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IEEE International Conference on Industrial Technology (ICIT)

作者： de Marchi, Rodrigo A. Bim, Edson Univ Estadual Campinas Sao Paulo Fed Inst Sch Elect & Comp Engn BR-13083852 Campinas Brazil Univ Estadual Campinas Sch Elect & Comp Engn BR-13083852 Campinas Brazil

ISBN: (纸本)9781479978007

This paper presents a predictive Direct Power control Applied to the Doubly Fed Induction Generator operating with or without voltage dip, in variable speed operation. The predictive controller is obtained from state-space DFIG model. The control law is derived by optimization of a cost function that considers the difference between the predicted outputs - stator active and reactive powers - and their references. It used a discrete state-space model to describe the system. As the voltage dip is not predicted, it is considered to be a disturbance in the control algorithm. Digital simulations are performed to verify the efficiency potential of the proposed controller.

关键词： Direct power control Doubly-fed induction generator predictive controller model based predictive control

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Decentralised heat pumps and small electricity storages as active components in a virtual power plant for smart grid services 15

Decentralised heat pumps and small electricity storages as a...

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IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

作者： Pietruschka, Dirk Brennenstuhl, Marcus Matthiss, Benjamin Binder, Jann Hsch Techn Zafh Net Stuttgart Germany Zentrum Sonnenenergie & Wasserstoff Forsch Photovolta Modules Syst Applicat Stuttgart Germany

ISBN: (纸本)9781479979929

Within the national funded project EnVisaGe (***) a small Plus-Energy district mainly with single-family homes is built in Wustenrot, Germany. A novel district heating system based on a cold-water heating grid supplies low temperature heat from a near surface geothermal system to the heat pumps with hot water storages in the connected buildings of the district. To reach the Plus-Energy level, highly energy efficient buildings close to passive house standard are built and equipped with large PV systems for onsite solar electricity generation. Some buildings are also equipped with electricity storages providing a capacity between 3 and 5 kWh each. Heat pumps with hot water storage offer the opportunity to shift electricity loads e.g. to periods with high solar radiation. Combined with the small electricity storages, this can significantly increase the PV self-consumption / self-sufficiency rate and help to reduce peak feed-in power to improve the grid compatibility [1] [2]. This complex energy management task requires innovative MPC (model based predictive control) schemes implemented in an appropriate monitoring and control system, which is developed and tested within the project. Apart from this local optimization on a single building level, decentralised heat pumps and batteries in a larger cluster can also deliver controllable electricity sinks as smart grid applications for electricity providers. To test these additional opportunities within the project, the heat pumps and batteries are connected to the virtual power plant of Vattenfall. Within the paper, the developed cloud-based data collection and management system is presented together with the MPC based system. Additionally, monitoring and simulation results are shown.

关键词： load management smart grid virtual power plant model based predictive control

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MBPC controller for UGV Trajectory Tracking 5

MBPC controller for UGV Trajectory Tracking

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5th IEEE Colombian Conference on Automatic control (IEEE CCAC) - Technological Advances for a Sustainable Regional Development

作者： Barrero, Oscar Tique, Juan C. Univ Ibague Res Grp D TEC Ibague Colombia

ISBN: (纸本)9781665418836

In this paper, the development of a tracking trajectories system for a skid-steering type agricultural vehicle based on predictive control is presented. Since the vehicle operates outdoors, a high precision measurement of the geolocation and orientation is needed, therefore, a Kalman filter was designed. That filter fusions the data of position and orientation from an inertial measurement unit (IMU), wheel encoders and GPS (Global Positioning System) mounted on the vehicle. On the other hand, for the autonomous tracking trajectories, two controllers were designed: first, a proportional controller for the linear speed, and second, a model based predictive controller (MBPC) for the vehicle heading angle. For the design of the MBPC, a dynamical model of the vehicle direction was obtained using a gray box modeling, additionally, an incremental state space model strategy was use to design and deploy the MBPC. Finally, the results show that the developed control strategy presents a good performance with a RMSE of 1.038m in tracking outdoor trajectories.

关键词： Unmanned Ground Vehicle UGV Skid-steering Vehicle Track Following model based predictive control Kalman Filter

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Multi-objective Tuning of Generalized predictive controller: A Trade-off Between Performance and Robustness 8

Multi-objective Tuning of Generalized Predictive Controller:...

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8th Brazilian Symposium on Computing Systems Engineering (SBESC)

作者： de Oliveira Junior, Javan A. Assuncao, Wesley K. G. Jeronymo, Daniel C. Univ Tecnol Fed Parana Dept Elect Engn Toledo Brazil Univ Tecnol Fed Parana Dept Internet Syst Toledo Brazil Univ Tecnol Fed Parana Dept Comp Engn Toledo Brazil

ISBN: (纸本)9781728102405

This paper investigates the application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to the Generalized predictive controller (GPC) parameter tuning. GPC stands out for using predictions of a future system behavior in order to evaluate an optimal control law according to certain performance criteria. GPC deals with multiple conflicting objectives such as minimizing set-point error, reducing variation of control actions and greater robustness. In this work, NSGA-II is applied to the problem of parameter tuning considering the trade-off between performance and robustness. A rule is deduced for choosing an adequate solution in the Pareto front which leads to overall best compromise between performance and robustness criteria. From the results we can observe that NSGA-II reaches satisfactory solutions that allows a better decision-making.

关键词： Process control model based predictive control Generalized predictive control NSGA-II

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Adaptive EPSAC predictive control of the Hypnotic Component in Anesthesia

Adaptive EPSAC Predictive Control of the Hypnotic Component ...

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IEEE International Conference on Automation, Quality and Testing, Robotics

作者： Nascu, Ioana Nascu, Ioan Ionescu, Clara M. De Keyser, Robin Tech Univ Cluj Napoca Dept Automat Cluj Napoca Romania Univ Ghent Dept Elect Energy Syst & Automat Ghent Belgium

ISBN: (纸本)9781467307024

The applications presented in this paper are oriented towards the study of the possibilities to apply adaptive algorithms to a model based EPSAC (Extended Prediction Self-Adaptive control) scheme for control of depth of anesthesia (DOA). The input variable is Propofol, an anesthetic substance used for both induction and maintenance control of DOA which is evaluated by means of the Bispectral index (BIS). An adaptive control algorithm based on an indirect adaptive predictive method is used. The unknown model parameters are estimated by recursive least square algorithm. The simulation tests performed on virtually generated models for a set of 12 patients show a fast response, optimal dosage and robustness of the algorithm regarding the control of the desired BIS setpoint during induction and maintainance phase.

关键词： anesthesia drug dosing control adaptive control parameter estimation model based predictive control

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predictive control review for a three-phase converter

Predictive control review for a three-phase converter

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IEEE IFAC International Conference on Automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-control (ACCA)

作者： Millalaf, Cristofer Bello, Jose Rohten, Jaime Andreu, Marcos Risso, Nathalie Rivera, Marco Munoz, Javier Univ Bio Bio Concepcion Chile Univ Bio Bio Dept Elect & Elect Engn Concepcion Chile Univ Concepcion Concepcion Chile Univ Talca Dept Elect Engn Talca Chile

ISBN: (纸本)9781665401272

This article presents a review of the predictive control techniques applied in a voltage source inverter to regulate the direct voltage (active power) and the reactive power injected into the grid. As Deadbeat and finite state control are based on the system model, they lead to a faster response and without overshoot, however, they are normally used in internal current control loops, in this case their applied behaviors are presented as such, but the application of a Deadbeat based on the general model of the static converter is also studied, thus contributing its advantages to the control of the DC voltage. When implementing the latter, some considerations related to the high power necessary to reach the references -especially in direct voltage- in a few control steps, in addition the device is protected by limiting the maximum power drained from the source. The mathematical development is carried out according to the power of the converter, limiting it, but tracking the references with high dynamics. The system is subjected to significant disturbances, demonstrating their robustness to such events.

关键词： Finite control Set Deadbeat control Voltage Source Inverter model based predictive control

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Constrained macroeconomic policy development with a separate predictive model

Constrained macroeconomic policy development with a separate...

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14th Biennial Conference on modelling and Simulation

作者： Herbert, RD Bell, RD Univ Newcastle Fac Sci & Informat Technol Ourimbah NSW 2258 Australia

This paper examines the issue of the generation of optimal control policies where there are explicit constraints upon the control values and there is limited knowledge of the complex economic system. The paper develops a methodology where the constrained optimal control is based upon a separate model that predicts the policy targets for the economic system. The methodology as applied to a small calibrated macroeconomic model of Australia. (C) 2003 IMACS. Published by Elsevier B.V.. All rights reserved.

关键词： constrained optimal control model based predictive control quadratic programming

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predictive models For Smart Vineyards

Predictive Models For Smart Vineyards

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作者： F. R. Luttich Stellenbosch University

学位级别：硕士

ENGLISH ABSTRACT: We investigate the application of machine learning algorithms to the predictive analysis of environmental datasets compiled from two distinct vineyards. These datasets include the soil temperature at various depths and locations, the soil moisture content of the same locations and the bud-burst dates. Measurements were taken regularly over the space of four months for one vineyard and over twelve months for the other. The prediction of the soil temperature from either ambient measurements or from satellite data, as well as the prediction of soil moisture content and the bud-burst dates were the primary objectives of our analysis. Linear regression, feedforward neural networks and recurrent neural networks were considered as algorithms. For the neural networks, several training strategies were considered. It was found that neural networks outperform linear regression when predicting soil temperatures from ambient temperature and humidity, and also when predicting soil moisture content from ambient temperature, humidity and rainfall data. Although recurrent neural networks (LSTMs) were able to achieve even better results when the data was carefully prepared, these networks were sensitive to discontinuities present in the data due to faulty sensor measurements. Feedforward neural networks, on the other hand, were more robust to these errors. Since sensors placed in a vineyard are exposed and must remain unattended, this is an important aspect to consider. It was also found that soil temperatures could be predicted with a modest loss in accuracy from freely-available satellite land temperature measurements. Although cloud cover leads to sporadic non-availability of the measurements, they represent a very attractive alternative to locally installed weather sensors since they would no longer need to be installed or maintained. For soil moisture content and bud-burst dates neural networks provided better predictions than a na ve guess. While this indicate

关键词： UCTD Machine learning model based predictive control Data sets

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control of a non-linear multivariable system in non-minimum phase by using a neural predictive controller

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2023年第1期20卷 32-43页

作者： Calle Chojeda, Elmer Oliden Semino, Jose Ipanaque Alama, William Univ Piura Lab Sistemas Automat Control Dept Ingn Mecan Elect Av Ramon Mugica 131 Piura 20001 Peru

In this paper, a Neural predictive controller (ANN-MPC) is proposed to control a nonlinear quadruple tank system, which is complex to control due to the nonlincarily of its valves and the interaction between its controlled variables. In addition, the problem is aggravated since the process presents a transient response with inverse dynamics due to being in a non-minimum phase. The ANN-MPC employs a modular artificial neural network framework and the Levcnbcrg-Marquardt training algorithm to more accurately and quickly estimate nonlinear process outputs and avoid model overfitting. Operational data was generated from the plant to train the neural network using Matlab. The performance of the ANN-MPC under reference changes was tested and compared with a linear MPC and a non-linear MPC. The simulation results showed that the ANN-MPC produced a shorter settling time than the linear MPC and generated RMSE values of the outputs similar to those of the NMPC In addition, the computation time required to calculate the optimal control variable was reduced compared to the NMPC. © 2023 Universitat Politecnica de Valencia. All rights reserved.

关键词： Artificial neural networks MIMO system model based predictive control Quadruple-tank system

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Closed-Loop Identification of Linear Time-Invariant Systems Under Linear Time-Varying Feedback

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IFAC Proceedings Volumes 1998年第11期31卷 341-346页

作者： C.T. Chou Michel Verhaegen Control Laboratory Department of Electrical Engineering Delft University of Technology P.O. Box 5031 2600 GA Delft The Netherlands. Fax: 4-31-15-2786679.

This paper looks at the problem of identifying a linear time-invariant (LTI) plant under linear time-varying (LTV) feedback. We first show that a particular subspace closed-loop identification algorithm that consistently identifies a LTI system under LTI feedback will also give consistent estimate under LTV feedback using only one set of input/output measurements provided that the resulting closed-loop system is exponentially stable. We then show, using piecewise LTI property of MBPC controllers, that the problem of identifying a LTI plant under MBPC feedback (which is nonlinear time-varying) can be transformed into the problem of identifying the same LTI plant under LTV feedback. The latter problem can then be solved with our identification algorithm.

关键词： System identification closed-loop identification model based predictive control subspace identification ergodicity

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