Y. K. Wong

Intelligent Automatic Fault Detection for Actuator Failures in Aircraft

This paper applies an intelligent technique based on fuzzy-genetic algorithm for automatically detecting failures in aircraft. The fuzzy-genetic algorithm constructs the automatic fault detection system for monitoring aircraft behaviors. Fuzzy-based classifier is employed to estimates the time of occurrence and types of actuator failure. Genetic algorithms are used to generate an optimal fuzzy rule set for the classifier. The optimization capability of genetic algorithms provides an efficient and effective way to generate optimal fuzzy rules. Different types of actuator failure can be detected online by the fuzzy-genetic algorithm based automatic fault detection system. Simulations with different actuator failures of the nonlinear F-16 aircraft model are reported and discussed.

Modelling and prediction of machining errors using ARMAX and NARMAX structures

Forecasting compensatory control, which was first proposed by Wu [ASME J. Eng. Ind. 99 (1977) 708], has been successfully employed to improve the accuracy of workpieces in various machining operations. This low-cost approach is based on on-line stochastic modelling and error compensation. The degree of error improvement depends very much on the accuracy of the modelling technique, which can only be performed on-line in a real-time recursive manner. In this study, the effect of the control input (i.e. the cutting force) is considered in the development of the error models, and the formulation of recursive exogenous autoregressive moving average (ARMAX) models becomes necessary. The nonlinear ARMAX or NARMAX model is also used to represent this nonlinear process. ARMAX and NARMAX models of different autoregressive (AR), moving average (MA) and exogenous (X) orders are proposed and their identifications are based on the recursive extended least square (RELS) method and the neural network (NN) method, respectively. An analysis of the computational results has confirmed that the NARMAX model and the NN method are superior to the ARMAX model and the RELS method in forecasting future machining errors, as indicated by its higher combined coefficient of efficiency.

Nonlinear system identification using optimized dynamic neural network

In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme-Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.

Adaptive fuzzy approach for a class of uncertain nonlinear systems in strict-feedback form

Adaptive fuzzy control is proposed for a class of affine nonlinear systems in strict-feedback form with unknown nonlinearities. The unknown nonlinearities include two types of nonlinear functions: one satisfies the triangularity condition and can be directly approximated by fuzzy logic system, while the other is assumed to be partially known and consists of parametric uncertainties. Takagi-Sugeno type fuzzy approximators are used to approximate unknown system nonlinearities and the design procedure is a combination of adaptive backstepping and generalized small gain design techniques. It is proved that the proposed adaptive control scheme can guarantee the uniformly ultimately bounded (UBB) stability of the closed-loop systems. Simulation studies are shown to illustrate the effectiveness of the proposed approach.

Model-based fault diagnosis in continuous dynamic systems.

Traditional fault detection and isolation methods are based on quantitative models which are sometimes difficult and costly to obtain. In this paper, qualitative bond graph (QBG) reasoning is adopted as the modeling scheme to generate a set of qualitative equations. The QBG method provides a unified approach for modeling engineering systems, in particular, mechatronic systems. An input-output qualitative equation derived from QBG formalism performs continuous system monitoring. Fault diagnosis is activated when a discrepancy is observed between measured abnormal behavior and predicted system behavior. Genetic algorithms (GAs) are then used to search for possible faulty components among a system of qualitative equations. In order to demonstrate the performance of the proposed algorithm, we have tested it on a laboratory scale servo-tank liquid process rig. Results of the proposed model-based fault detection and diagnosis algorithm for the process rig are presented and discussed.

Fusion of qualitative bond graph and genetic algorithms: A fault diagnosis application

In this paper, the problem of fault diagnosis via integration of genetic algorithms (GAs) and qualitative bond graphs (QBGs) is addressed. We suggest that GAs can be used to search for possible fault components among a system of qualitative equations. The QBG is adopted as the modeling scheme to generate a set of qualitative equations. The qualitative bond graph provides a unified approach for modeling engineering systems, in particular, mechatronic systems. In order to demonstrate the performance of the proposed algorithm, we have tested the proposed algorithm on an in-house designed and built floating disc experimental setup. Results from fault diagnosis in the floating disc system are presented and discussed. Additional measurements will be required to localize the fault when more than one fault candidate is inferred. Fault diagnosis is activated by a fault detection mechanism when a discrepancy between measured abnormal behavior and predicted system behavior is observed. The fault detection mechanism is not presented here.

Direct adaptive fuzzy control for a nonlinear helicopter system

Purpose – To design effective and practical controllers that use the adaptive fuzzy approaches and are applicable to helicopters.Design/methodology/approach – Based on Takagi‐Sugeno fuzzy systems, a new direct adaptive fuzzy control scheme is developed for a class of nonlinear multiple‐input‐multiple‐output systems. A simple observer is designed to generate an error signal for the adaptive law. The system states of the system are not required to be available for measurement.Findings – The overall adaptive scheme guarantees all the signals involved being uniformly bounded in the Lyapunov sense.Research limitations/implications – The implementation of this research work needs further investigation.Practical implications – The simplicity of the design algorithm facilitates the application of the design to helicopters by the use of Matlab.Originality/value – Experimental results of a two degree of freedom helicopter are presented to confirm the usefulness of the proposed new control scheme.

Comparative studies of three adaptive controllers

The performance of three adaptive controllers has been compared with experimental studies on a VVS-400 Heating/ Ventilation System. The control algorithms are Generalized Predictive Control (GPC), an adaptive PID Controller (APC) based on Dahlin algorithm and the VVS-400 Heating/Ventilation System local controller—a Fuji PID autotuner. For the on-line identification module of the first two adaptive control algorithms, the recursive least-squares estimation technique is implemented. It is demonstrated that the two model-based approaches outperform the PID auto-tuner. Moreover, the adaptive PID controller compares favorably with a much more sophisticated GPC. # 1999 Elsevier Science Ltd. All rights reserved.

Lithium-ion battery models for computer simulation

Lithium-ion batteries are very popular nowadays. In order to design and evaluate the performance of systems involving batteries, good models are required for systems simulation. In this paper, popular lithium-ion battery models are investigated and presented. Selection of appropriate models for a particular simulation will also be presented.

Synchronization of Ghostburster neurons under external electrical stimulation via adaptive neural network H∞ control

In this paper, an adaptive neural network H~ control is proposed to realize the synchronization of two Ghostburster neurons under external electrical stimulation. We first analyze the periodic and chaotic dynamics of individual Ghostburster neuron under different external electrical stimulations, then design a H~ controller via adaptive neural networks to synchronize two Ghostburster neurons and drive the slave neuron to act as the master one. Asymptotic synchronization can be obtained by proper choice of the control parameters. Simulation results demonstrate the effectiveness of the proposed control method.