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neural network predictive control in renewable systems (HKT-PV) for delivered power smoothing

JOURNAL OF ENERGY STORAGE 2024年 87卷

作者： Cano, Antonio Arevalo, Paul Jurado, Francisco Univ Jaen Dept Elect Engn Jaen Spain Univ Cuenca Dept Elect Elect & Telecommun Engn DEET Cuenca 010107 Ecuador

The reduction of power fluctuations from intermittent renewable sources is one of the most pressing challenges today. Recent research has shown that prediction and control mechanisms, when combined with energy storage systems, significantly contribute to improving these techniques. However, substantial research gaps still exist regarding the optimization of energy storage system operability. This article introduces an innovative power smoothing method based on neural network predictive control, in conjunction with the exponential moving average method. The proposed approach encompasses the ability to substantially reduce energy fluctuations, optimize battery state of charge, and mitigate ramp rates, thereby preventing deep discharges that shorten battery lifespan. Furthermore, the control system ' s primary objective is to optimize energy exchange with the grid, surpassing the performance offered by other conventional power smoothing methods. The control system excels in optimizing energy exchange within the network, surpassing conventional methods. Extensive testing on the University of Cuenca microgrid reveals a consistently more stable and higher battery charge compared to conventional methods. Numerical results for underscore the method ' s effectiveness with a fluctuation suppression rate of 30.78 % compared to 34.85 % (low pass filter) and 36.22 % (ramp rate) methods respectively. The enhanced voltage profiles at the common coupling point ensure the delivery of high -quality and stable power.

关键词： neural network predictive control Exponential moving average Power smoothing Renewable energy integration Battery operation

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neural network predictive control of converter inlet temperature based on event-triggered mechanism in flue gas acid production

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OPTIMAL control APPLICATIONS & METHODS 2024年第4期45卷 1815-1831页

作者： Liu, Minghua Li, Xiaoli Wang, Kang Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Key Lab Computat Intelligence & Intelligen Beijing Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

The process of smelting non-ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO 2$$ {}_2 $$), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO 2$$ {}_2 $$ and simultaneously mitigate environmental pollution by generating acid from flue gas. Because of the high degree of uncertainty in smelting process, converter inlet temperature is challenging to regulate and controller frequently needs updating. To improve control performance and decrease controller update times, an event-triggered neural network model predictive control (ETNMPC) strategy is proposed. First, long short-term memory (LSTM) prediction model and model predictive controller are developed. Second, it is decided whether to update the existing controller by designing an event-triggered mechanism. Finally, using real data from a copper facility in Jiangxi Province, the temperature control experiment of converter inlet is carried out. Simulation results demonstrate that the proposed ETNMPC outperforms conventional time-triggered method in terms of control performance, greatly lowers the times of controller updates, and significantly lowers computation costs and communication burden. Key findings: (1) LSTM neural network is used to establish the predictive model of converter inlet temperature and model predictive controller is designed. (2) Two different event-triggered mechanisms with fixed threshold are designed. (3) Event-triggered neural network predictive control can effectively reduce the number of controller triggers, save computing resources, and improve system performance. image

关键词： event-triggered mechanism flue gas acid production neural network predictive control tracking control

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Design of autonomous vehicle trajectory tracking controller based on neural network predictive control

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年第5期238卷 946-963页

作者： Geng, Guoqing Lu, Sinan Duan, Chen Jiang, Haobin Xiang, Huarong Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China Wayne State Univ Elect & Comp Engn Detroit MI USA Jiangsu Univ Sch Automobile & Traff Engn Zhenjiang Jiangsu Peoples R China China Automot Engn Res Inst Co Ltd Chongqing Peoples R China

When the linear Model predictive control (MPC) algorithm is used for trajectory tracking control of autonomous vehicles in high-speed turning conditions, the tracking accuracy is usually decreased due to the low fidelity of the predictive model. To deal with this problem, a trajectory tracking controller based on neural network predictive control (NNPC) is designed in this paper. The data collected from driving simulation experiments is used for the Back Propagation neural network (BPNN) training process to obtain the vehicle state predictive models. To reduce the iteration times of the rolling optimization module of NNPC, Particle Swarm Optimization (PSO) with Fitness Allocating Inertia Weights (PSO-FAIW) algorithm is proposed, which can allocate inertia weights in light of the distance between each particle's fitness and the best fitness of the swarm. Finally, the performance of the controller designed in this paper is verified based on MATLAB /Simulink. The simulation results demonstrate that the controller proposed in this paper can give consideration to tracking accuracy and real-time performance.

关键词： Autonomous vehicle trajectory tracking accuracy real-time performance neural network predictive control Particle Swarm Optimization

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neural network predictive control of Explorer Class Autonomous Underwater Vehicle

Neural Network Predictive Control of Explorer Class Autonomo...

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2022 Australian and New Zealand control Conference

作者： Chuong Nguyen Minh Tran Saji, Neetha Nguyen, Hung D. RMIT Univ Sch Sci Engn & Technol Melbourne Vic Australia Univ Tasmania Australian Maritime Coll Hobart Tas Australia Southerly Designs Port Denison WA Australia Univ Tasmania Australian Maritime Coll Natl Ctr Maritime Engn & Hydrodynam Hobart Tas Australia

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

This paper investigates neural network predictive control (NNPC) of Explorer Class Autonomous Underwater Vehicle (AUV) in path following missions. A non-linear dynamic model for the Explorer class AUV at the Australian Maritime College, University of Tasmania is developed based on analytical approach and it is approximated by a neural network via a training process before implemented in a predictive control approach. The fundamental control objectives are to maintain the AUV at desired forward velocity known as surge velocity as well as the position and heading angle in the horizontal plane, which then will be integrated into the cascade control to guide the AUV following a desired path. The effectiveness of the developed NNPC is validated by comparison with conventional Proportional Integral Derivative (PID) controller through numerical simulations.

关键词： Autonomous Underwater Vehicle neural network predictive control Path Following Simulation

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neural network predictive control of a Vapor Compression Cycle

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第2期45卷 779-796页

作者： Turgut, Mert Sinan Coban, Mustafa Turhan Ege Univ Mech Engn Dept Fac Engn TR-35040 Izmir Turkey

This study investigates the neural network predictive control of a vapor compression cycle (VCC). VCC consists of four components, namely the compressor, electronic expansion valve (EEV), evaporator and condenser. Modeling of the compressor and EEV is carried out with the static relationships, while modeling of the evaporator and condenser is performed with the lumped parameter moving boundary method. The established thermodynamic model is validated against the ASPEN model with the same design specifications. The neural network is trained off-line with the input and output signal data of the established model. The solution of the optimization problem for the each time step is achieved with the metaheuristic method called Whale Optimization Algorithm in the predictive controller. Ultimately, performances of the four different controllers, namely the cooling load, first law efficiency, entropy generation and second law efficiency, are compared with each other. The results show that the entropy generation controller achieves the most favorable exergy destruction performance with 0.2% lower than the worst performer cooling load controller. It is also observed that the second law efficiency controller is the best performer in terms of the overall second law efficiency through the simulation time.

关键词： Vapor compression cycle control systems Whale Optimization Algorithm neural network predictive control Dynamic system modeling

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A neural network predictive control method for power control of small pressurized water reactors

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ANNALS OF NUCLEAR ENERGY 2022年 169卷 108946-108946页

作者： Xiao, Kai Wu, Qiaofeng Chen, Jie Pu, Xiaofei Zhang, Ying Yang, Pengcheng Nucl Power Inst China Sci & Technol Reactor Syst Design Technol Lab Chengdu 610213 Peoples R China

Compared with large pressurized water reactors (PWRs), small PWRs have flexible operating conditions and complex operating environment, putting forward higher requirements on the power control system. Considering the low model identification accuracy of the widely-used model predictive control algorithm in reactor control, a neural network predictive (NNP) power control method is proposed for small PWRs in this paper. The local models under five typical operating conditions are weighted by a multi-model method to establish a core multi-model system, based on which a global NNP power control system is designed. The neural network is used to identify the core model first, and then a performance criterion function is optimized to determine the optimal control input to the reactor core. Simulation results of a small PWR core under typical transient conditions demonstrate the good load-following performance and strong anti-interference capability of the proposed NNP power control method for small PWRs. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Small pressurized water reactor neural network predictive control Reactor power control system Multi-model method

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neural network predictive control of Systems with Faster Dynamics using PSO

Neural Network Predictive Control of Systems with Faster Dyn...

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International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)

作者： M. Germin Nisha M. John Robert Prince A. Jenisha Jones Electrical and Electronics Engineering St. Xavier’s Catholic College of Engineering Nagercoil Civil Engineering St. Thomas College of Engineering and Technology Chenganur Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil

ISBN: (纸本)9781538694831

An effective application of Model predictive control by means of a multi-layer feed forward neural network as the nonlinear model of the process is discussed. The main draw back in the use of Nonlinear programming for optimization is the complexity in the calculation of Hessian matrix and its inverse. There are many derivative free evolutionary algorithms, inspired by biological evolution. In this paper the optimization problem is solved using particle swarm optimization (PSO). Simulation results show convergence to a good solution within fewer numbers of iterations which makes it suitable for real time applications with faster sampling.

关键词： evolutionary computation feedforward neural nets Hessian matrices neurocontrollers nonlinear programming particle swarm optimisation predictive control Hessian matrix feedforward neural nets Nonlinear Programming Neurocontrollers Particle swarm optimization evolutionary algorithm predictive control neural network predictive control parametric subharmonic oscillator control systems evolution

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On-line neural network predictive control for Swing Phase control of MR Damper Prosthetic Knee 56

On-line Neural Network Predictive Control for Swing Phase Co...

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56th Annual Conference of the Society-of-Instrument-and-control-Engineers-of-Japan (SICE)

作者： Chaovalit, Nicha Kongprawechnon, Waree Ekkachai, Kittipong Thammasat Univ Sch Informat Comp & Commun Technol Sirindhorn Int Inst Technol Bangkok Thailand Natl Elect & Comp Technol Ctr NECTEC AAS Khlong Luang Pathum Thani Thailand

ISBN: (纸本)9784907764579

The prosthetic knees have been improved and developed to support the amputee to be able to walk as normal people and help them on a daily basis. This research is concerned with a swing phase of a semi-active prosthetic knees utilizing magnetorheological (MR) damper. Although the referred work which use a neural network predictive control (NNPC) has a satisfying results with low error, it has a possibility to improve the error at certain walking speeds. Therefore, an alternative algorithm for a semi-active prosthetic knee is proposed. The modified version of NNPC using online training scheme with scaled conjugate gradient training algorithm is applied to substitute the initial version of NNPC for the prosthetic knee model to estimate appropriate knee angle. The results from simulation using proposed controller show the improvement in error over the initial version of NNPC at absolutely slow and absolutely fast walking speeds while the results using other walking speeds are comparable.

关键词： prosthetic knee magnetorheological (MR) damper neural network predictive control online training conjugate gradient algorithm

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Variable Walking Speed controller of MR Damper Prosthetic Knee using neural network predictive control 53

Variable Walking Speed Controller of MR Damper Prosthetic Kn...

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SICE Annual Conference (SICE)

作者： Ekkachai, Kittipong Tantaworrasilp, Apicit Nithi-Uthai, Sirichai Tungpimolrut, Kanokvate Nilkhamhang, Itthisek Natl Elect & Comp Technol Ctr NECTEC AAS Bangkok Thailand Thammasat Univ Sirindhorm Int Inst Technol SIIT Sch Informat Comp & Commun Technol Bangkok Thailand

ISBN: (纸本)9784907764456

Semi-active prosthetic knees utilizing magnetorheological (MR) damper have been introduced to help transfemoral amputees to improve their quality of life. However, they lack the ability to generate power required to achieve the normal gait of healthy people. The aim of this paper is to propose a novel control algorithm for MR damper prosthetic knee, focusing on swing phase of the gait cycle. The proposed controller is developed by using a neural network predictive control optimized using constrained nonlinear optimization. It provides the ability to predict future knee angle trajectory when a certain command voltage is applied in order to determine the optimal command voltage. Moreover, the proposed algorithm is designed to support amputees at walking speeds. Performance of the proposed controller has been evaluated in an offline simulation compared to the normal gait of healthy people. The results show that the proposed controller can generate the knee trajectory with minimal errors.

关键词： neural network predictive control prosthetic knee magnetorheological damper

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Thermal stress management of a solid oxide fuel cell using neural network predictive control

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ENERGY 2013年 62卷 320-329页

作者： Hajimolana, S. A. Tonekabonimoghadam, S. M. Hussain, M. A. Chakrabarti, M. H. Jayakumar, N. S. Hashim, M. A. Univ Malaya Fac Engn Dept Chem Engn Kuala Lumpur 50603 Malaysia Univ London Imperial Coll Sci Technol & Med Energy Futures Lab London SW7 2AZ England

In SOFC (solid oxide fuel cell) systems operating at high temperatures, temperature fluctuation induces a thermal stress in the electrodes and electrolyte ceramics;therefore, the cell temperature distribution is recommended to be kept as constant as possible. In the present work, a mathematical model based on first principles is presented to avert such temperature fluctuations. The fuel cell running on ammonia is divided into five subsystems and factors such as mass/energy/momentum transfer, diffusion through porous media, electrochemical reactions, and polarization losses inside the subsystems are presented. Dynamic cell-tube temperature responses of the cell to step changes in conditions of the feed streams is investigated. The results of simulation indicate that the transient response of the SOFC is mainly influenced by the temperature dynamics. It is also shown that the inlet stream temperatures are associated with the highest long term start-up time (467 s) among other parameters in terms of step changes. In contrast the step change in fuel velocity has the lowest influence on the start-up time (about 190 s from initial steady state to the new steady state) among other parameters. A NNPC (neural network predictive controller) is then implemented for thermal stress management by controlling the cell tube temperature to avoid performance degradation by manipulating the temperature of the inlet air stream. The regulatory performance of the NNPC is compared with a PI (proportional-integral) controller. The performance of the control system confirms that NNPC is a non-linear-model-based strategy which can assure less oscillating control responses with shorter settling times in comparison to the PI controller. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.

关键词： Solid oxide fuel cell neural network predictive control Cell-tube temperature Thermal stress

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