multivariable systems with performance optimization

In this paper, an integrated data-driven fault-tolerant control (FTC) design scheme is proposed under the configuration of the Youla parameterization for multiple-input multiple-output (MIMO) systems. With unknown system model parameters, the canonical form identification technique is first applied to design the residual observer in fault-free case. In faulty case, with online tuning of the Youla parameters based on the system data via the gradient-based algorithm, the fault influence is attenuated with system performance optimization. In addition, to improve the robustness of the residual generator to a class of system deviations, a novel adaptive scheme is proposed for the residual generator to prevent its over-activation. Simulation results of a two-tank flow system demonstrate the optimized performance and effect of the proposed FTC scheme.

13th European Workshop on Advanced Control and Diagnosis (ACD 2016)

13th European Workshop on Advanced Control and DiagnosisThe 13th European Workshop on Advanced Control and Diagnosis (ACD 2016), www.acd2016.eu took place at Hautes Etrudes d’Ingénieur (HEI), Lille, France, on November 17-18, 2016.The annual European Workshop on Advanced Control and Diagnosis has been organized since 2003 by Control Engineering departments of several European universities in Germany, France, the UK, Poland, Italy, Hungary, Denmark and Czech Republic, to bring together senior and junior academics and engineers from diverse fields of automatic control, fault detection, and signal processing. The workshop provides an opportunity for researchers and developers to present their recent theoretical developments, practical applications, or even open problems. It also offers a great opportunity for industrial partners to express their needs and priorities and to review the current activities in the fields.A total of 100 papers have been submitted for ACD 2016. Based on the peer reviews 71 papers were accepted for oral presentation, 4 papers withdrawn, 3 papers not presented. The accepted papers covered areas of Fault Diagnosis, Estimation and Observation, Fault Tolerant Control, Predictive Control, Robust Control, Control Theory, fractional order systems, Modeling and Data °Processing, Software tools and code generation. In addition, four excellent plenary lectures were delivered by experts on their domain: Ir. Serge Legonidec from the company Airbus Safran Launchers, France : An overview of connections between scientific automatic topics and their applications in the propulsive systems, Dr. Ir. Jan-Willem Van Wingerden, Technical University of Delft, Nederland : Wind Energy Control Research, Prof. Sarah Spurgeon, University College London, UK: On discontinuous Observers: From Basic Properties to a Robust Fault Detection and Condition Monitoring Tool, Dr. Joseph-Julien Yame, University of Lorraine, Nancy, France : Data Driven Fault Tolerant Control: a behavioral approach.The ACD 2016 has been co-sponsored technically by the International Federation of Automatic Control (IFAC).On behalf of the ACD 2016 organizing committee, we would like to thank all those who prepared and submitted papers, participated in the peer review process, supported, and attended the workshop.Abdel AITOUCHE - General ChairJosef KORBICZ - IPC ChairVincent COCQUEMPOT - IPC Co-Chair

Role of Performance Evaluator in data-driven Fault Tolerant Control

In this note, we discuss about the performance evaluator tool that plays a significant role in the data-driven fault tolerant control (FTC) system. In our notion of data-driven approach, we do not have any access to the a priori plant model in real-time. Moreover, we do not equip any estimation algorithm to determine the model of the plant. Here, we use the trajectories generated by the system in real time. These trajectories, in fact, capture the behavior of the system and we directly evaluate the control performance of the closed-loop based on these trajectories. Whenever a fault occurs, this tool assists the supervisor to take necessary actions for the controller reconfiguration mechanism. Since no a priori knowledge about the plant is used online, this tool is shown here performing all the necessary roles, particularly fault detection, fault accommodation, and especially, stability assessment.