Fault-Tolerant Control: History
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor: , , ,

Fault-tolerant controllers (FTCs) can be defined as controllers that are able to tolerate faults and keep the control performance in the ideal range in the presence of faults. FTC approaches can be categorized into two main categories: Passive FTCs and Active FTC.

Passive FTCs are not dependent on fault detection and isolation (FDI) modules and can keep the desired controller performance without considering the type and size of fault.

Active FTCs are equipped with the FDI module and behave differently to different kinds of fault.

  • Active fault tolerant control
  • Abrupt faults
  • fault detection and isolation
  • Resilient Controllers
  • false data
  • fault causes

1. Introduction

In the classic control theory, it is assumed that all the components work properly and precisely. However, experience has taught us that this assumption cannot be guaranteed all the time, and on many occasions, system components might face some faults or failures in their task. These accumulative faults would endanger the controller stability and its performance that cannot be tackled by robust control theories. With the increasing demand for having a reliable and safe controller, the fault-tolerant control (FTC) systems became one of the most attractive topics in the field of advanced control theory, which received a great deal of attention among researchers. The ongoing achievements in this field of control lead to several valuable review articles to overview the most recent techniques.

One of the earliest review papers in the field of FTC was published in 1991 by Stengel [1], which investigated the basic concepts of FTC and artificial intelligence application in FTC systems. In 1997, Patton presented a comprehensive review of FTC techniques and analyzed the key issues of FTC design [2]. Luze and Richter presented an introductory tutorial for FTC design based on reconfiguration and reviewed the state-of-the-art achievement in the field [3]. Alwi et al. reviewed different kinds of possible faults and failures in the control system and briefly overviewed fault detection and isolation (FDI) and FTC approaches [4]. The survey papers in [5][6][7][8] have reviewed the development of active and passive FTC systems and investigated the challenges and advantages of them. In [9], FDI and FTC approaches in the aerospace system have been briefly reviewed, and the combination of active and passive FTC was investigated. Some survey papers reviewed designing of FTC for a specific application, for instance, FDI and FTC approaches for attitude control in spacecraft [10], single-rotor aircraft (e.g., helicopters) [11], electric speed drive systems [12][13], photovoltaic (PV) systems [14], and power electronics systems [15][16]. In [17][18], FDI approaches have been extensively investigated, and they classified FDI techniques to four subcategories: model-based, signal-based, knowledge-based, and hybrid/active approaches.

Despite the valuable efforts in recent decades to provide a comprehensive FTC and FDI approaches, most of the works were only reviewed hardware-redundancy-based FTC approaches. At the same time, analytical redundancy, which has received a great deal of attention in recent years, has not been investigated to the best of our knowledge. In addition, most of the works reviewed FDI and FTC separately, and the link between active FTC and FDI to obtain a united active FTC system was not technically investigated. Furthermore, the ongoing achievements in this field of control and the increasing need to develop a reliable control system are another reason to review the latest works in the field. These reasons motivated us to prepare the current work.

2. Passive and Active FTC

FTC techniques can be divided into two main categories: active and passive [5][19]. Active FTC uses detection techniques to find the fault, then, a supervisory system will decide how to modify the control structure and parameters to compensate for the effect of the faults in the system [7]. However, in passive FTC, a robust compensator is used to reduce the fault effects in the system or at least stabilize the system in the presence of a fault in the system.

2.1. Passive FTC

Passive FTC systems do not rely on the fault information, and their design is directly integrated with the concept of redundancy. The concept of hardware redundancy in passive FTC systems can be defined as the application of identical components with the same input signal so that the duplicated output signal can be compared with the main component to switch between redundant actuators in case of performance degradation to mitigate the fault effect [17]. As can be seen in Figure 1, in passive FTC design, redundancy can be considered in the controller, actuators, plant components, and sensors that the FTC system can switch to them in the presence of a fault in the system.

Figure 1. Passive FTC Structure: This kind of controller can be designed by considering redundant controller/actuator/plant/sensor and in the presence of fault will switch to the redundant component.

Several approaches have been used in designing passive FTC varies from sliding mode control (SMC) approach [20][21][22] to H[23][24][25], Linear Quadratic control [26], fuzzy logic control [27][28], Lyapunov-based control [29], and control allocation [30][31][32]. Such control strategies are commonly less complicated and are popular due to their simplicity in design and application, less lag between fault occurrence and accommodation, and their low computation load [7][8][9].

The main challenges of passive FTC can be summarized as

(1) The extreme dependency on hardware redundancy: despite the advantage of having redundant hardware in improving the reliability of the system, having redundant hardware increases the product cost, and also increases the needed space (product size) and the weight of the product. It is obvious that the key components need redundancy to avoid breakdown, but applying redundancy for the whole system would be costly and difficult to be applied considering the weight and space limits.

(2) Passive FTC strategies rely on the assumption that the system will maintain its asymptotic stability of the closed-loop under specified fault/failure scenarios. However, this assumption may not be sufficient to prevent the system break down in the presence of a large number and unforeseen faults.

(3) Due to the fact that in passive FTC design, the normal and fault/failure conditions should be considered simultaneously, in the performance aspect, they are more conservative compared to active FTC design. In other words, passive FTC systems focus on the robustness of the system considering all the scenarios rather than the optimal performance for each scenario, i.e., to guarantee the stability of the system in the presence of a fault, the settling time of the controller would be increased even in a normal situation.

For these reasons, active FTC system received great attention among the researchers [20][33][34][35][36][37][38][39][40][41][42].

2.2. Active FTC

In contrast with passive FTC systems, active FTC systems react to each fault differently. This reaction is based on the control approach used in the active FTC design and information received from the detection system. Generally, an active FTC design has three main steps: (1) Detection, (2) Supervision, (3) Control. Figure 2 shows the three main steps and their roles in designing active FTC systems.

Figure 2. General Structure of active FTC systems.

Generally, in designing an efficient active FTC system, three major factors should be considered: First, the detection unit should be accurate. False fault alarm and inaccurate fault measurement have a direct impact on the performance of the active FTC system. This inaccuracy will lead to a negative reaction to the fault and would even endanger the system stability. Second, the designed active FTC should be robust against the imperfect fault detection information. Third, the time spent for fault recovery should be less than the available time for recovery. In other words, control reconfiguration/fault compensation should be fast enough to guarantee system stability and performance.

In fact, the most important part of an active FTC system is its FDI unit; thus, we categorized active FTC systems based on the FDI approach used in their design. In the following section, we reviewed different approaches for FDI design.

Active FTC approaches are mainly categorized based on the FDI unit used in their design. However, the strategy used for the compensation of fault might be different. Here, a brief review of different fault compensation approaches used in active FTC design is presented.

3. Conclusions

An active FTC approach is generally more efficient in dealing with different types of faults; however, the controller performance is primarily dependent on their FDI unit in providing timely and accurate fault information. We categorized FDI system based on the approaches used in their design into three main categories: model-based, knowledge-based, and combined model-knowledge-based approaches. The model-based approaches are simple to implement; however, their performance is highly dependent on the accuracy of the mathematical model of the system. The knowledge-based approaches are not dependent on the mathematical model of the system; however, they need a huge historic data about the system performance for training purposes. The combined model-knowledge-based approach has less dependency on model accuracy and needs less training data; however, the design complexity would increase and the designer should have knowledge of both approaches to design an efficient system.

This entry is adapted from the peer-reviewed paper 10.3390/electronics9091513

References

  1. Stengel, R.F. Intelligent failure-tolerant control. IEEE Control Syst. 1991, 11, 14–23.
  2. Patton, R.J. Fault-tolerant control: The 1997 situation. IFAC Proc. Vol. 1997, 30, 1029–1051.
  3. Lunze, J.; Richter, J.H. Reconfigurable fault-tolerant control: A tutorial introduction. Eur. J. Control 2008, 14, 359.
  4. Alwi, H.; Edwards, C.; Tan, C.P. Fault tolerant control and fault detection and isolation. In Fault Detection and Fault-Tolerant Control Using Sliding Modes; Springer: Berlin/Heidelberg, Germany, 2011; pp. 7–27.
  5. Zhang, Y.; Jiang, J. Bibliographical review on reconfigurable fault-tolerant control systems. Annu. Rev. Control 2008, 32, 229–252.
  6. Jiang, J.; Yu, X. Fault-tolerant control systems: A comparative study between active and passive approaches. Annu. Rev. Control 2012, 36, 60–72.
  7. Yu, X.; Jiang, J. A survey of fault-tolerant controllers based on safety-related issues. Annu. Rev. Control 2015, 39, 46–57.
  8. Moor, T. A discussion of fault-tolerant supervisory control in terms of formal languages. Annu. Rev. Control 2016, 41, 159–169.
  9. Fekih, A. Fault diagnosis and fault tolerant control design for aerospace systems: A bibliographical review. In Proceedings of the 2014 American Control Conference (ACC), Portland, OR, USA, 4–6 June 2014; pp. 1286–1291.
  10. Yin, S.; Xiao, B.; Ding, S.X.; Zhou, D. A review on recent development of spacecraft attitude fault tolerant control system. IEEE Trans. Ind. Electron. 2016, 63, 3311–3320.
  11. Qi, X.; Qi, J.; Theilliol, D.; Zhang, Y.; Han, J.; Song, D.; Hua, C. A review on fault diagnosis and fault tolerant control methods for single-rotor aerial vehicles. J. Intell. Robot. Syst. 2014, 73, 535–555.
  12. Campos-Delgado, D.; Espinoza-Trejo, D.; Palacios, E. Fault-tolerant control in variable speed drives: A survey. IET Electr. Power Appl. 2008, 2, 121–134.
  13. Bourogaoui, M.; Sethom, H.B.A.; Belkhodja, I.S. Speed/position sensor fault tolerant control in adjustable speed drives—A review. ISA Trans. 2016, 64, 269–284.
  14. Pillai, D.S.; Rajasekar, N. A comprehensive review on protection challenges and fault diagnosis in PV systems. Renew. Sustain. Energy Rev. 2018, 91, 18–40.
  15. Song, Y.; Wang, B. Survey on reliability of power electronic systems. IEEE Trans. Power Electron. 2013, 28, 591–604.
  16. Mirafzal, B. Survey of Fault-Tolerance Techniques for Three-Phase Voltage Source Inverters. IEEE Trans. Ind. Electron. 2014, 61, 5192–5202.
  17. Gao, Z.; Cecati, C.; Ding, S.X. A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 2015, 62, 3757–3767.
  18. Cecati, C. A survey of fault diagnosis and fault-tolerant techniques—Part II: Fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Trans. Ind. Electron. 2015, 62, 1.
  19. Tabbache, B.; Rizoug, N.; Benbouzid, M.E.H.; Kheloui, A. A control reconfiguration strategy for post-sensor FTC in induction motor-based EVs. IEEE Trans. Veh. Technol. 2013, 62, 965–971.
  20. Alwi, H.; Edwards, C.; Stroosma, O.; Mulder, J. Fault tolerant sliding mode control design with piloted simulator evaluation. J. Guid. Control. Dyn. 2008, 31, 1186–1201.
  21. Qinglei, H.; Zhang, Y.; Xing, H.; Bing, X. Adaptive integral-type sliding mode control for spacecraft attitude maneuvering under actuator stuck failures. Chin. J. Aeronaut. 2011, 24, 32–45.
  22. Wang, R.; Wang, J. Passive actuator fault-tolerant control for a class of overactuated nonlinear systems and applications to electric vehicles. IEEE Trans. Veh. Technol. 2013, 62, 972–985.
  23. Yang, Z.; Blanke, M.; Verhaegen, M. Robust control mixer method for reconfigurable control design using model matching. IET Control Theory Appl. 2007, 1, 349–357.
  24. Shen, H.; Park, J.H.; Wu, Z.G. Finite-time reliable L 2-L∞/H ∞ control for Takagi–Sugeno fuzzy systems with actuator faults. IET Control Theory Appl. 2014, 8, 688–696.
  25. Shen, H.; Su, L.; Park, J.H. Reliable mixed H∞/passive control for T–S fuzzy delayed systems based on a semi-Markov jump model approach. Fuzzy Sets Syst. 2017, 314, 79–98.
  26. Staroswiecki, M.; Yang, H.; Jiang, B. Progressive accommodation of parametric faults in linear quadratic control. Automatica 2007, 43, 2070–2076.
  27. Wu, H.N. Reliable LQ fuzzy control for continuous-time nonlinear systems with actuator faults. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 2004, 34, 1743–1752.
  28. Zeghlache, S.; Kara, K.; Saigaa, D. Fault tolerant control based on interval type-2 fuzzy sliding mode controller for coaxial trirotor aircraft. ISA Trans. 2015, 59, 215–231.
  29. Benosman, M.; Lum, K.Y. Passive actuators’ fault-tolerant control for affine nonlinear systems. IEEE Trans. Control Syst. Technol. 2010, 18, 152–163.
  30. Luo, Y.; Serrani, A.; Yurkovich, S.; Oppenheimer, M.W.; Doman, D.B. Model-predictive dynamic control allocation scheme for reentry vehicles. J. Guid. Control. Dyn. 2007, 30, 100–113.
  31. Johansen, T.A.; Fossen, T.I. Control allocation—A survey. Automatica 2013, 49, 1087–1103.
  32. Hua, M.D.; Ducard, G.; Hamel, T.; Mahony, R.; Rudin, K. Implementation of a nonlinear attitude estimator for aerial robotic vehicles. IEEE Trans. Control Syst. Technol. 2014, 22, 201–213.
  33. Wang, Z.; Liu, L.; Zhang, H.; Xiao, G. Fault-tolerant controller design for a class of nonlinear MIMO discrete-time systems via online reinforcement learning algorithm. IEEE Trans. Syst. Man Cybern. Syst. 2016, 46, 611–622.
  34. Ye, D.; Yang, G.H. Adaptive fault-tolerant tracking control against actuator faults with application to flight control. IEEE Trans. Control Syst. Technol. 2006, 14, 1088–1096.
  35. Alwi, H.; Edwards, C. Fault tolerant longitudinal aircraft control using non-linear integral sliding mode. IET Control Theory Appl. 2014, 8, 1803–1814.
  36. Yu, X.; Jiang, J. Hybrid fault-tolerant flight control system design against partial actuator failures. IEEE Trans. Control Syst. Technol. 2012, 20, 871–886.
  37. Lu, P.; Van Eykeren, L.; van Kampen, E.J.; de Visser, C.; Chu, Q. Double-model adaptive fault detection and diagnosis applied to real flight data. Control Eng. Pract. 2015, 36, 39–57.
  38. Gao, Z.; Jiang, B.; Shi, P.; Qian, M.; Lin, J. Active fault tolerant control design for reusable launch vehicle using adaptive sliding mode technique. J. Frankl. Inst. 2012, 349, 1543–1560.
  39. Bateman, F.; Noura, H.; Ouladsine, M. Fault diagnosis and fault-tolerant control strategy for the aerosonde UAV. IEEE Trans. Aerosp. Electron. Syst. 2011, 47, 2119–2137.
  40. Park, P.; Khadilkar, H.; Balakrishnan, H.; Tomlin, C.J. High confidence networked control for next generation air transportation systems. IEEE Trans. Autom. Control 2014, 59, 3357–3372.
  41. Nazarnia, H.; Sarmasti, H. Characterizing infrastructure resilience in disasters using dynamic network analysis of consumers’ service disruption patterns. Civ. Eng. J. 2018, 4, 2356–2372.
  42. Abbaspour, A.; Sargolzaei, A.; Forouzannezhad, P.; Yen, K.K.; Sarwat, A.I. Resilient Control Design for Load Frequency Control System under False Data Injection Attacks. IEEE Trans. Ind. Electron. 2019, 67, 7951–7962.
More
This entry is offline, you can click here to edit this entry!
ScholarVision Creations