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Workshop on Predictive control of Electrical Drives and Power Electronics (PRECEDE)

作者： Christian A. Rojas Samir Kouro Marcelo Perez Felipe Villarroel Department of Electronic Engineering Universidad Tecnica Federico Santa Maria Valparaiso chile CADETECH S.A. Concepcion Chile

Recently the use of Model Predictive control (MPC) in electrical motor drives has been reported both theoretically and experimentally. Predictive Torque control (PTC) has been developed to control induction motor drives, allowing high-performance and fast dynamics. However, the optimization used in PTC is based on a single cost function minimization, where control objectives are merged by using weighting factors. The adjustment of these factors is achieved through a nontrivial process and they are heavily dependent on the system parameters and user requirements, being a complex task in systems where two or three weighting factors must be adjusted. To avoid the well-known time-consuming simulations and branch and bound search process, a Multiobjective Fuzzy Predictive Torque control (FPTC) is presented for a Three-Level Neutral-Point-Clamped voltage source inverter (3L-NPC). The proposed strategy changes the single cost function with a Multiobjective Optimization based on Fuzzy-Decision-Making. Simulation results are presented to illustrate the behavior of the motor drive under steady-state and dynamic conditions.

关键词： Torque Inverters Stators Rotors Cost function Torque control

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process-ADAPTED control TO MAXIMIZE dynamics OF SPEED- AND DISPLACEMENT-VARIABLE PUMPS

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ASME/BATH 2014 symposium on Fluid Power and Motion control

作者： Willkomm, Johannes Wahler, Matthias Weber, Juergen Bosch Rexroth AG Lohr Germany Tech Univ Dresden IFD Dresden Germany

ISBN: (纸本)9780791845974

Within the last years, speed-variable pump drives were investigated in numerous applications. In combination with a variable displacement pump, the volume flow and the drive speed can be decoupled. In this paper the resulting degree of freedom will be used to maximize the dynamics of so called "Hydro-Gear" drive systems. By means of a novel process-adapted control concept, a dynamic model of all drive components will be transformed to a mathematical optimization problem. It will be shown that the optimum use of the two control variables depends on the actual system and process conditions. In comparison to known control strategies of speed- and displacement-variable pumps the new process-adapted approach can exemplaiy double the maximum volume flow gradient while reducing the necessary motor speed by over 40% at the same time.

关键词： dynamics (Mechanics) Pumps Displacement

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Approximate Real-Time Optimal control Based on Sparse Gaussian process Models

Approximate Real-Time Optimal Control Based on Sparse Gaussi...

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IEEE symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

作者： Boedecker, Joschka Springenberg, Jost Tobias Wuelfing, Jan Riedmiller, Martin Univ Freiburg Dept Comp Sci Machine Learning Lab D-79110 Freiburg Germany

ISBN: (纸本)9781479945528

In this paper we present a fully automated approach to (approximate) optimal control of non-linear systems. Our algorithm jointly learns a non-parametric model of the system dynamics - based on Gaussian process Regression (GPR) - and performs receding horizon control using an adapted iterative LQR formulation. This results in an extremely data-efficient learning algorithm that can operate under real-time constraints. When combined with an exploration strategy based on GPR variance, our algorithm successfully learns to control two benchmark problems in simulation (two-link manipulator, cart-pole) as well as to swing-up and balance a real cart-pole system. For all considered problems learning from scratch, that is without prior knowledge provided by an expert, succeeds in less than 10 episodes of interaction with the system.

关键词： Gaussian processes learning systems linear quadratic control manipulators nonlinear dynamical systems regression analysis GPR variance Gaussian process regression approximate real-time optimal control cart-pole system data-efficient learning algorithm iterative LQR formulation nonlinear systems receding horizon control sparse Gaussian process models system dynamics nonparametric model two-link manipulator Approximation algorithms Approximation methods Computational modeling Optimal control Optimization Predictive models Trajectory Gaussian processes Optimal control linear quadratic control Nonlinear systems learning systems Approximation method Nonlinear dynamical systems Approximation algorithms Manipulators Computational modeling Prediction models trajectory exploration strategy regression analysis Benchmark testing

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H_∞ based Stabilization and Disturbance Attenuation for Nonlinear Differential Repetitive processes with an Iterative Learning control Application

<i>H</i><sub>∞</sub> based Stabilization and Disturbance At...

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IEEE International symposium on Intelligent control (ISIC)

作者： Emelianov, Mikhail Pakshin, Pavel Galkowski, Krzysztof Rogers, Eric RE Alekseev Nizhny Novgorod State Tech Univ Arzamas Polytech Inst 19 Kalinina St Arzamas 607227 Russia Lobachevsky State Univ Nizhny Novgorod Nizhnii Novgorod 603950 Russia Univ Zielona Gora Inst Control & Computat Engn PL-65246 Zielona Gora Poland Univ Southampton Elect & Comp Sci Southampton SO17 1BJ Hants England

ISBN: (纸本)9781479974061

Repetitive processes propagate information in two independent directions and arise in the modeling of industrial systems such as metal rolling. Also iterative learning control law design can be undertaken in a repetitive process setting and for linear dynamics such designs have been experimentally verified. This paper addresses stabilization and disturbance attenuation for differential nonlinear repetitive processes where vector Lyapunov functions are used to characterize a physically relevant stability property and the disturbance attenuation is expressed in terms of an H-infinity norm. An extension to processes with failures modeled by a finite state Markov chain is also developed and applied to iterative learning control design in the presence of model uncertainty and sensor failures.

关键词： Lyapunov methods control system synthesis iterative methods nonlinear control systems H∞ based stabilization differential nonlinear repetitive processes disturbance attenuation finite state Markov chain industrial systems iterative learning control application iterative learning control design iterative learning control law design linear dynamics metal rolling model uncertainty nonlinear differential repetitive processes sensor failures stability property vector Lyapunov functions Attenuation Boundary conditions Markov processes process control Stability analysis Uncertainty Vectors Metal rolling model uncertainty Markov chain control system synthesis Nonlinear control systems Lyapunov methods iterative methods process control Attenuation Attenuation Stability analysis Iterative learning control Repetitive Boundary conditions

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Closed Loop control of Hysteretic Magnetization 7

Closed Loop Control of Hysteretic Magnetization

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7th International symposium on Resilient control systems (ISRCS)

作者： Bosack, Matthew Kollmer, James Niemoczynski, Bogdan Biswas, Saroj NSWCD SSES Philadelphia PA USA Temple Univ Philadelphia PA 19122 USA

ISBN: (纸本)9781479941872

This paper presents a novel method for closed loop control of magnetization of ferrite material using concepts from nonlinear control theory. A first principle nonlinear mathematical model of magnetization has been developed that describes the temporal dynamics of the magnetization process. Using concepts from nonlinear control theory, it is shown that the developed control system is able to maintain the magnetization of the structure at a desired level in the presence of arbitrary ambient excitation. Simulation of the nonlinear controller, and experimental PID studies are presented to illustrate the proposed method of closed loop control of magnetization.

关键词： Nonlinear control PID control Magnetic signature Hysteresis

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Neural Network Dynamic Progamming Constrained control of Distributed Parameter systems Governed by Parabolic Partial Differential Equations with Application to Diffusion-Reaction processes

Neural Network Dynamic Progamming Constrained Control of Dis...

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IEEE International symposium on Intelligent control (ISIC)

作者： Talaei, Behzad Xu, Hao Jagannathan, S. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA Texas A& M Univ Corpus Christi Coll Sci & Engn Corpus Christi TX 78414 USA

ISBN: (纸本)9781479974061

In this paper, a novel neural network (NN) adaptive dynamic programming (ADP) control scheme for distributed parameter systems (DPS) governed by parabolic partial differential equations (PDE) is introduced in the presence of control constraints and unknown system dynamics. First, Galerkin method is utilized to develop a relevant reduced order system which captures the dominant dynamics of the DPS. Subsequently, a novel control scheme is proposed over finite horizon by using NN ADP. To relax the requirement of system dynamics, a novel NN identifier is developed. Moreover, a second NN is proposed to estimate online the time-varying non-quadratic value function from the Hamilton-Jacobi-Bellman (HJB) equation. Subsequently, by using the identifier and the value function estimator, the optimal control input that inherently lies in actuation limits is obtained. A local uniform ultimate boundedness (UUB) of the closed-loop system is verified by using standard Lyapunov theory. The performance of proposed control scheme and effects of its design parameters are successfully verified by simulation on a diffusion reaction process.

关键词： Neural networks

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Supervisory System Identification for Bilinear systems with Application to Thermal dynamics in Buildings

Supervisory System Identification for Bilinear Systems with ...

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IEEE International symposium on Intelligent control (ISIC)

作者： Chasparis, Georgios C. Natschlaeger, Thomas Software Competence Ctr Hagenberg GmbH Dept Data Anal Syst Softwarepk 21 A-4232 Hagenberg Austria

ISBN: (纸本)9781479974061

When system identification is performed online and predictions of the system response are requested often (as in model predictive control formulations), the identification model with the best performance may not be fixed with time. Besides, more accurate models may require larger training times compared to low-order linear models. This is particularly evident in thermal dynamics in buildings where operating conditions may change throughout the year. To this end, this paper introduces a supervisory identification process, tailored specifically for input-output stable bilinear systems, where two parallel decision processes run periodically. The first one is concerned with the selection of the appropriate partition of the input(s) domain, while the second one is concerned with the selection of the identification model for each one of the resulting partition sets. The overall identification model constitutes a switched system. We show analytically that the proposed scheme is adaptive and robust to changes in the performance of the identification models, while convergence is attained (in probability) to the best model.

关键词： Model predictive control

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Extremum seeking control of the CANON process-Existence of multiple stationary solutions

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JOURNAL OF process control 2014年第2期24卷 348-356页

作者： Trollberg, Olle Carlsson, Bengt Jacobsen, Elling W. KTH Royal Inst Technol Dept Automat Control SE-10044 Stockholm Sweden Uppsala Univ Dept Informat Technol Div Syst & Control SE-75105 Uppsala Sweden

The paper considers the use of extremum seeking control (ESC) for real-time optimization of the completely autotrophic nitrogen removal over nitrite (CANON) process. In particular, the feasibility of employing relatively fast estimation and control, compared to the open-loop dynamics, is considered. From simulations it is found that the ESC can lock onto stationary solutions far removed from the optimum and that, for some control parameters, multiple stationary solutions may exist. To verify the simulation results we analyze a general dynamic model for ESC and show that all stationary solutions can be characterized by simple conditions on the gain and phase lag of the frequency response of the locally linearized system. In order to relate the derived conditions to conditions for optimality, we consider characteristic dynamic properties of systems displaying extremum points and show that the phase lag condition implies that a stationary solution in the vicinity of the optimum is likely to exist even with relatively fast estimation and control. However, the phase lag condition can also be fulfilled at operating points with no relation to the optimum whatsoever. Thus, the ESC can yield multiple stationary solutions and for some initial conditions lock onto solutions far removed from the optimum. A simple stability analysis is presented to verify that multiple solutions can be locally asymptotically stable. A simple bioreactor model is considered to illustrate the results and we show that the ESC provides three stationary solutions, of which two are stable and one unstable, even for relatively slow estimation schemes. We also confirm that the stationary solutions of the CANON process, found through simulations, fulfill both the phase lag condition and the stability criterion derived in the paper. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Wastewater Extremum seeking control Real time optimization Bifurcation

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REDUCED ORDER DIGITAL controlLER DESIGN FOR PLANTS WITH LARGE TIME DELAYS

REDUCED ORDER DIGITAL CONTROLLER DESIGN FOR PLANTS WITH LARG...

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6th International symposium on Communications, control and Signal processing (ISCCSP)

作者： Ergenc, Ali Fuat Alikoc, Baran Istanbul Tech Univ Control & Automat Engn Dept TR-34469 Maslak Turkey

ISBN: (纸本)9781479928903

In process control, inherited large time delays both in feedback line and actuator dynamics compromise the stability and the performance of the system especially with the fast acting controllers. The new method utilizes Kronecker multiplication and palindromic polynomials to reduce the order of the characteristic equation to ease the digital controller design. Furthermore, the method facilitates rightmost root placement to implement controllers which increases the performance of the control action. In this study, we present the new controller design method and its application on an industrial experimental case.

关键词： discrete-time time-delay systems kronecker product palindrome polynomials dominant pole assignment two-tank liquid level

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Modeling Surface Nanostructure Formation for Spray-dried Multicomponent Microparticles

Modeling Surface Nanostructure Formation for Spray-dried Mul...

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The 5th Asian symposium on Emulsion Polymerization and Functional Polymeric Microspheres

作者： Yu Wang Liangchao Shang Jie Xiao Xiao Dong Chen School of Chemical and Environmental Engineering College of ChemistryChemical Engineering and Materials ScienceSoochow University

Surface composition and nanoscale structure of spray-dried microparticles play a critical role in powder products' functionality and quality. Due to experimental limitations, there is a significant lack of understanding of the surface formation mechanisms for spray-dried microparticles. Thus, structure-focused particle quality control has been a challenge. This work summarizes our past and on-going efforts on surface composition prediction and nanostructure formation modeling for spray-dried particles. Recently, we introduced a multiscale modeling method to predict surface composition of a two-component spray-dried particle. A continuum diffusion analytical model has been coupled with a molecular-level geometrical packing interpretation, which bridges quantitatively the composition result in the thick continuum-scale surface layer and that in the thin layer detected by the X-ray photoelectron spectroscopy(XPS). This model, however, cannot offer nanoscale surface morphology and it cannot capture surface formation dynamics. Particle surface formation is essentially an evaporation induced self-assembly of solute molecules. We are developing a unique coarse-grained lattice Monte Carlo(LMC) model to describe this non-equilibrium process for thin film drying. Preliminary results on a 2D system demonstrate that the bulk concentration and composition of the precursor solution together with the drying condition significantly affect the nanoscale structure of aggregated solute layers as well as its dynamic evolution. The current model is being extended to the study of multicomponent 3D systems, which should be able to generate comprehensive results that can shed light on surface formation mechanisms and eventually lead to effective approaches toward surface structure focused particle quality on-aim control.

关键词： Spray drying surface microstructure evaporation induced self-assembly Monte Carlo simulation

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