Fault Diagnosis and Fault-Tolerant Control and Guidance for Aerospace Vehicles

This chapter presents briefly the motivations and the book outline. This book presents a number of advanced fault detection and diagnosis and reconfiguration technologies for aerospace vehicles. An attempt is made to develop useful solutions that can be relevant and viable candidates for future space and aeronautical systems. The presented techniques have been tested and validated on highly representative benchmarks, real flight data, or real-world aerospace systems. The examples presented in this book are taken mainly from four recent projects related to fault detection and diagnosis and fault-tolerant control and guidance of aircraft and space systems:

This chapter starts with some basic definitions and concepts as well as a quick literature review on FDIR academic methods. The main concepts of the industrial state-of-practice for space and avionics systems will also be briefly presented. An attempt will be made to analyze major reasons for the slow-progress in applying advanced model-based techniques to real-world aerospace systems. Fault detection and diagnosis (FDD) is an important aspect of process engineering. The primary objective of an FDD system is early detection of faults, isolation of their location, and diagnosis of their causes, enabling correction of the faults before additional damage to the system or loss of service occurs. Abnormal situations occur when processes deviate significantly (outside the allowed range) from their normal regime during online operation. A fault can be defined as an unpermitted deviation of at least one characteristic property or parameter of the system from the standard condition [1]. A failure is a permanent interruption of a system’s ability to perform a required function under specified operating conditions. Within the academic literature, the terminology is now more or less standardized. Such malfunctions may occur in the individual unit of the plants, sensors, actuators, or other devices and affect adversely the local or global behavior of the system. Process abnormalities are usually classified into additive or multiplicative faults according to the effects on a process. In general, additive faults affect processes as unknown inputs, while multiplicative faults usually have important effects on the process dynamics and can cause unstable behaviors. Abrupt faults are sudden changes in behavior of the system (step like), while incipient faults are gradual and slow drifting faults. Permanent faults lead to the total failure of the equipment (once they occur they do not disappear), transient faults are temporary malfunctioning (appear for a short time and then disappear), and intermittent faults are the repeated occurrences of transient faults (they appear, disappear, and then reappear). Hidden faults are those which are present on standby equipment and visible only when this equipment is activated.

This chapter deals with model-based Fault Detection and Diagnosis (FDD) methods which have been recently applied to Oscillatory Failure Case (OFC) in aircraft control surface servo-loops. This failure case, related to the Electrical Flight Control System (EFCS), could have an influence on structural loads and aircraft controllability. Two methods will be presented and, in order to improve FDD performance and robustness, the tuning of their free design parameters are discussed. The presented methods are nonlinear observer design and fault reconstruction via sliding-mode differentiation. The efficiency of the above techniques will be illustrated through their application to highly representative aircraft benchmarks, real flight data, and real-time implementation on Airbus test facilities. In addition to thrust control, the principal means of controlling an aircraft is through aerodynamic forces generated by control surfaces which are generally movable flaps located on the fuselage, wing, and tail. The primary purpose of certain control surfaces (e.g., elevator, rudder, and ailerons) is to generate control moments; hence, their resultant forces act at some distance from the aircraft center of mass. In this section the main control surfaces and their functions are briefly recalled (see Fig. 3.1).

This chapter is dedicated to two other important EFCS-failure cases in aviation: runaway and jamming. A runaway is an untended (or uncontrolled) deflection of a control surface which can go until its stops if it remains undetected. A jamming is a scenario where a control surface is physically stuck at its current position. It will be shown that by careful fault modeling, simple estimation techniques (Kalman-based) can lead to remarkable results. The technique has been implemented as a part of the A380 Flight Control Computer (FCC) software and provided very good results on the Airbus test facilities. The robustness of the method has been confirmed during about 70 h of flight tests. This chapter follows the basic problem addressed in the previous chapter and deals with two other important EFCS-failure cases: runaway (aka hardover) and jamming (or lock-in-place failure) of aircraft control surfaces. Early and robust detection of such failures is also an important issue for achieving sustainability goals and for early system reconfiguration [1]. The chapter focuses on the elevator runaway and jamming. As outlined in the previous chapter, the elevator setting controls the pitch angle, an important function especially during takeoff and landing.

This chapter is dedicated to techniques for ensuring fault tolerance in redundant aircraft sensors involved in computation of flight control laws. The objective is to switch off the faulty sensor and to compute a reliable (a.k.a. as “consolidated”) parameter using data from valid sensors, in order to eliminate any anomaly before propagation in the control loop. The benefit of the presented method is to improve the consolidation process with a fault detection and isolation approach when only few sources (less than three) are valid. Different techniques are compared to accurately detect any behavioral change of the sensor outputs. The approach is tested on a recorded flight dataset. This chapter is dedicated to fault detection and isolation of redundant aircraft sensors involved in the computation of flight control laws. The objective is to switch off the erroneous sensor and to compute a so-called consolidated parameter using data from valid sensors, in order to eliminate any anomaly before propagation in the control loop. We will focus on oscillatory failures and present a method for integrity control based on the processing of any flight parameter measurement in the flight control computer (FCC) like, e.g., anemometric and inertial data. One of the main tasks dedicated to the FCC is the flight control laws (FCL) computation which generates a command (position order) to servo-control each moving surface (see Fig. 5.1). The comparison between the pilot commands (or the piloting objectives) and the aircraft state is used for FCL computation. The aircraft state is measured by a set of sensors delivering, e.g., anemometric and inertial measurements that characterize the aircraft attitude, speed, and altitude. The data is acquired using an acquisition system composed by several dedicated redundant units (usually three). The FCC receives three redundant values of each flight parameter data from the sensors and must compute unique and valid flight parameters required for the FCL computation. This specific data fusion processing, called “consolidation,” classically consists of two simultaneous steps (Fig. 5.2): selection or computation of one unique parameter from the three available sources, and, in parallel, monitoring of each of the three independent sources to discard any faulty one. As a consequence, the consolidation allows reliable flight parameters computation with the required accuracy by discarding any involved failed source.

This chapter deals with the next step following the design of an FDD system, i.e. appropriate recovery strategies, based on all available actuator/sensor/communication resources. An active fault tolerant flight control strategy based on H _∞ design tools is presented. The Fault Tolerant Control (FTC) strategy operates in such a way that once a fault is detected and confirmed by an FDD unit, a compensation loop is activated for safe recovery. A key feature of the proposed strategy is that the added FTC loop keeps unchanged the in-service control laws facilitating the certification of the whole approach and limiting the underlying Verification and Validation activities. The methodology is applied to actuator fault accommodation of a large commercial aircraft during landing approach. The results, obtained from a piloted 6-DoF flight simulator, will be presented and discussed. The application is taken from the GARTEUR project. The problem studied in this chapter is that of design and analysis of an active flight fault-tolerant control system. The chapter presents a practical case study taken from the European GARTEUR project (Flight Mechanics Action Group 16) on fault-tolerant control. Piloted flight simulator experiments are presented which show that fault tolerance can be achieved provided that there exists sufficient onboard control authority.

This chapter is dedicated to space applications. Three application cases will be presented: an Earth observation satellite, a deep space mission and an atmospheric re-entry vehicle. The design method is based on H _∞ /H ₋ tools and is associated with a suitable post-analysis process, the so-called generalized μ-analysis. It is shown that the resulting design/analysis procedure provides an iterative refinement cycle which allows the designer to get “as close as possible” to the required robustness/performance specifications and trade-offs. This chapter is dedicated to actuator fault detection and diagnosis in space applications. Fault tolerance in terms of control and guidance will also be discussed. The design method is based on H _∞ /H ₋ and robust pole assignment tools. Three space applications will be studied:

This chapter is dedicated to final remarks and suggestions on future challenges and opportunities. We focus on what useful and realistic contributions the research community can make in order to develop successful solutions for future space and avionics systems. In this chapter, we would like to discuss briefly some future challenges and opportunities.