This paper presents the design and implementation of an active control mechanism for noise cancellation and vibration suppression within an adaptive control framework. A control mechanism is designed within a feedforw...
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This paper presents the design and implementation of an active control mechanism for noise cancellation and vibration suppression within an adaptive control framework. A control mechanism is designed within a feedforward control structure on the basis of optimum cancellation at an observation point. The design relations are formulated such that to allow on-line design and implementation and thus result in a self-tuning control algorithm. The algorithm is implemented on an integrated digital signal processing (DSP) and transputer system. Simulation results verifying the performance of the algorithm are presented and discussed. (C) 1996 Academic Press Limited
An adaptive controller with improved performance characteristics is introduced. The proposed controller extends recent results in this area since it achieves performance improvement of the zero-state output error in t...
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An adaptive controller with improved performance characteristics is introduced. The proposed controller extends recent results in this area since it achieves performance improvement of the zero-state output error in the presence of some uncertainty on the high-frequency gain of the plant.
There have been numerous methods for learning and predicting time series ranging from the traditional time-series analyses to recent approaches using neural networks. A central issue common to all of them is the deter...
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There have been numerous methods for learning and predicting time series ranging from the traditional time-series analyses to recent approaches using neural networks. A central issue common to all of them is the determination of model structure. Both mean prediction error and An Information Criterion (AIC) are useful in model selection;the model with the smallest mean prediction error or AIC is selected from among a set of models as the best one. In this way they give a solution to the problem of model selection. Due to huge search space, however, the mean prediction error or AIC alone is not powerful enough to find the best model structure from among all the candidates. In the present paper the authors propose to use both a structural learning with forgetting and the mean prediction error or AIC to find a model with better generalization ability. Jordan networks and buffer networks, popular in the modeling of time series, are examined in this paper. The structural learning with forgetting and backpropagation (BP) learning are applied to compare the learning and prediction performance of these two types of models. Simulation results demonstrate that the structural learning with forgetting has better generalization ability than BP learning both in Jordan networks and buffer networks.
This paper presents an investigation into the utilisation of digital signal processing and parallel processing techniques for the real-time simulation of a flexible manipulator system. A finite dimensional simulation ...
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This paper presents an investigation into the utilisation of digital signal processing and parallel processing techniques for the real-time simulation of a flexible manipulator system. A finite dimensional simulation of the system is developed using a finite difference approximation to the governing dynamic equation of the manipulator. The proposed algorithm allows dynamic modification of the boundary conditions and the inclusion of a distributed actuator and sensor term in the system dynamic equation. The algorithm developed is implemented on a number of uni-processor and multi-processor, homogeneous and heterogeneous parallel architectures. The partitioning and mapping of the algorithm on the homogeneous and heterogeneous architectures is also explored. A comparison of the results of these implementations is made and discussed to establish merits of design and real-time processing requirements in the control of flexible manipulator systems. (C) 1996 Academic Press Limited
A prototype concurrent engineering tool has been developed for the preliminary design of composite topside structures for modern navy warships. This tool, named GELS for the Concurrent engineering of Layered Structure...
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A prototype concurrent engineering tool has been developed for the preliminary design of composite topside structures for modern navy warships. This tool, named GELS for the Concurrent engineering of Layered Structures, provides designers with an immediate assessment of the impacts of their decisions on several disciplines which are important to the performance of a modern naval topside structure, including electromagnetic interference effects (EMI), radar cross section (RCS), structural integrity, cost, and weight. Preliminary analysis modules in each of these disciplines are integrated to operate from a common set of design variables and a common materials database. Performance in each discipline and an overall fitness function for the concept are then evaluated. A graphical user interface (GUI) is used to define requirements and to display the results from the technical analysis modules. Optimization techniques, including feasible sequential quadratic programming (FSQP) and exhaustive search are used to modify the design variables to satisfy all requirements simultaneously. The development of this tool, the technical modules, and their integration are discussed noting the decisions and compromises required to develop and integrate the modules into a prototype conceptual design tool.
This paper focuses on the two general approaches being investigated for condition monitoring systems: static pattern analysis approach and the dynamical systems approach. In each, statistical and neural network method...
This paper focuses on the two general approaches being investigated for condition monitoring systems: static pattern analysis approach and the dynamical systems approach. In each, statistical and neural network methods are used. The dynamical systems approach lends itself to model-based condition monitoring systems. The performances of the different methods for the monitoring of the complex aircraft gas turbine engine is described, based on real engine data.
作者：
Wei-Ming LingDaniel E. RiveraDepartment of Chemical
Bio and Materials Engineering and Control Systems Engineering Laboratory Computer-Integrated Manufacturing Systems Research Center Arizona State University Tempe AZ 85287-6006
A two-step nonlinear system identification method using restricted complexity models (RCM) is proposed. In the first step, a parsimonious yet full order Volterra model is identified using the orthogonal least squares ...
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A two-step nonlinear system identification method using restricted complexity models (RCM) is proposed. In the first step, a parsimonious yet full order Volterra model is identified using the orthogonal least squares method. In the second step, using a control relevant approach, the full order model is further reduced to a restricted complexity model which is more amenable to control design and analysis. The minimization problem in the model reduction step is posed such that it can be solved using general optimization routines. A corresponding two-step model validation procedure is implemented to ensure the closed-loop performance of the resulting model. Effectiveness of the proposed method is illustrated by a polymerization reactor example.
Load forecasting in power systems is an important subject and has been studied from different points of view in order to achieve better load forecasting results. This paper addresses one of the challenges in spatial l...
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ISBN:
(纸本)0780335228
Load forecasting in power systems is an important subject and has been studied from different points of view in order to achieve better load forecasting results. This paper addresses one of the challenges in spatial load forecasting area, urban re-development, and presents a theory and methodology to incorporate urban re-development into spatial load forecasting considerations.
A fundamental limitation in achieving high performance control for multivariable plants is associated with the uncertainty in the modelling of the plant. Previous measures of robust stability uncertainty have typicall...
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A fundamental limitation in achieving high performance control for multivariable plants is associated with the uncertainty in the modelling of the plant. Previous measures of robust stability uncertainty have typically been based on complex plant perturbations, which often lead to excessively conservative controller design. It is the purpose of this paper to propose a nonconservative measure of plant model uncertainty; this is done by utilizing results obtained on the real stability radius problem. A CAD approach to synthesize controllers for a plant which has a specified degree of plant model uncertainty is then proposed. Some application studies of the procedure are included.
The regulation problem of linear discrete-time systems under state and control constraints is investigated. In the first part of the paper necessary and sufficient conditions for the existence of a solution to the con...
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The regulation problem of linear discrete-time systems under state and control constraints is investigated. In the first part of the paper necessary and sufficient conditions for the existence of a solution to the constrained control problem are established. The constructive form of the proof of this result provides also a method for the derivation of a control law transferring to the origin any state belonging to a given set of initial states while respecting the state and control constraints. Then a design technique for the determination of a solution to the constrained control problem is developed. The proposed technique is based on the reduction of the constrained control problem to simple linear programming problems.
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