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This chapter considers sliding modes applied to the problem of observer design. The historical development is outlined leading to the description of a specific class of sliding mode observer which will be used throughout the book. It will be shown how the unique properties associated with the so-called equivalent injection signal necessary to maintain sliding can be exploited to reconstruct actuator and sensor faults modelled as additive perturbations to the inputs and the outputs of the plant. Design methodologies based on Linear Matrix Inequalities (LMIs) are presented. These approaches exploit all the available degrees of freedom associated with the choice of the observer gains. The chapter describes sliding mode observers which can reconstruct faults and yet be robust to disturbances/uncertainties which may corrupt the quality of the reconstructions resulting from mismatches between the model about which the observer is designed and the real system. Initially, the design method is formulated for the case of actuator faults. A comparison is also made between the sliding mode observer schemes developed in the chapter and more traditional linear unknown input observers which are prevalent in the literature.

In this chapter a new sliding mode scheme for reconfigurable control is presented. The controller is based on a state-feedback scheme where the nonlinear unit-vector term is allowed to adaptively increase when the onset of a fault is detected. The scheme is applied to a benchmark aircraft problem. In comparison to other fault tolerant controllers which have been previously implemented on this model, the controllers proposed in this chapter are simple and yet are shown to work across the entire ‘up and away’ flight envelope. Excellent rejection of a certain class of actuator faults is shown. However, the proposed controller cannot directly cope with the total failure of an actuator. In the second half of the chapter, the use of sensor fault reconstruction methods to correct faulty measurements prior to the control law calculations, hence effecting fault tolerant control, is demonstrated. Here, a formal closed-loop analysis is made of the resulting schemes. An example of such a method applied to a benchmark aircraft problem is described.

This chapter examines the assumptions that must be made for the observer schemes described earlier in the book to be applicable. (These amount to relative degree one minimum phase limitations on the transfer function matrices relating the unknown fault signals to the measurements.) This chapter explores ways of obviating these limitations, at the expense of creating cascaded observer structures. The components of the cascade will be observer formulations taken from earlier chapters, and explicit constructive algorithms will be given to ensure the overall scheme can still accurately estimate actuator faults in the case where the relative degree between the faults and the measurements is greater than or equal to two. The advantages that these schemes offer over traditional linear methods (particularly Unknown Input Observers) will be demonstrated.

This chapter formally provides a definition of the terms fault and failure and briefly discusses the different types of faults and failures which can occur in actuators and sensors—with specific aircraft examples. The chapter introduces the concept of fault tolerant control and gives a general overview of the different FTC and FDI research fields. The main concepts and strategies behind some of the FTC and FDI schemes in the literature are also discussed.

This chapters presents the ELAL flight 1862 (Bijlmermeer incident) scenario—which is one of the case studies of GARTEUR AG16. The results presented in this chapter demonstrate the outcome of the ‘flight testing’ campaign and the GARTEUR AG16 final workshop at Delft University of Technology in November 2007. The results represent the successful real–time implementation of a sliding mode controller on SIMONA with experienced test pilots flying and evaluating the controller.

In this chapter the real time implementation of the sensor fault reconstruction schemes (for FDI and FTC) from the previous chapter, on a laboratory crane and a small DC-motor rig, will be discussed. These rigs provide cheap, safe and practical demonstrators for the ideas presented in the previous chapters. The data collection and (subsequent) controller implementation has been achieved using Matlab ® and dSPACE®. Estimates of the sensor faults, obtained from online sliding mode FDI schemes have been used to correct the measured outputs from the sensors. The ‘virtual sensors’ have been used in the control algorithm to form the output tracking error signal which is processed to generate the fault tolerant control signal.

This chapter describes an adaptive model reference sliding mode fault tolerant control scheme with online control allocation. As in the previous chapter, the control allocation scheme uses the effectiveness level of the actuators to redistribute the control signals to the remaining actuators when a fault or failure occurs. Meanwhile, the adaptive non-linear gain and reference model provide online tuning for the controller. This chapter provides a rigorous stability analysis for the model reference scheme. The scheme has been tested on a linearisation of the ADMIRE aircraft model to convey the ideas associated with the proposed scheme and shows that various faults and even total actuator failures can be handled.

In this chapter a brief introduction to the concept of sliding mode control is given and the properties of sliding mode systems are discussed. The chapter also highlights the benefits of sliding modes when applied to the fields of FTC and FDI. A simple pendulum example is used to introduce the concept. The unit vector approach for multi-input systems, sliding surface design and tracking requirements (integral action and model reference based tracking) are also discussed. The chapter ends with some discussions on the benefits and motivation for sliding mode control in the fields of FTC and FDI.

This chapter will focus specifically on sensor faults. Different formulations will be considered in which the measured output signals are filtered to yield ‘fictitious systems’ in which sensor faults appear as ‘actuator faults’. Consequently, the actuator fault reconstruction ideas from the previous chapters can be applied to the fictitious system to reconstruct the sensor fault. The results will also be extended to the case of unstable plants which result in nonminimum phase configurations post-filtering.

This chapter describes the implementation of the sliding mode allocation schemes from the previous chapter on the 6-DOF research flight simulator SIMONA at Delft University of Technology, the Netherlands. The controller is implemented in ‘C’ and runs on the ‘flight control’ computer associated with SIMONA. Real-time implementation issues are discussed and a range of fault scenarios from the GARTEUR AG16 benchmark are tested and discussed.

This chapter proposes an online sliding mode control allocation scheme for fault tolerant control. The effectiveness level of the actuators is used by the control allocation scheme to redistribute the control signals to the remaining actuators when a fault or failure occurs. The chapter provides an analysis of the sliding mode control allocation scheme and determines the nonlinear gain required to maintain sliding. The allocation scheme shows that faults and even certain total actuator failures can be handled directly without reconfiguring the controller.