To Learn or Not to Learn - The AFDIA Scheme

The AFDIA Scheme

6.11 To Learn or Not to Learn

This section is independent of the results presented in this chapter. Instead, the aim here is to express the view of the author on how the schemes (SFDIA and AFDIA)

developed in this thesis could be considered for industrial implementation. These implementation issues are discussed from the perspective of the need to learn (or adapt on-line).

Before an aircraft system is in service, it has to be certified to add confidence in the system and meet strict safety requirements. This certification also applies to the control software on-board the aircraft systems. Certification is provided by authorities such as the Federal Aviation Administration (FAA) in the US or the Civil Aviation Authority (CAA) in the UK. The main document used as a guideline for the certification process is the DO-178 [101].

In accordance with this document, it is challenging to certify control software that is adaptive in nature. One of the major obstacles here is that adaptive control systems, such as the AFDIA scheme presented here, are considered non deterministic in nature and therefore it is harder to verify the stability of the control software. Due to this, the aerospace industry in general is reluctant to use such adaptive systems in practice. However, an argument can be made to use such systems, by considering when the system learns or adapts on-line.

In [37], the authors present an SFDIA scheme using NNs. Another NN based SFDIA is presented by the authors in [11]. In both of these schemes, the NN sensor estimators adapt on-line, even in the absence of failure. The main reasoning here is that the estimators improve their estimations by adapting to the dynamic environment of the aircraft. When a sensor failure occurs, the on-line learning is terminated to avoid degrading the sensor estimations. However, the question has to be raised on the necessity to learn in the absence of failure.

The SFDIA scheme presented in this thesis does not learn in the presence or absence of failure. Once the NN based estimators are developed, their structure remains fixed (i.e. no change in the weights or the number of neurons) through-out their lifetime. The reasoning for this is that if the NN based sensor estimators produce good estimates of the hardware sensors, there is no need to adapt the esti-mators on-line to the changing dynamics of the aircraft environment. The hardware sensors have already been certified and are implemented in practical systems,

with-136

out any need to adapt to the environment dynamics. Therefore, if the structure of the NN based sensor estimations are fixed, they can be classed as deterministic software and considered for certification using the existing practices.

Additionally in [11], the AFDIA scheme implements NN based pitch, roll and yaw controllers that adapt in the absence of failures. Once again, the argument here is to improve the controller estimation with the changing aircraft dynamics.

Once a failure occurs, the NN controllers adapt using different guiding functions to accommodate the failures. Similar to the SFDIA scheme, an argument can be made that, if the NN controllers produce good estimates of the normal controls that have already been certified and which do not adapt on-line, there is no need to adapt (or learn) the NN based controllers in the absence of failures. Therefore, the NN based controllers with fixed structure can be certified using existing practices. The NN controllers will only adapt on-line when there is a failure (i.e. a need to learn the new dynamics of the aircraft).

In conclusion, although NNs have learning capabilities, one should consider when this is beneficial to the intended application.

6.12 Conclusion

In this chapter, an FCC NN based actuator failure detection, identification and accommodation (AFDIA) scheme was presented. The aim of this scheme was to add endurance to an aircraft following 66% loss of wing surface. This chapter presented the development of an FCC NN based roll controller, which was used by the AFDIA scheme. This FCC NN roll controller uses only 5 neurons to control the roll attitude of an aircraft.

The experiments were conducted using the Airbus A320 aircraft model in Plane. Following a 66% loss of wing surface, the aircraft under the control of X-Plane built-in autopilot would go into an uncontrollable spin and crash. This can be attributed to the fact that the autopilot is unaware of the change in dynamics of the aircraft following such a sever failure. An ideal scenario for the AFDIA scheme

Initially, an AFDIA scheme was developed and studied on 240 experiments.

Based on the observations from these experiments (Section 6.6), an improved version of the AFDIA scheme was developed. In the original AFDIA scheme (Section 6.4), the aircraft avoided going into an uncontrollable spin, like the results from the X-Plane built-in autopilot (Section 6.2). The aircraft did eventually crash, but the scheme increased the duration of the flight following a failure, when compared against the X-Plane autopilot results. Hence, adding endurance to the aircraft in presence of the failure. However, the ideal scenario of maintaining flight following failure was not achieved.

With the improved AFDIA scheme (Section 6.7.3), not only did the aircraft manage to avoid an uncontrollable spin, but also maintained flight following such a severe failure. This is remarkable, considering the fact that the aircraft maintains flight with 66% of the wing surface missing. Therefore, with the improved AFDIA scheme, the ideal scenario of maintaining flight following this failure is achieved.

This is an exceptional addition of endurance to the aircraft system in the presence of such an extreme failure. Due to timing constraints on the research, the improved AFDIA scheme was evaluated on 20 experiments only, each for 5 minutes duration.

During the duration of these experiments, the aircraft did not crash following the failure. However, to highlight the endurance added by the improved AFDIA scheme, 6 experiments were conducted, each of which spanned a duration of 2 hours. Dur-ing these experiments, the aircraft maintained flight with 66% of the wDur-ing surface missing. This demonstrated the endurance added by the improved AFDIA scheme over long duration flight.

One of the drawbacks of the scheme was the use of the fixed threshold based mechanism for fault detection. The threshold must be predetermined, taking into account the probability of false detection and longer detection time. It must be noted that, in all the experiments conducted in this chapter, the failure was promptly detected and there was no false detection. However, this 100 % detection of failure can be attributed to the fact that the failure simulations were limited to the straight level flight manoeuvre phase of the aircraft. Further work needs to be conducted to

138

improve on the detection mechanism.

In conclusion, an AFDIA scheme is presented in this chapter that can add en-durance to an aircraft with 66% of the wing surface missing. The AFIDA scheme manages to maintain flight in the presence of such a severe failure. The results pre-sented in this chapter also validate the use of an FCC NN for AFDIA applications.

The AFDIA scheme is able to add such remarkable endurance with just 5 neurons in the FCC NN based roll controller.

1000 2000 3000 4000 5000 6000 7000

−6

1000 2000 3000 4000 5000 6000 7000

−15

1000 2000 3000 4000 5000 6000 7000

1500

Figure 6.16: Aircraft flight data over 2 hours following left wing failure. The Red line marks when the failure was injected.

140

1000 2000 3000 4000 5000 6000 7000

−6

1000 2000 3000 4000 5000 6000 7000

−15

1000 2000 3000 4000 5000 6000 7000

1000

Figure 6.17: Aircraft flight data over 2 hours following left wing failure. The Red line marks when the failure was injected.

1000 2000 3000 4000 5000 6000 7000

−4

1000 2000 3000 4000 5000 6000 7000

−20

1000 2000 3000 4000 5000 6000 7000

1000

Figure 6.18: Aircraft flight data over 2 hours following left wing failure. The Red line marks when the failure was injected.

142

1000 2000 3000 4000 5000 6000 7000

−1

1000 2000 3000 4000 5000 6000 7000

1000

Figure 6.19: Aircraft flight data over 2 hours following right wing failure. The Red line marks when the failure was injected.

1000 2000 3000 4000 5000 6000 7000

−1

1000 2000 3000 4000 5000 6000 7000

1000

Figure 6.20: Aircraft flight data over 2 hours following right wing failure. The Red line marks when the failure was injected.

144

1000 2000 3000 4000 5000 6000 7000

−1

1000 2000 3000 4000 5000 6000 7000

1000

Figure 6.21: Aircraft flight data over 2 hours following right wing failure. The Red line marks when the failure was injected.

146

In document Fault Tolerant Flight Control: An Application of the Fully Connected Cascade Neural Network (Page 159-171)