Guidelines for specifying design parameters for minimum crest factor multisine signals per the approach of Guillaume et al. are presented. These guidelines are evaluated for the identification of nonlinear process sys...
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Guidelines for specifying design parameters for minimum crest factor multisine signals per the approach of Guillaume et al. are presented. These guidelines are evaluated for the identification of nonlinear process systems. The minimum crest factor multisine signals offer some distinct advantages over both Schroeder phased multisine signals and m-level Pseudo-Random Sequence (m-level PRS) signals with respect to "plant-friendliness" considerations. These signals can be used to reduce the effects of nonlinearity in obtaining an empirical transfer function estimate (ETFE). As an example, the ETFE of a Rapid Thermal Processing (RTP) reactor simulation is constructed. "Plant-friendly" issues are also discussed and illustrated in the identification and control of a CSTR simulation via "Model-on-Demand" estimation. This provides a compelling example, since the "Model-on-Demand" estimator is a data-driven nonlinear identification approach.
作者：
D.E. RiveraM.E. FloresDepartment of Chemical
Bio and Materials Engineering and Control Systems Engineering Laboratory Manufacturing Institute Arizona State University Tempe Arizona 85287-6006 phone:(480)-965-9476
This paper describes efforts at Arizona State University to introduce substantive topics in system identification to undergraduate chemicalengineering students. Specifically, the paper focuses on how system identific...
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This paper describes efforts at Arizona State University to introduce substantive topics in system identification to undergraduate chemicalengineering students. Specifically, the paper focuses on how system identification issues relevant to industrial practice have been incorporated into a simulated gas-oil furnace experiment that is part of a senior-level process dynamics and control course (ChE 461).
An experimented study of "Model-on-Demand" (MoD) identification is made on a pilot-scale brine-water mixing tank. MoD estimation is compared against semi-physical modeling techniques using identification dat...
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An experimented study of "Model-on-Demand" (MoD) identification is made on a pilot-scale brine-water mixing tank. MoD estimation is compared against semi-physical modeling techniques using identification data generated from a systematically designed m-level Pseudo Random Sequence (PRS) input. The estimated models are the basis for evaluating the usefulness of MoD-based Model Predictive control (MPC). For this application, MoD-MPC is shown to provide better performance at high bandwidths compared to a linear MPC controller.
The comparison of the performance of two artificial neural network or ANN paradigms trained to learn data obtained from the kinematics model of a UMI RTX robotic arm are presented. Trained ANN simulators were implemen...
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The comparison of the performance of two artificial neural network or ANN paradigms trained to learn data obtained from the kinematics model of a UMI RTX robotic arm are presented. Trained ANN simulators were implemented to position the robotic manipulator demonstrating the feasibility of using ANN technology in actual implementation.
This paper describes pIDtuneTM; a MATLAB-based package that integrates system identification and PID controller design. The program addresses the PID tuning needs commonly expressed by control engineers in the process...
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This paper describes pIDtuneTM; a MATLAB-based package that integrates system identification and PID controller design. The program addresses the PID tuning needs commonly expressed by control engineers in the process industries. The package relies on ARX estimation and control-relevant model reduction to obtain models consistent with the Internal Model control (IMC) PID tuning rules. Furthermore, the package allows the user to simulate closed-loop behavior and provides analysis tools for assessing the benefits of choosing particular tuning parameters for setpoint tracking and load disturbances, with or without uncertainty.
A control oriented hybrid model structure combining first principles models with standard black-box techniques for modelling nonlinear dynamics of reaction systems is presented in this paper. The approach is formulate...
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A control oriented hybrid model structure combining first principles models with standard black-box techniques for modelling nonlinear dynamics of reaction systems is presented in this paper. The approach is formulated in a general framework for continuous stirred tank reactors and analyzed in details through a case study of a reactive distillation column. The approach is based on easily established mass balance equations, the stoichiometry of the system as well as model reduction techniques. The choice of combined inputs and the model structure is motivated by some general control objectives for this class of systems. A progressive identification of this model structure can be performed when a dominant part exists. The application to real process data is presented. This model structure has been successfully used in an IMC scheme for an industrial reactive distillation column.
As several problems arise at the application of adaptive controllers to real industrial processes, for a successful implementation the standard control algorithms have to be combined with heuristically derived safety ...
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As several problems arise at the application of adaptive controllers to real industrial processes, for a successful implementation the standard control algorithms have to be combined with heuristically derived safety jackets. These safety nets are often realized in expert systems. This paper presents such an intelligent supervision system, where the heuristic knowledge is represented by fuzzy rules. The fuzzy system designed for adaptive control of a simulated fermenter supervises the semi-continuous identification and the controller tuning procedure. Simulation results show that the application of the proposed algorithm results in a robust and good control performance.
The design and the performances of a hybrid model based control of an industrial reactive distillation column are presented. The model structure is a combination of first principles with standard black-box techniques....
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The design and the performances of a hybrid model based control of an industrial reactive distillation column are presented. The model structure is a combination of first principles with standard black-box techniques. The approach is based on easily established mass balance equations, the stoichiometry of the system and a model reduction from fast system dynamics. This model makes use of a combined physically meaningful variable from several physical inputs of the process. It is also a key variable for the optimization and the control of the underlying process. This model structure has been successfully used in an IMC scheme for the on-line control of this process.
This paper describes the robust control design of a 1.5 MW free turbine with complex load. A non-linear model of the engine with load has been developed within Simulink from details previously presented. State space H...
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An adaptive model predictive controller is applied to the optimization and control of reentrant semiconductor manufacturing lines. It is implemented within a three-layer hierarchical structure. At the top layer the pa...
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An adaptive model predictive controller is applied to the optimization and control of reentrant semiconductor manufacturing lines. It is implemented within a three-layer hierarchical structure. At the top layer the parameters of an aggregated model are obtained online while at the intermediate layer production optimization and inventory control are performed using these parameters. The bottom layer consists of a discrete event "follow-up" controller which tracks the targets issued by the optimizer. This controller is applied to a discrete-event semiconductor manufacturing line problem whose specifications were developed by Intel Corp.
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