Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header

An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications.

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Improving adaptive subtraction in seismic multiple attenuation

Geophysics ◽

10.1190/1.3122408 ◽

2009 ◽

Vol 74 (4) ◽

pp. V59-V67 ◽

Cited By ~ 12

Author(s):

Shoudong Huo ◽

Yanghua Wang

Keyword(s):

Least Squares ◽

Seismic Data ◽

Primary Energy ◽

Multiple Models ◽

Multiple Model ◽

Data Set ◽

Multiple Attenuation ◽

Matching Filter ◽

Theoretical Analyses ◽

Different Order

In seismic multiple attenuation, once the multiple models have been built, the effectiveness of the processing depends on the subtraction step. Usually the primary energy is partially attenuated during the adaptive subtraction if an [Formula: see text]-norm matching filter is used to solve a least-squares problem. The expanded multichannel matching (EMCM) filter generally is effective, but conservative parameters adopted to preserve the primary could lead to some remaining multiples. We have managed to improve the multiple attenuation result through an iterative application of the EMCM filter to accumulate the effect of subtraction. A Butterworth-type masking filter based on the multiple model can be used to preserve most of the primary energy prior to subtraction, and then subtraction can be performed on the remaining part to better suppress the multiples without affecting the primaries. Meanwhile, subtraction can be performed according to the orders of the multiples, as a single subtraction window usually covers different-order multiples with different amplitudes. Theoretical analyses, and synthetic and real seismic data set demonstrations, proved that a combination of these three strategies is effective in improving the adaptive subtraction during seismic multiple attenuation.

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A constrained recursive least squares algorithm for adaptive combination of multiple models

2015 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2015.7280298 ◽

2015 ◽

Cited By ~ 1

Author(s):

Xia Hong ◽

Yu Gong

Keyword(s):

Least Squares ◽

Recursive Least Squares ◽

Multiple Models ◽

Adaptive Combination ◽

Recursive Least Squares Algorithm ◽

Least Squares Algorithm

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Self-Tuning Predictive Control of Nonlinear Servo-Motor

Journal of Electrical Engineering ◽

10.2478/v10187-010-0056-x ◽

2010 ◽

Vol 61 (6) ◽

pp. 365-372 ◽

Cited By ~ 7

Author(s):

Vladimír Bobál ◽

Petr Chalupa ◽

Marek Kubalčík ◽

Petr Dostál

Keyword(s):

Predictive Control ◽

Control Algorithm ◽

Least Squares Method ◽

Recursive Least Squares ◽

Laboratory Model ◽

Servo Motor ◽

Real Time Control ◽

Highly Nonlinear ◽

Model Control ◽

Self Tuning

Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model — Amira DR300.

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Multiple Models Predictive Control of Once-Through Steam Generator

Volume 3: Thermal Hydraulics; Instrumentation and Controls ◽

10.1115/icone16-48641 ◽

2008 ◽

Author(s):

Yun Tai ◽

Su-Xia Hou ◽

Fu-Yu Zhao

Keyword(s):

Steam Generator ◽

Predictive Control ◽

Good Control ◽

Multiple Models ◽

Mathematics Model ◽

System Control ◽

Multiple Model ◽

Nonlinear System Control ◽

Predictive Controller ◽

Output System

Because of multiphase flow during the heat transfer, OTSG (Once-Through Steam Generator) is a complex nonlinear MIMO (Multiple Input and Multiple Output) system. This article sets the mathematics model of OTSG with internal screw and double tubes, uses the FOROTSG program to simulate OTSG, and identifies the system models of different power levels. Based on this, Multiple Model Predictive Control (MMPC) strategy is proposed, which designs the Model Predictive Controller of each model identified, and then integrates the multiple models by Membership Function Law, to achieve smoothly switch of the multiple models at last. The simulating result indicates the MMPC has the good control effects, and it is an available strategy to solve the problems of the nonlinear system control.

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Fault Detection and Control of Process Systems

Mathematical Problems in Engineering ◽

10.1155/2007/80321 ◽

2007 ◽

Vol 2007 ◽

pp. 1-20 ◽

Cited By ~ 10

Author(s):

Vu Trieu Minh ◽

Nitin Afzulpurkar ◽

W. M. Wan Muhamad

Keyword(s):

Control System ◽

Fault Detection ◽

Predictive Control ◽

Detection System ◽

Control Process ◽

Multiple Models ◽

Interacting Multiple Model ◽

Multiple Model ◽

Process Systems ◽

And Control

This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.

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Identification and Control of Nonlinear Laboratory Model Amira DR 300

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.611.284 ◽

2014 ◽

Vol 611 ◽

pp. 284-293

Author(s):

Petr Navrátil

Keyword(s):

Least Squares ◽

Recursive Least Squares ◽

Laboratory Model ◽

Adaptive Controllers ◽

Real Model ◽

Model Control ◽

Simulation Testing ◽

Self Tuning ◽

Adaptive Forgetting ◽

And Control

This article deals with identification and a control design of nonlinear laboratory model Amira DR 300 in a real time. It mentions a mathematical description of the model, its static and dynamical characteristics. Using an experimental method of identification a model has been created, which is suitable for the purpose of simulation testing of non-adaptive as well as adaptive controllers. These were tested as simple adaptive, i.e. non-adaptive controllers, namely in both simulation ways and at a real model control. For the purpose of a non-adaptive control, system parameters were determined by off-line method of the least squares. In an identification part of self-tuning controllers a method of recursive least squares with directional adaptive forgetting factor has been used.

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