This paper describes the robust control design of a gas generator engine. A nonlinear model of the engine has been developed within Simulink from details previously presented by Hill (1987). State space H/sub /spl inf...
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This paper describes the robust control design of a gas generator engine. A nonlinear model of the engine has been developed within Simulink from details previously presented by Hill (1987). State space H/sub /spl infin// control designs are performed using a linearised model to represent the key components in the single loop control configuration. The performance criteria are specified in terms of stability margins, bandwidth and desired response of the engine to large step input. The engine is subject to constraints on its manipulated variable (i.e. the throttle valve angle) which cause integral windup. The H/sub /spl infin// design is simplified to a classical PI controller. A technique so-called utilise saturation feedback is used to reduce the effect of the integral windup. The results show that the PI control produces results similar to H/sub /spl infin// at low speeds but that H/sub /spl infin// gives better robustness and performance at higher speeds. Nonlinear simulations with parameter changes support the conclusion that the design is robust.
We investigate herein the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise. A relatively large number of amplitude estimators are described which encompass Least Squar...
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Describes a graduate student laboratory experiment called "MARTS" (multivariable apparatus for real time control studies), which has been in use at the systemscontrol Group, University of Toronto since 1985...
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ISBN:
(纸本)0780352513
Describes a graduate student laboratory experiment called "MARTS" (multivariable apparatus for real time control studies), which has been in use at the systemscontrol Group, University of Toronto since 1985. The laboratory experiment is unique in that it uses industrial commercial process control sensors/actuators for its hardware, and thus when problems arise in controller implementation on MARTS, students cannot invoke the usual excuse that inadequate or faulty university sensor/actuator equipment is to blame. The paper describes the experimental apparatus, the control problem specifications to be carried out on the apparatus, mathematical modelling of the system, typical control experiments which have been carried out on the system using both conventional and unconventional controllers, and features which make the control laboratory experiment challenging.
The focus of the paper is directed towards the control of decentralized continuous systems using generalized sampled-data hold functions (GSHF). A digital control law together with a GSHF forms a time-varying controll...
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The focus of the paper is directed towards the control of decentralized continuous systems using generalized sampled-data hold functions (GSHF). A digital control law together with a GSHF forms a time-varying controller which can potentially outperform a digital controller with a simple zero-order hold. A class of decentralized continuous systems for which a GSHF controller can improve the performance of the resultant closed loop system is characterized.
Concerns decentralized adaptive switching control of decentralized LTI systems, using a family of controllers approach. Our objective is to design a high performance controller for a decentralized system, when the unc...
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Concerns decentralized adaptive switching control of decentralized LTI systems, using a family of controllers approach. Our objective is to design a high performance controller for a decentralized system, when the uncertainty in the plant model is sufficiently large so that a fixed decentralized controller is ineffective. It will be assumed that a set of previously designed high performance decentralized controllers is available for each input-output agent, and that the decentralized switching controller may "switch" from this set.
Repetitive, or multipass, processes are a class of 2D systems of both practical and algorithmic/theoretical interest whose dynamics cannot be analysed or controlled using standard (1D) systems theory. Recently it has ...
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A Markov-like weighted least squares (WLS) estimator is presented herein for harmonic sinusoidal parameter estimation. The estimator involves two distinct steps whereby it first obtains a set of initial parameter esti...
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There are a great many problems in generalizing classical 1D systems theory to multidimensional (nD) systems, i.e. systems which propagate information in two or more separate directions. Recent years have seen the eme...
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There are a great many problems in generalizing classical 1D systems theory to multidimensional (nD) systems, i.e. systems which propagate information in two or more separate directions. Recent years have seen the emergence of the so-called behavioural approach to systems theory which holds the promise of providing for the first time a framework in which all the important concepts of systems theory for nD systems can be expressed. This paper reports further development based on this general approach in the context of the pole/zero structure of an nD linear system.
The emergence of intelligent control has seen a focus of attention on the ideas of learning control. This paper explores the relationship between the performance of learning algorithms and the structure of the system ...
The emergence of intelligent control has seen a focus of attention on the ideas of learning control. This paper explores the relationship between the performance of learning algorithms and the structure of the system to be controlled. The importance of system's relative degree (pole-zero excess) and the system's zeros are described and the role of prediction in improving performance is demonstrated using ideas from iterative learning control.
A new learning algorithm suitable for pattern classification in machine condition health monitoring based on fuzzy neural networks called an "incremental learning fuzzy neuron network" (ILFN) has been develo...
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A new learning algorithm suitable for pattern classification in machine condition health monitoring based on fuzzy neural networks called an "incremental learning fuzzy neuron network" (ILFN) has been developed. The ILFN, using Gaussian neurons to represent the distributions of the input space, is an online one-pass incremental learning algorithm. The network is a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. To prove the concept the simulations have been performed with the vibration data known as Westland vibration data set. Furthermore, the classification performance of the network has been tested on other benchmark data sets, such as Fisher's iris data (1936) and a vowel data set. For the generalization capability, comparison studies among other well-known classifiers were performed and the ILFN was found competitive with or even superior to many existing classifiers. Additionally the ILFN uses far less training time than conventional classifiers.
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