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QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

Frontiers of Structural and Civil Engineering 2023年第1期17卷 25-36页

作者： Xinyu WANG Jian WU Xin YIN Quansheng LIU Xing HUANG Yucong PAN Jihua YANG Lei HUANG Shuangping MIAO Yellow River Engineering Consulting Co. Ltd.Zhengzhou 450003China School of Civil Engineering Wuhan UniversityWuhan 430072China Power China Huadong Engineering Co. Ltd.Hangzhou 311122China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

关键词： tunnel boring machine control parameter optimization quantum particle swarm optimization artificial neural network tunneling energy efficiency

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Virtually constrained generalized relative motion modeling and a control parameter optimizer for automatic carrier landing

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2024年第3期96卷 448-457页

作者： Zhang, Yiwei Li, Daochun Kan, Zi Yao, Zhuoer Xiang, Jinwu Beihang Univ Sch Aeronaut Sci & Engn Beijing Peoples R China Tianmushan Lab Hangzhou Zhejiang Peoples R China

PurposeThis paper aims to propose a novel control scheme and offer a control parameter optimizer to achieve better automatic carrier landing. Carrier landing is a challenging work because of the severe sea conditions, high demand for accuracy and non-linearity and maneuvering coupling of the aircraft. Consequently, the automatic carrier landing system raises the need for a control scheme that combines high robustness, rapidity and accuracy. In addition, to exploit the capability of the proposed control scheme and alleviate the difficulty of manual parameter tuning, a control parameter optimizer is ***/methodology/approachA novel reference model is constructed by considering the desired state and the actual state as constrained generalized relative motion, which works as a virtual terminal spring-damper system. An improved particle swarm optimization algorithm with dynamic boundary adjustment and Pareto set analysis is introduced to optimize the control *** control parameter optimizer makes it efficient and effective to obtain well-tuned control parameters. Furthermore, the proposed control scheme with the optimized parameters can achieve safe carrier landings under various severe sea ***/valueThe proposed control scheme shows stronger robustness, accuracy and rapidity than sliding-mode control and Proportion-integration-differentiation (PID). Also, the small number and efficiency of control parameters make this paper realize the first simultaneous optimization of all control parameters in the field of flight control.

关键词： Automatic carrier landing system Virtually constrained generalized relative motion Particle swarm optimization algorithm control parameter optimization

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Dynamic optimization of SVR control parameters for Improving Tap Operation Efficiency of Voltage control in Distribution Networks

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2021年第1期16卷 67-77页

作者： Murakami, Kohei Yoshizawa, Shinya Ishii, Hideo Hayashi, Yasuhiro Kondo, Hiroshi Kanazawa, Yuki Nomura, Hideo Kajikawa, Takuya Waseda Univ Dept Adv Sci & Engn Tokyo 1698555 Japan Waseda Univ Adv Collaborat Res Org Smart Soc ACROSS Tokyo 1698555 Japan Chubu Elect Power Grid Co Inc Nagoya Aichi 4618680 Japan

In this study, we propose a new optimization method to determine the control parameters of a step voltage regulator (SVR) to maximize its voltage control characteristic in distribution networks (DNs) with photovoltaic systems. Considering the service life of an SVR, the proposed method evaluates the expected number of tap operations and the voltage control performance based on the past voltage measurements obtained with sensors in the DN. Subsequently, the time series of two control parameters, that is, the reference voltage and the dead bandwidth, are determined for the most improved voltage control characteristic of the SVR, maintaining the number of tap operations within the upper limit. Voltage control simulation using a realistic 6.6-kV DN model is conducted to show that the proposed method significantly reduces the voltage violation compared with the conventional SVR control using fixed parameters, as well as the alternative SVR operation developed in our previous study, in which the reference voltage was temporally updated. In addition, the simulation results demonstrate that the proposed method realizes efficient tap operations to mitigate the voltage violation, whereas the conventional method requires a much larger number of tap operations to mitigate the voltage violation to the same degree. (c) 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

关键词： control parameter optimization distribution network photovoltaic system semicentralized control step voltage regulator

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Fuzzy optimization Design of Adaptive Robust control for Uncertain Cooperative Robots with Servo Constraints

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2024年第4期26卷 1158-1174页

作者： Wang, Faliang Chen, Ke Zhen, Shengchao Chen, Xiaofei Zheng, Hongmei Hefei Univ Technol Sch Mech Engn Hefei 230009 Anhui Peoples R China Jingdezhen Univ Sch Mech & Elect Engn Jingdezhen 333000 Jiangxi Peoples R China Hefei Univ Technol Anhui Key Lab Digital Design & Mfg Hefei 230009 Anhui Peoples R China

In this paper, the reference trajectory tracking issue of cooperative robots system with bounded uncertainty is investigated from a novel standpoint of servo constraint-following. The bound of uncertainty is not known but can be characterized by the fuzzy set theory. Then, establishes the dynamical model of cooperative robots system with fuzzy uncertainty. The process of the proposed controller design contains two steps. First, an adaptive robust control is investigated which can render the desired servo constraint to be followed even if there exists uncertainty in the system, and through stability analysis by selecting appropriate Lyapunov function which proves uniform boundedness and ultimate uniform boundedness of tracking error. The second step is to find a parameter selection guideline for the optimal control parameter. Based on the fuzzy description of system uncertainty, a performance index consisting of system performance and control cost can be developed. Then, the selection of optimal control parameters translates into finding the parameter that makes the performance index minimized. Finally, numerical simulations are performed on a two-planar two-link manipulator system for evaluating the performance of the proposed method.

关键词： Fuzzy set theory Adaptive robust control control parameter optimization Servo constraint-following

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control Criteria Determination and Quality Inference for Resistance Spot Welding through Monitoring the Electrode Displacement Using Bayesian Belief Networks

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JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2012年第4期6卷 432-444页

作者： Gong, Liang Liu, Cheng-Liang Li, Yan-Ming Shanghai Jiao Tong Univ Inst Mechatron Shanghai 200030 Peoples R China

Currently real-time control and online quality estimation of the resistance spot welding (RSW) has benefited a lot from monitoring the electrode displacement. Based on these emerging monitoring techniques a new approach is proposed to determine the optimal welding parameters and help to assess the weld quality. Two causal models are built with the offline trained Bayesian Belief Networks (BBN). The first model which is a pattern determination net deals with the optimal control criteria, i.e. an ideal combination of the maximum electrode displacement and electrode travel velocity, to provide more reliable welding process and qualified welds. The second model which is a weld quality assessment net reveals the dependency of the weld quality on the features displayed by the displacement curve, which can be used for overdesigning the safety welds or for online assessing weld quality as the probabilistic forecasting model. The experimental results show that the proposed approach is valid and feasible to determine the controlled parameters and to predict the weld quality in practices.

关键词： Resistance Spot Welding control parameter optimization Quality Assessment Bayesian Belief Network Electrode Displacement Welding Expulsion

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Design and control of a Passive Magnetic Levitation Carrier System

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2015年第4期16卷 693-700页

作者： Li, Shengbo Eben Park, Jin-Woo Lim, Jae-Won Ahn, Changsun Tsinghua Univ Dept Automot Engn Beijing 100084 Peoples R China Korea Inst Machinery & Mat Dept Syst Dynam Vibrat & Shock Res Team Taejon 305343 South Korea Korea Inst Machinery & Mat Dept Magnet Levitat & Linear Drive Taejon 305343 South Korea Pusan Natl Univ Sch Mech Engn Busan 609735 South Korea

A passive magnetic levitation (maglev) carrier is a transportation device that is based on magnetic levitation and does not have any electric circuits on the moving part. Because the actuation devices are not in the moving platform for passive operation, the levitation forces are non-homogenous. This characteristic makes the design and control of the system challenging. We present design and control procedures for a passive maglev carrier system. To achieve stable and accurate levitation, mechanical analyses were performed to determine the configuration and locations of the levitation magnets. The key challenge in the control design is that the locations and magnitudes of the levitation forces are not continuous around the edge of the moving platform when it passes the magnet. Therefore, the longitudinal positions of the magnets relative to the moving platform are a noise (uncontrollable) factor for the controller The control parameters should be robust against the noise factor to achieve reliable performance. The Taguchi method was used to determine the optimal control parameters in the presence of the noise factor The optimized gain reduced the position variation of the levitated carrier by 50% in terms of the root mean square error.

关键词： control parameter optimization Magnetic levitation Passive levitation Taguchi method

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Research on online optimization scheme and deployment of PMSM control parameters based on honey badger algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Zhu, Xiaofeng Hu, Yiming Yu, Yinquan Zeng, Dequan Yang, Jinwen Carbone, Giuseppe East China Jiaotong Univ Sch Mechatron & Vehicle Engn Nanchang 330000 Peoples R China Univ Calabria Dept Mech Energy & Management Engn I-87036 Arcavacata Di Rende Italy

Permanent magnet synchronous motor (PMSM) drive systems are receiving increasing attention due to their superior control quality and efficiency. Optimizing the control parameters are key to achieve the high-performance operation of PMSMs. Although heuristic algorithms demonstrate excellent optimization outcomes in simulations, there are still challenges in deploying optimization schemes in practical drives. In this study, a real-time online deployable control parameter optimization scheme is proposed. The optimization effect is evaluated through the system step response performance, and a framework for deploying optimization algorithms within the driver is developed. A fault suppression mechanism is also designed to mitigate overshoot and vibration issues caused by suboptimal solutions. The proposed scheme is validated on a rapid prototyping control platform. Experimental results confirm that the scheme exhibits good optimization performances across various operating conditions. The honey badger algorithm employed in this paper shows faster convergence and more stable optimization effects than other optimization algorithms. The optimization effect is improved by 2.2% and its performance in terms of consistency across multiple optimization results has increased by 40%.

关键词： PMSM Honey badger algorithm Metaheuristic algorithms Online optimization control parameter optimization control failure suppression

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A METHOD FOR AUTO-TUNING OF PID control parameterS

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AUTOMATICA 1984年第3期20卷 321-332页

作者： NISHIKAWA, Y SANNOMIYA, N OHTA, T TANAKA, H FUJI FACOM CORP SYST DEV SECTHINOTOKYO 191JAPAN FUJI FACOM CORP SYST RES SECTHINOTOKYO 191JAPAN

A method for automatic tuning of the PID process control parameters, usually called ‘auto-tuning’, is developed. The procedure of applying the method consists of (1) sampling a process response to a test input signal, (2) processing the sampled data for estimating characteristic values of the process, and (3) calculating the optimal values of the PID control parameters. For the optimization, a new type of performance index, i.e. a weighted integral of squared error is introduced. The procedure is implemented on a digital controller using microprocessors and applied to some real processes, yielding satisfactory results.

关键词： adaptive control control parameter optimization direct digital control man-machine interactive method PID process control process parameter estimation weighted ISE

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Research on optimization of control parameters of Coal Sampling Robot Based on Model and Neural Network Algorithm 12th

Research on Optimization of Control Parameters of Coal Sampl...

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12th International Conference on Intelligent Robotics and Applications (ICIRA)

作者： Liu, Xiaodong Xu, Haibo Wang, Jun Wang, Rui Liu, Li Xi An Jiao Tong Univ Xian 710049 Shanxi Peoples R China Xian Hongyu Min Special Mobile Equipment Co Xian 710075 Shanxi Peoples R China

ISBN: (纸本)9783030275297;9783030275280

This paper takes the single joint (big arm joint) servo control system of coal sampling robot as the research object, analyzes the structure of servo control system, establishes the model of single joint servo control system to study the method of tuning and optimizing the control parameters of the servo system. The combination of model-based control parameter tuning and improved BP neural network control parameter optimization is adopted to improve the system's ability to adapt to the load and improve the performance of system.

关键词： Load inertia BP neural network control parameter optimization

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Genetic algorithm-based optimization framework for control parameters of ventricular assist devices

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BIOMEDICAL SIGNAL PROCESSING AND control 2023年 85卷

作者： Magkoutas, Konstantinos Rossato, Leonardo Nunes Heim, Marco Daners, Marianne Schmid Swiss Fed Inst Technol Dept Mech & Proc Engn Prod Dev Grp Zurich Zurich Switzerland

In this work, a novel, genetic algorithm-based optimization framework (GAOF) has been developed and evalu-ated to optimize the control parameters of ventricular assist devices (VADs). This framework enables the opti-mization of complex control structures based on VAD-and patient-specific characteristics by allowing the selection of the numerical model of the human cardiovascular system and the VAD to represent the patient scenario of interest accurately. Additionally, the GAOF can incorporate treatment-specific goals during the definition of the objective functions of the optimization problem and, consequently, promotes the development of treatment-specific VAD controllers. The efficacy of the GAOF was assessed with one-and two-degree-of -freedom physiologic proportional-integral-derivative controllers and a physiologic data-driven iterative learning controller. Two VAD designs and various patient disease scenarios were used to further explore and evaluate the capabilities of the GAOF. The optimized controllers outperformed substantially the hand-tuned controller, which was used as the benchmark, in all the investigated cases. This highlights the potential improvement in the performance of any VAD controller by deploying the GAOF and, consequently, the possibility to increase the survival rates and enhance the quality of life of VAD patients.

关键词： Genetic algorithm optimization Ventricular assist devices (VAD) VAD physiological control PID optimization Heart Failure control parameter optimization

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