S. Daley

Takagi-Sugeno fuzzy fault-tolerant control of an induction motor

This paper demonstrates the application of a new fault-tolerant control scheme for a rail vehicle traction system using digital signal processing hardware and a representative induction motor test-rig. The approach presented takes into account the stability and design of non-linear fuzzy inference systems based on Takagi–Sugeno (T-S) fuzzy models. The paper derives the necessary conditions for the assignability of eigenvalues to a region in the s-plane and the necessary conditions to guarantee the stability of adaptive fuzzy models. The problem is solved via the Linear Matrix Inequalities (LMI) method.

Design of nonlinear observers for detecting faults in hydraulic sub-sea pipelines ☆

Abstract Firstly, a general nonlaminar model is considered for pipeline dynamics, including a treatment of faults caused by pipe restrictions. For three cases results are given for stability, robustness and fault detectability of a combined observer and residual (fault detection signal). An efficient numerical design algorithm is proposed. The method is applied to an actual experimental pipeline (rig system) which is set up to model a sub-sea umbilical. Results on modelling and on observer and residual (signal) design are given. The effectiveness of the design is tested by inducing two types of fault on the rig system.

Robust fault detection observers for nonlinear polynomial systems

This paper proposes a robust fault detection observer (RFDO) for classes of nonlinear models which consists of polynomial nonlinearities up to degree three and are structured in continuous state-space form. The observer and detection signal are designed to be independent of unknown inputs. Sufficient conditions are given, in an efficient compact form, for an RFDO (observer and signal) to exist. Several useable results on detectablility conditions are given and a numerical procedure is considered. The modelling of a hydraulic rig system is considered. Design steps are shown in obtaining an RFDO which is sensitive to faults at certain nodal points. This RFDO forms part of a bank of three observers used in a fault isolation procedure which is tested on real input-output data.

A hybrid fault diagnosis approach using neural networks

A hybrid fault diagnosis method is proposed in this paper which is based on the parity equations and neural networks. Analytical redundancy is employed by using parity equations. Neural networks then are used to maximise the signal- to- noise ratio of the residual and to isolate different faults. Effectiveness of the method is demonstrated by applying it to fault detection and isolation for a hydraulic test rig. Real data simulation shows that the sensitivity of the residual to the faults is maximised, whilst that to the unknown input is minimised. The simulated faults are successfully isolated by a bank of neural nets.

Supervisory Takagi-Sugeno Fuzzy Fault-Tolerant Control of a Rail Traction System

Abstract This paper investigates the development of a supervisory control system with qualitative tasks at the upper level and simple quantitative models at the lower level to control complex non-linear systems. A new quantitative approach for the stability of non-linear fuzzy inference systems using Takagi-Sugeno (T-S) fuzzy models is presented. The necessary conditions for the assignability of eigenvalues to a region in the s-plane and the necessary conditions to guarantee the stability of fuzzy models for both T-S fuzzy observers and T-S fuzzy controllers together are derived. The problem is solved via the Linear Matrix Inequality (LMI) method. The paper demonstrates the feasibility of this system architecture through application to a fault-tolerant design for a railway traction system using DSP in a hardware test-rig.

Design of a Robust Fault Detection Observer for Polynomial Nonlinearities

Abstract This paper proposes a fault detection observer for classes of nonlinear models which consist of bilinear and polynomial nonlinearies up to degree three and are structured in continuous state space form. A design procedure is presented for calculating the observer matrices and is applied to an industrial application.

Neural network based active control of unstable combustion systems

Abstract This paper is concerned with active stabilisation of unstable combustion systems using neural networks. A novel active control strategy is proposed. It is comprised of three parts: an output model, an output predictor and a feedback controller. The output model is established using neural networks to model the measured output. To overcome the time delay of the system, which is often very large compared with the sampling period, the output predictor is developed using the output model. Unlike a classical observer (e.g., state observer), only a measured output signal is required. An output-feedback controller is introduced which uses the output of the predictor. The approach developed is first demonstrated using a simulated unstable combustor with six modes. Results are also presented showing its application to an experimental combustion facility using a loudspeaker actuation device.

On eigenstructure assignability using parametric output feedback

Abstract This paper presents a new solution to the problem of eigenstructure assignment in linear multivariable systems with incomplete state observation. A simple procedure for defining an assignable set of right eigenvectors for a prescribed eigenvalue set is presented. The effectiveness of the method is demonstrated by designing controllers to assign different combinations of eigenvalue types in a fourth order system having three outputs and two inputs.

Identification and adaptive control of SISO singular systems

A general adaptive control scheme is presented for an unknown time invariant singular system of the form Ex(t + 1) = Ax(t) + Bu(t); y(t) = Cx(t). Owing to the non-causality of this kind of system, the identification of unknown parameters is re-considered and a new residual signal is constructed and used in the recursive calculations. A general design procedure is obtained that uses the identified parameters and includes two steps: (i) preliminary output feedback gain design in order to make the original system causal; (ii) adaptive control design for the causal system. It has been shown that any adaptive control algorithm can be combined with this scheme to obtain a globally stable closed-loop system. The design procedure is shown to perform well on a simulation of a third-order singular system