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15th IFAC Symposium on System Identification, SYSID 2009

作者： Schwartz, Jay D. Rivera, Daniel E. Control Systems Engineering Laboratory Department of Chemical Engineering Arizona State University Tempe AZ 85287-6006 United States

ISBN: (纸本)9783902661470

Efficient supply chain management is a crucial imperative for modern, global enterprises. Tactical decision policies based on process control principles have been developed in the literature for managing production-inventory systems and supply chain networks. To be effective these decision policies depend on accurate nominal models. With a discreteevent simulation acting as a "truth model", we employ system identification techniques to parameterize a nonlinear Partial Differential Equation (PDE) model of the semiconductor manufacturing process. A case study shows that the identified PDE model can accurately predict the output of the discrete-event simulation, but without the high computational burden. © 2009 IFAC.

关键词： Discrete event simulation

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Optimization of proof test policies for safety instrumented systems using multi-objective genetic algorithms

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作者： Torres-Echeverria, A.C. Martorell, S. Thompson, H.A. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Department of Chemical and Nuclear Engineering Polytechnic University of Valencia Valencia Spain

ISBN: (纸本)9780415485135

This paper addresses the optimization of proof testing policies for safety instrumented systems with redmidant parallel architectures. Testing policies include both the test interval and the test strategy. The approach addresses the requirements of the standard IEC 61508. The Average Probability of Failure on Demand, a measure of system safety integrity, Spurious Trip Rate and Lifecycle Cost are the three objectives used for the optimization. This gives as a resuh optimally balanced testing policies that consider the main aspects for decision making about safety system implementation: safety, reliability and cost. A new model is used for quantification of the time dependent PFD, capable of modelling explicitly different test intervals and different test strategies. It also approaches the issues of common cause failure and diagnostic coverage. The optimization is implemented using tíie NSGA-II genetic algorithm. The method is illustrated through an application case of a system implementing a protective fimction on a chemical reactor. ©2009 Taylor & Francis Group.

关键词： Safety testing

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Modelling test strategies effects on the probability of failure on demand for safety instrumented systems

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ISBN: (纸本)9780415485135

This paper presents a new approximation for modelling the time dependent Probability of Failure on Demand (PFD) for parallel redundant safety systems, focussed on the analysis of the impact that different test strategies have on the system dependability. The model permits to evaluate explicitly the effects of simultaneous, sequential and staggered test strategies, diverse common cause failure rates and diagnostic coverage. It has been developed based on the different evaluation intervals that a module goes periodically through its time in service: test, repair and time between tests. It includes quantification of both detected and undetected failures, and puts special emphasis on the quantification of the contribution of the common cause failure to the system PFD as an additional component. The effectiveness of the model is demonstrated with a practical application case. Additionally, the impact of different common cause failure rates in the system and different diagnostic coverage in the components is analyzed through a sensitivity analysis. © 2009 Taylor & Francis Group.

关键词： Failure analysis

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State estimation and fault tolerant nonlinear predictive control of an autonomous hybrid system using unscented kalman filter

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作者： Prakash, J. Deshpande, Anjali P. Patwardhan, Sachin C. MIT Campus Anna University Chennai India Systems and Control Engineering Indian Institute of Technology Bombay Mumbai India Dept. of Chemical Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9783642010934

In this work, we propose a novel fault tolerant nonlinear model predictive control (FTNMPC) scheme for dealing with control problems associated with an autonomous nonlinear hybrid system (NHS). To begin with, we develop a scheme for state estimation of continuous as well as discrete states for autonomous NHS using unscented Kalman filter (UKF), a derivative free nonlinear state estimator, and further use it for formulating an NMPC scheme. The salient feature of the NMPC scheme is that the concept of sigma point propagation in UKF is extended to carry out the future trajectory predictions. We then proceed to develop a nonlinear version of generalized likelihood ratio (GLR) method that employs UKF for diagnosing sensor and/or actuator faults. The diagnostic information generated by the nonlinear GLR method is used for on-line correction of the measurement vector, the model used for state estimation/prediction and constraints in the NMPC formulation. The efficacy of the proposed state estimation, diagnosis and control schemes is demonstrated by conducting simulation studies on the benchmark three-tank hybrid system. Analysis of the simulation results reveals that the FTNMPC scheme facilitates significant recovery in the closed loop performance particularly on occurrence of sensor faults. © 2009 Springer Berlin Heidelberg.

关键词： Hybrid systems

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A System Identification Approach to PDE Modeling of a Semiconductor Manufacturing Process

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IFAC Proceedings Volumes 2009年第10期42卷 964-969页

作者： Jay D. Schwartz Daniel E. Rivera Control Systems Engineering Laboratory Department of Chemical Engineering Arizona State University Tempe AZ 85287-6006 USA

Efficient supply chain management is a crucial imperative for modern, global enterprises. Tactical decision policies based on process control principles have been developed in the literature for managing production-inventory systems and supply chain networks. To be effective these decision policies depend on accurate nominal models. With a discrete-event simulation acting as a “truth model”, we employ system identification techniques to parameterize a nonlinear Partial Differential Equation (PDE) model of the semiconductor manufacturing process. A case study shows that the identified PDE model can accurately predict the output of the discrete-event simulation, but without the high computational burden.

关键词： System Identification Discrete Event Simulation Semiconductor Manufacturing Simultaneous Perturbation Stochastic Approximation

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Multi-step prediction error approach for MPC performance monitoring

Multi-step prediction error approach for MPC performance mon...

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作者： Zhao, Yu Chu, Jian Su, Hongye Huang, Biao State Key Lab. of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China Department of Chemical and Materials Engineering University of Alberta Edmonton AB T6G 2G6 Canada

ISBN: (纸本)9783902661548

Performance monitoring of model predictive control systems (MPC) has received a great interest from both academia and industry. In recent years some novel approaches for multivariate control performance monitoring have been developed without the requirement of process models or interactor matrices. Among them the prediction error approach has been shown to be a promising one, but it is k-step prediction based and may not be fully comparable with the MPC objective that is multi-step prediction based. This paper develops a multi-step prediction error approach for performance monitoring of model predictive control systems, and demonstrates its application in an industrial MPC performance monitoring and diagnosis problem.

关键词： Model predictive control

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Plant-Friendly Signal Generation for System Identification using a Modified SPSA Methodology

Plant-Friendly Signal Generation for System Identification u...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Richard Steenis Daniel E.Rivera the Department of Electrical Engineering Arizona State University the Control Systems Engineering Laboratory School of MechanicalAerospaceChemical and Materials EngineeringArizona State University

The Simultaneous Perturbation Stochastic Approximation(SPSA) methodology and a modified SPSA version(MSPSA-K) were investigated for the generation of plantfriendly multi-sinusoidal *** MSPSA-K methodology principally differs from SPSA in that it perturbs the signal phase parameters in K subsets rather than *** and MSPSA-K provide a flexible,extensible computational framework to incorporate various plant-friendly performance measures into the design of an input signal for experimental design purposes in system *** this paper an objective function comprised of the input signal crest factor,rate of change,acceleration,and output crest factor is presented and applied to a representative case study.A detailed analysis of the tradeoffs between these various performance measures is illustrated by the choice of weighting of the objective function *** proposed method can be applied to signals with an arbitrarily-defined spectrum(in both amplitude and frequency spacing) and is easily implemented.

关键词： Signal Generation Plant-Friendly MultiSinusoidal Signal System Identification Simultaneous Perturbation Stochastic Approximation

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A method to coordination for decentralized multi- MPC controllers in integrated plant

A method to coordination for decentralized multi- MPC contro...

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IEEE International Conference on Automation and Logistics

作者： Aimin An Xiaohong Hao Hongye Su Guangtao Yang Yongwei Ma Institute of Electrical Engineering and Information Engineering Lanzhou University of Technology China Institute of Cyber-Systems and Control University of Zhejiang China Department of Electric Control Company of Engineering Lanzhou Petroleum Chemical Engineering Company China

In this paper, a method to coordinate multi-decentralized model predictive controllers(DMMPC), which is used as a strategy to control the integrated plant through decomposing the total system model into different functional sub-models, is proposed. Usually, an integrated plant is also called a large scale systems. The main aim is to relieve the expensive computation load and for improving the flexibility and reliability for operation. How to control and coordinate the possible dynamic coupling and constraints among subsystems has already become an important problem. In order to accomplish the overall objective, coordinating the relationship between optimizer located in upper layer and multi-decentralized MPC controllers located in down layer is a challenging work. A stochastic probability density functions(PDFs) coordination rule is applied to coordinate optimizer with decentralized MPC controllers in the hierarchical control structure. This approach provides the guarantee that local optimal solutions for dominant controlled variables approximate their global optimal solutions under considering uncertainties and interactions among subunits on closed-loop stability condition. As a study case, an integrated plant which consists of two subunits connected via other process and some intermediate tanks under severe constraints while considering maximization economic performance is used to demonstrate feasibility and efficiency of this framework.

关键词： Predictive models Optimal control Power generation economics Economic forecasting Environmental economics Industrial control Industrial relations Centralized control Industrial economics Production

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control-Relevant Estimation of Demand Models for Closed-Loop control of a Production-Inventory System

Control-Relevant Estimation of Demand Models for Closed-Loop...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Jay D.Schwartz Daniel E.Rivera the Department of Chemical Engineering Arizona State University the Digital Enterprise Group Decision Engineering Team Intel Corporation the Control Systems Engineering Laboratory School of MechanicalAerospaceChemical and Materials EngineeringArizona State University

The development of control-oriented decision policies for inventory management in supply chains has received considerable interest in recent years, and demand modeling to supply forecasts for these policies is an important component of an effective solution to this problem. Drawing from the problem of control-relevant parameter estimation, this paper presents an approach for demand modeling in a productioninventory system that relies on a control-relevant weight to tailor the emphasis of the fit to the intended purpose of the model, which is to provide forecast signals tactical inventory management policies based on Internal Model control. The formulation is multi-objective in nature, allowing the user to emphasize inventory variation, starts change variation, or a weighted combination. By integrating the demand modeling and inventory control problems, it is possible to obtain reducedorder demand models that exhibit superior performance. A systematic approach for generating these weights is presented and the benefits resulting from their use demonstrated on a representative production-inventory system case study.

关键词： demand modeling control-relevance Internal Model control production control supply chain management

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A Backstepping Approach to Multivariable Robust Constraint Satisfaction With Application to a VTOL Helicopter

A Backstepping Approach to Multivariable Robust Constraint S...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Mathias Brger Martin Guay the Institute for Systems Theory and Automatic Control University of Stuttgart the Department of Chemical Engineering Queen's University

A recursive procedure is proposed to reduce a set of inequality constraints of high relative order to single constraints of relative order one for a class of uncertain,multi-input nonlinear systems.A modification of a nominal control signal on the newly constructed constraints guarantees robust constraint *** resulting robust invariant controller is applied to the constrained flight and the obstacle avoidance problem of a VTOL helicopter.

关键词： A Backstepping Approach to Multivariable Robust Constraint Satisfaction With Application to a VTOL Helicopter

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