Fault signatures of sensors.

## Abstract

A Lagrangian formalism is used to model a PVTOL in order to obtain an aircraft model. The Euler‐Lagrange model of the PVTOL is used to develop an algorithm for fault diagnosis. Diagnosis implies the detection, isolation and identification of a fault. The considered approach is based on the knowledge of a system model as well as the model of the possible faults. The idea is to use non‐linear decoupling approach to derivate a set of subsystems, each related to a specific fault or a set of faults. An observer‐based residual generation is designed for each subsystem, this structure allows the fault detection and isolation stage, for fault identification a kind of approximated inversion algorithm to meet the different diagnostic levels. The results are obtained taking advantage of the structure given by the Euler‐Lagrange modelling of the PVTOL as well as from recent results related to observer design and fault identification.

### Keywords

- fault diagnosis
- Lagrangian systems
- non‐linear systems
- observers
- fault isolation

## 1. Introduction

Nowadays, the unmanned aerial vehicles (UAVs) represent a big boom in the electronic industry, thanks to their versatility and largely due to the falling cost of the electronic parts and the UAV by itself. UAV is a kind of an aerial vehicle that is able to take off vertically, such as helicopters and some special airplanes, and it is represented by the planar vertical take‐off landing (PVTOL) aircraft model. Note that PVTOL aircraft models represent more than only UAV systems. Reliability requirements in aerial vehicles bring the necessity of a fault detection and isolation schema. In general, they are non‐linear systems, and so a non‐linear inspired strategy for the detection and isolation of faults could also be used. An idea consists in taking advantage of the structure given by the Lagrangian model of a PVTOL in order to develop an algorithm for the detection, isolation and identification of faults.

Many research studies dealing with the fault detection and isolation (FDI) problem have been already published, most of them deals with linear systems, see for instance Refs. [1–3]. On the other hand, for non‐linear systems, some solutions exist, based on the inherited characteristics, see Refs. [4, 5] for more details. The most common approach used for FDI is the hardware redundancy; however, this approach normally represents an increment in weight and economical cost of the aircraft. In order to avoid this problem, some mathematical relations could be used, the simplest way is to compare two or more internal signals, having as goal to create a residue, which, in fact will be zero if the system is working normally and different from zero if not. In order to create such relations it is common to exploit some intrinsic characteristics of the systems. See for instance Ref. [6]. Diagnosis for the PVTOL system has been considered previously using a Hamiltonian formalism [7].

A Lagrangian formalism is used to model a PVTOL in order to obtain an aircraft model. The Euler‐Lagrange model of the PVTOL is used to develop an algorithm for fault diagnosis. Diagnosis implies the detection, isolation and identification of a fault. The considered approach is based on the knowledge of a system model as well as the model of the possible faults. The idea is to use non‐linear decoupling approach to derivate a set of subsystems, each related to a specific fault or a set of faults. An observer‐based residual generation is designed for each subsystem. Detection and isolation of faults can be reached at this stage, for fault identification a kind of approximated inversion algorithm to meet the different diagnostic levels. The results are obtained taking advantage of the structure given by the Euler‐Lagrange modelling of the PVTOL as well as from recent results related to observer design and fault identification.

Fault diagnosis algorithms can be developed for a more or less general Euler‐Lagrange model of a system, which, in fact, also include a PVTOL system. Fault diagnosis includes detection, isolation and identification of faults. In order to meet a diagnosis task, an observer‐based residual generator is designed in order to determine whether a fault is present. A decoupling approach is used in order to guarantee also a fault isolation task. As discussed, both steps could be systematically developed for the considered system model. Further, fault isolation is approached using a kind of approximated system inversion to develop approximated fault estimation through dynamic inversion of the corresponding residual equation. The schema is shown using a specific example of a PVTOL. As presented in the results, the proposed approach can be used effectively for the diagnosis of a PVTOL system.

## 2. Lagrangian modelling

There is a huge amount of literature about Lagrange's equations of movement, however, see for example Ref. [8]. The structure of a *P*lanar‐*V*ertical‐*T*ake‐*O*ff and *L*anding (PVTOL) is represented in Figure 1.

The absolute linear position to the PVTOL is defined in the inertial frame *θ*, i.e. the rotation angle around the *y*‐axis, and yaw angle *z*‐axis, are zero. The only angular movement is the roll angle *x*‐axis.

The origin of the body frame (also the origin of the inertial frame) is the centre of mass of the PVTOL system. The PVTOL is assumed to have a symmetric structure with the two arms aligned with the body *x*‐axis. The inertia is represented by

The Lagrangian is defined as the sum of kinetic energy minus the potential energy

The movement equations of Lagrange are given by

where *y*‐axis, *z‐*axis and

For the PVTOL results:

So that the Lagrangian results

and the terms

The generalised forces (in the inertial frame) are given by

where *z*‐axis of the body frame. *y*‐axis of the body frame. The moment acting on the rolling angle is given by

The movement equations are given by

## 3. Diagnosis approach

Fault diagnosis aim to detect the fault occurrence in the functional units of the system, as well as to classify the different faults and to determine the type, magnitude and cause of faults, which leads to undesired behaviour of the whole system. The fault diagnosis can be achieved by hardware redundancy or software redundancy also called analytical redundancy. One technique of fault diagnosis is the model‐based fault diagnosis, which employs software redundancy.

In the model‐based fault diagnosis technique, the system behaviour is online reconstructed by a mathematical model, which is implemented in the software form. In this scheme, the system model run in parallel to the system and both of them are driven by the same control inputs. Thus, in the fault‐free case, reconstructed system variables by the system model follow the corresponding real system variables and show a derivation in the faulty case.

A comparison of the measured system variables with their estimates by the system model is called residual. Thus, a residual signal includes the fault effect, and ideally if the residual signal is different from zero, then a fault has occurred otherwise the system is fault free. The residual generation process is carried out in two stages, first, the system outputs have to be estimated, then, the difference between those signals and the signal coming from sensors is computed [9].

Figure 2 shows the general scheme for residual generation using a model‐based fault diagnosis technique.

In this contribution, a fault diagnosis for systems with model Euler‐Lagrange is presented. A model‐based fault diagnosis technique with analytical redundancy is used to obtain a residual generation.

Consider a dynamic system without faults described by the following Euler‐Lagrange equations

where

In this work, additive faults in control input and sensor are considered. The Euler‐Lagrange model of the faulty system is defined as

where

**Assumption 1.** *Consider an Euler‐Lagrange system with faults described by Eq. (15) and the system behaviour is on line reconstructed by the Euler‐Lagrange system without faults Eq. (14), then the faults presented in the system Eq. (15) can be detected by the residual generator*

## 4. Application results

The method presented in the previous section is applied in a PVTOL. Only additive faults are taken into account, the faults could affect sensors (

The faulty PVTOL system is defined as

where

Among the different detection methods available in the literature [2], the threshold‐based approach is one of the most common thanks to its simplicity and accuracy. The principle of this approach is based on the idea that the parameters of the system (e.g. mass and dimensions) could vary because of the measurement or estimation errors. Those data are used to determine a threshold, which is computed by varying the internal parameters of the system in a certain ± percentage. This is carried out in order to avoid false alarms caused by the difference between the mathematical model and the real system.

The internal parameters of the PVTOL that may vary are the mass (

As explained in the beginning of this section only additive faults are taken into account, since the controller is designed to stabilize the system in hover flight, the fault amplitude is defined as a percentage of the initial value for sensors and a percentage of the maximum amplitude of the input control. This percentage is fixed ±10% for sensors and ±5% for control inputs. The faults are triggered 7 seconds after the beginning of the simulation and it is persistent until the end.

In order to detect the fault, six different residues are computed, for this, it is assumed that the entire state is available, according to the previous section as follows:

where the

### 4.1. Sensor faults

The sensor faults considered in this work affect the vertical measurement (

Once the residue exceeds the detection threshold, the fault is considered detected and it will be isolable if and only if the fault signature is different among the others. As expected, and thanks to the controller design every fault affecting the sensors is isolable. The fault signatures are presented in Table 1.

Fault | R1 | R2 | R3 | R4 | R5 | R6 |
---|---|---|---|---|---|---|

Sensor | ||||||

Sensor | ||||||

Sensor |

### 4.2. Control inputs faults

The fault amplitudes of the control inputs are fixed by obtaining the 5% of the maximum size of them during an unfaulty simulation, after this processes the amplitudes are fixed to 0.5, 0.425 and 0.008 for

Fault | R1 | R2 | R3 | R4 | R5 | R6 |
---|---|---|---|---|---|---|

Control input | ||||||

Control input | ||||||

Control input |

## 5. Conclusion

This work presents a fault detection and isolation approach applied to Lagrangian systems.

Every fault is detectable and isolable as it can be seen in Tables 1 and 2, this result is obtained thanks to the special design of the state feedback controller, by consequence the faults affecting the sensors are easily isolable, and the residues affected during a control input fault are those related to the measures affecting the states, for instance a fault affecting