Showing papers by "Paul M. Frank published in 2003"

Fuzzy gain scheduling: controller and observer design based on Lyapunov method and convex optimization

Abstract: Addresses model-based fuzzy control. A constructive and automated method for the design of a gain-scheduling controller is presented. Based on a given Takagi-Sugeno fuzzy model of the plant, the controller is designed such that stability and prescribed performance of the closed loop are guaranteed. These properties are valid in a wide working range around an equilibrium without restrictions to slowly varying trajectories. The synthesis is based on linear matrix inequalities and convex optimization techniques. If required, a fuzzy state estimator and an extended controller can be included, providing a zero steady-state error in the presence of disturbances and modeling errors. The proposed method has been applied to a control of a laboratory liquid-level process. Hence, the performance has been evaluated in simulations as well as in real-time control.

Threshold calculation using LMI-technique and its integration in the design of fault detection systems

Abstract: In this paper, problems related to the threshold calculation and its integration in the design of observer-based fault detection systems are studied. The focus of the study is the application of practical fault detection methods in the framework of observer-based fault detection schemes. The basic idea consists in the formulation of threshold calculation as some standard optimization problems which are then solved using the well-established LMI-optimization technique.

Application of probabilistic robustness technique to the fault detection system design

Abstract: This paper deals with problems related to the computation of false alarm rate (FAR), the determination of thresholds and their integration in the design of observer-based fault detection systems. Different from the known norm-based residual evaluation methods, the probabilistic robustness theory is applied for the purposes of calculating thresholds and FAR.

Study on detecting multiplicative faults in linear dynamic systems

Abstract: In this paper, problems related to detecting and isolating multiplicative faults in linear time invariant (LTI) systems are studied. First, detectability and isolability of multiplicative faults are investigated. Further study is fo- cused on the design of LTI residual generators for the pur- pose of detecting and isolating multiplicative faults. The final study deals with the problem of generating residual signals decoupled from unknown inputs.

Dynamic functional — Link neural networks genetically evolved applied to fault diagnosis

Abstract: The paper addresses the development of neural observer schemes for process fault diagnosis. The design is based on a generalised functional-link neural network with internal dynamics. An evolutionary search of genetic type and multi-objective optimisation in the Pareto-sense is used to determine the optimal architecture of the dynamic network. Symptoms characterising the current state of the process are obtained based on prediction errors. The latter are further evaluated by a static artificial network. Experimental results regarding the detection and isolation of artificial sensor faults in an evaporation station from a sugar factory illustrate the approach.

Multiobjective design of fault detection filters

Abstract: In this paper, problems related to the design of fault detection filters (FDF) for dynamic systems with unknown inputs are studied. Core of our study is to consider the design problems under two different residual evaluation functions, the i?2-norm and the peak amplitude of the residual signals. The background of this study is the fault detection strategy of using multi-residual evaluation functions, which is widely adopted in practice. Based on the above-mentioned two residual evaluation functions, different design schemes for FDF are formulated as multiobjective optimization problems, which are then solved using the well-established LMI-technique.