Linear Time-invariant Plant Models Research Articles

Signal-to-Noise Ratio Based Fault Detection and Identification

In this work, we introduce signal-to-noise ratio (SNR) based fault detection and identification mechanisms for a networked control system feedback loop, where the network component is represented by an additive white noise (AWN) channel. The SNR approach is known to be a steady-state analysis and design tool, thus we first introduce a finite time approximation for the estimated AWN channel SNR. We then consider the case of a general linear time-invariant plant model with one unstable pole. The potential faults that we discuss here cover simultaneously the plant model gain and/or the unstable pole. The fault detection is performed relative to the estimated AWN channel SNR. The fault identification is performed using recursive least squares ideas and then further validated with the observed SNR value, when a fault has been previously detected. We show that the proposed SNR-based fault mechanism (fault detection plus fault identification) is capable of processing the proposed faults. We conclude discussing future research based on the contributions exposed in the present work.

Signal-to-Noise Ratio Constrained Feedback Control over Fading Channels

AbstractIn this paper we address the problem of stabilizing feedback control of a linear time invariant plant model over a communication channel model. The location of the communication channel is in the control path, that is between the controller and the plant, nevertheless similar results are obtained if the alternative location over the feedback path is chosen. The communication channel model itself is selected to be a fading channel type, that is we consider the communication channel model to be constituted of a variable gain and an additive noise process. In a first approach we consider the stabilizing feedback control to be designed for the mean value of the communication channel gain and obtain the infimal channel SNR in closed-form when the gain variability is assumed as an equivalent communication channel multiplicative modelling error. In a second approach we still consider the stabilizing feedback control to be designed for the mean value of the communication channel gain, but obtain the infimal channel SNR in closed-form accounting for the stochastic nature of the channel gain variability.

Robust model-following controller design for LTI systems affected by parametric uncertainties: a design example for aircraft motion

This article addresses the design problem of a robust model-following controller (MFC) which minimises the error between plant controlled output and model output for a linear time-invariant (LTI) plant system affected by parametric uncertainties and an LTI target model. To design such an MFC, a previously proposed MFC scheme, whose applicability has already been demonstrated with flight controller design, is adopted in this article. Our design procedure is as follows: first, a basic MFC is designed using the nominal LTI plant model and the LTI target model while a structured free matrix in the MFC is not assigned; second, model-following performance of the MFC for appropriately defined disturbance input and model input for the parametric uncertain plant model and the LTI target model is minimised using the structured free matrix; and finally, a robust MFC is obtained using the basic MFC and the optimal structured matrix. In the second step, an iterative design method for robust H 2 controllers for LTI parameter-dependent (LTIPD) systems using parameter-dependent Lyapunov functions (PDLFs), which is also proposed in this article, is applied. Two MFCs for the lateral-directional (L/D) motions of a research aircraft, which has been developed for an in-flight simulator, for two different real aircraft models, i.e. a Boeing 747 model and a Lockheed Jetstar model, are designed and their performance is confirmed via numerical simulations and flight tests.

System identification-based control of an unmanned autonomous wind-propelled catamaran

An autonomous catamaran, based on a modified Prindle-19 day-sailing catamaran and fitted with several sensors and actuators was built to test the viability of GPS-based system identification for precision control. Using an electric trolling motor for propulsion, and lead ballast to match all-up weight, several system identification passes were performed to excite system modes and model the dynamic response. The identification process used the Observer Kalman IDentification (OKID) method for identifying a linear time invariant plant model and associated pseudo-Kalman filter. System identification input was generated using a human pilot driving the catamaran on roughly straight line passes. A fourth order discrete time model was generated from the data, and showed excellent prediction results. Using these models, linear quadratic Gaussian (LQG) controllers were designed and tested with the electric trolling motor. These controllers demonstrated excellent line-tracking performance, with error standard deviations of less than 0.15 m. The wing-sail propulsion system was fitted, and these same controllers re-tested with the wing providing all propulsive thrust. Line-following performance and disturbance rejection were excellent, with the cross-track error standard deviations of approximately 0.30 m, in spite of wind speed variations of over 50% of nominal value.

Fuzzy Gain Scheduling for Flutter Suppression in an Unmanned Aerial Vehicle

The active flutter suppression of the flexible modes of a generic model of an unmanned aerial vehicle that exhibits open-loop nonminimum phase behavior and three flutter mechanisms is investigated. A fuzzy gain scheduler interpolates plant models, state feedback, and observer gains based on an exogenous input velocity that is frozen in time. The novelty of the approach rests on the fuzzy interpolation of high-order models and on the method of gain table construction. Closed-loop stability over a wide range of velocities is demonstrated via time simulations for both linear time-invariant plant models and their fuzzy approximations. The fuzzy gain scheduling algorithm demonstrates stronger stability characteristics and generalization capabilities over pointwise synthesized linear quadratic Gaussian controllers, reduced-order controllers, and recurrent neural networks. The resultant global linear controller extends the flutter boundary to a velocity approximately 60% higher than the first open-loop flutter onset speed.

Multiple model adaptive control. Part 1: Finite controller coverings

We consider the problem of determining an appropriate model set on which to design a set of controllers for a multiple model switching adaptive control scheme. We show that, given mild assumptions on the uncertainty set of linear time-invariant plant models, it is possible to determine a finite set of controllers such that for each plant in the uncertainty set, satisfactory performance will be obtained for some controller in the finite set. We also demonstrate how such a controller set may be found. The analysis exploits the Vinnicombe metric and the fact that the set of approximately band- and time-limited transfer functions is approximately finite-dimensional. Copyright © 2000 John Wiley & Sons, Ltd.

Multi‐input, multi‐output flight control system design for the YF‐16 using nonlinear QFT and pilot compensation

AbstractNonlinear quantitative feedback theory (QFT) and pilot compensation techniques are used to design a 2 × 2 flight control system for the YF‐16 aircraft over a large range of plant uncertainty. The design is based on numerical input‐output time histories generated with a FORTRAN implemented nonlinear simulation of the YF‐16. The first step of the design process is the generation of a set of equivalent linear time‐invariant (LTI) plant models to represent the actual nonlinear plant. It has been proven that the solution to the equivalent plant problem is guaranteed to solve the original nonlinear problem. Standard QFT techniques are then used in the design synthesis based on the equivalent plant models. A detailed mathematical development of the method used to develop these equivalent LTI plant models is provided. After this inner‐loop design, pilot compensation is developed to reduce the pilot's workload. This outer‐loop design is also based on a set of equivalent LTI plant models. This is accomplished by modelling the pilot with parameters that result in good handling qualities ratings, and developing the necessary compensation to force the desired system responses.