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17 篇 会议

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New approach for the design of dynamic optimal control systems with model-reality differences

New approach for the design of dynamic optimal control syste...

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computing and control division colloquium on identification of uncertain systems

作者： Roberts, P.D. City Univ London United Kingdom

The paper addresses the specific problem of designing nonlinear dynamic optimal control systems, where the model employed to compute the optimal solution is different from reality. A mathematical model, which is an essential constituent of a control system design technique, inevitably is an approximation of the real technological process that it represents. These model - reality differences may be introduced deliberately, in order to facilitate the mathematical aspects of the design and analysis, or arise due to uncertainties in knowledge of process mathematical structure and parameters. The challenge to the control designer is to achieve a control system that is robust to model uncertainties and to try to obtain a suitable design in spite of model reality differences. An approach known as Integrated System Optimization and Parameter Estimation (ISOPE), inspired by Haimes and Wismer (1972), was developed by Roberts (1979) and Roberts and Williams (1981) for on - line steady - state optimization of industrial processes implemented through adjustment of regulatory controller set-points. The method is iterative in nature using repeated solutions of optimization and estimation of parameters within the model used for calculating the optimum. An important property of the ISOPE technique is that the iterations converge to the correct real optimum in spite of the model-reality differences. This paper describes recent research (Roberts, 1993) on the extension of the ISOPE principle to develop an iterative procedure for solving dynamic optimal control problems which specifically takes in to account that the model used in the computations is different, in structure and parameters, from reality. The work is a further development of Dynamic Integrated System Optimization and Parameter Estimation, DISOPE, first introduced by Roberts (1992), in which the convergence behaviour is improved through convexification, and an implementable algorithm based on a linear model and quadratic

关键词： control system synthesis

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Advanced control of a polymerisation reactor

Advanced control of a polymerisation reactor

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computing and control division colloquium on Advances in control in the Process Industries: an Exercise in Technology Transfer

作者： Lightbody, G. Irwin, G.W. Taylor, A. Kelly, K. McCormick, J. Queen's Univ of Belfast Belfast United Kingdom

The use of model-based approaches is proposed to improve the control performance of an industrial polymerization reactor. This involves the development of a process model using system identification techniques, the simulation of the plant within the Simulink environment to allow for the design and validation of control strategies. From these studies a Smith Predictor was implemented to significantly improve the polymer viscosity control. A hardware platform is developed to facilitate the implementation of sophisticated algorithms such as recursive least squares, that could not be accommodated on the existing DCS.

关键词： Process control

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On-line adaptive control of the hot rolling of steel sections

On-line adaptive control of the hot rolling of steel section...

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IEE computing and control division colloquium on Adaptive controllers in Practice - Part One

作者： Stephens, Richard I. Manning, Mark P. Dides, Alex G. CEGELEC Projects Ltd Rugby United Kingdom

A multi-loop control scheme for a state-of-the-art section mill is described. The basis of the system is the set-up calculation which uses complex, non-linear models of the rolling process to predict the optimum settings for the mill stands. To obtain maximum performance from the system, adaptive control loops are included on three levels: piece-by-piece, pass-by-pass and in-pass. These loops use various techniques for identification of piece state and model parameters, including square root information filtering and extended Kalman filtering. The paper describes some of the problems of applying these methods to an online system and shows how these problems are addressed in practice.

关键词： Adaptive control systems

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Worst case estimation under model uncertainty

Worst case estimation under model uncertainty

引用

IEE colloquium on identification of uncertain systems

作者： I.D. Grobov M.P. Spathopoulos Division Dynamics and Control University of Strathclyde Glasgow UK

In system identification a dynamical model of a process is identified using measurement data. The uncertainties in the system parameters and the observation noise are described usually by stochastic mechanisms. However there are many situations where the main contribution to the error is not of a random nature and therefore cannot be suitably described by random noise. Thus statistical methods are not always appropriate for system modelling and identification. A theory, which assumes that there is no statistical description for the measurement noise or for the disturbances in the system, has been developed named theory of guaranteed identification or set-membership description of uncertainty or theory of difference inclusions. A considerable number of applications in engineering and systems analysis are treated under informational assumptions that justify this approach. In this paper we address the problem of deriving bounding parameter sets of state-space systems in the presence of bounded, uncontrolled but nonstochastic disturbances.< >

关键词： identification State space methods uncertain systems

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On the moment-based robust MPC formulations

On the moment-based robust MPC formulations

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computing and systems Technology division 2016 - Core Programming Area at the 2016 AIChE Annual Meeting

作者： Saltik, Muhammed B. Özkan, Leyla Ludlage, Jobert H.A. Weiland, Siep Van Den Hof, Paul M.J.

ISBN: (纸本)9781510834323

The control technique commonly used in process industry for constrained multivariable optimal control is the model predictive control (MPC) [1]. The performance of these controllers depends to a large extent on the quality/validity of process models ([2]). In the mean time, more and more rigorous process models that cover wider operating conditions have become available. A valid hypothesis is then the use of this type of models for MPC. The practice, on the other hand, is conflicting since the model based estimation and control algorithms require relatively simple explicit models for efficient and robust calculations in their algorithms. This practical drawback has led to research directions in the area of model simplifications ([3] and references therein) or multiple model MPC approaches ([4]). In addition to the plant-model mismatch as a result of inaccurate identification of model parameters and its dynamics, such simplifications introduce uncertain behavior between the observations induced from the true process and the model predictions. In order to limit the effect of uncertainty on the closed loop performance, robust control techniques are required. In the last couple of decades, different MPC algorithms have been introduced to achieve the desired control objectives while reducing the effect of uncertainty ([5]). We can broadly classify these algorithms into two classes;i) the worst case based techniques (WC-MPC) ([6]), including min-max based, tube based or input-to-state stability based arguments, where under any effect of predefined maximum level of uncertainty, the process variables should not violate the specifications;or ii) chance based MPC algorithms ([7]), including quantile based or scenario based formulations, where the specifications are softened, meaning that the constraints are allowed to be violated, but the chance of being off the specification is kept small, thus violations are rare events. Both of these formulations have their own drawbacks.

关键词： Model predictive control

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Proceedings - 14th International Symposium on Pervasive systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative computing, ISCC 2017

Proceedings - 14th International Symposium on Pervasive Syst...

引用

14th International Symposium on Pervasive systems, Algorithms and Networks, I-SPAN 2017, 11th International Conference on Frontier of Computer Science and Technology, FCST 2017 and 3rd International Symposium of Creative computing, ISCC 2017

ISBN: (纸本)9781538608401

The proceedings contain 176 papers. The topics discussed include: lessons learned in design and delivery of an introductory styling MOOC;machine learning approach to task ranking;coercion resistance in a practical secret voting scheme for large scale elections;an evaluation method of data valuation based on analytic hierarchy process;more on the inverse problem of Steiner Wiener index;probabilistic frequent itemsets mining based on expectation bound over uncertain database;on detecting partially occluded faces with pose variations;grid-based indexing and search algorithms for large-scale and high-dimensional data;image segmentation based on exponential kernel function;a personnel identification method based on the regional division of a classroom image;accurate camera calibration using iteratively optimized algorithm of control points;and a novel fish image retrieval method based on saliency spatial pyramid.

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Managing in an uncertain world: risk analysis and the bottom line

Managing in an uncertain world: risk analysis and the bottom...

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IEE colloquium on systems Engineering Contribution to Increased Profitability

作者： I. Rowley Management Sciences Division PA Consulting Group UK

Business and Government must act positively in an increasingly risky environment. In particular, when making a decision, it is important to consider whether its prospective benefits justify the risk of failure. Risk analysis can contribute to answering this question, so helping to ensure that an organisation takes only the right risks and gains control of its overall exposure to risk. This paper reviews some of the ways in which risk analysis can aid the decision maker in assessing and managing risk. Topics covered include the identification of risk areas, approaches to risk assessment, strategies for risk reduction and control, and the limitations of risk analysis. As a practical example of how risk analysis can be applied, an approach to the risk screening of projects is described. This is based on a method developed by the PA Consulting Group to assess the level of risk it takes on when tendering for consultancy work; the higher the perceived risk, the more scrutiny given before a proposal can be approved for submission. The benefits of this approach are discussed.< >

关键词： Decision-making Management

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