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Improved grid mapping technology based on Rao-Blackwellized particle filters and the gradient descent algorithm

SYSTEMS SCIENCE & CONTROL ENGINEERING 2019年第1期7卷 65-74页

作者： Zhang, Tengfei Wang, Chuanjiang Yuan, Zhen Zheng, Mingyue Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Shandong Peoples R China

Recently, the Rao-Blackwellized particle filter (RBPF) has been used to solve the problem of simultaneous localization and mapping (SLAM). Using the odometer information of robot to calculate the proposed distribution requires a number of sampled particles, which increases the calculation complexity in the RBPF operation. In this paper, we integrate the odometer measurement and sensor observation into the proposed distribution, effectively reducing the particle sample scale. To reduce the inconsistency in the map model caused by the cumulative error of the odometer information of robot, we applied a gradient descent algorithm to fuse the sensor data to obtain the real-time attitude angle. This combination method, based on the robot operation system (ROS), runs on a platform of self-built mobile robot equipped with a laser rangefinder. The experimental results show that this method can realize the online real-time high-precision grid map which provides a new approach for robot navigation and SLAM.

关键词： RBPF proposal distribution gradient descent algorithm ROS

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A High-Order Neutrosophic-Neuro-gradient descent algorithm-Based Expert System for Time Series Forecasting

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2019年第7期21卷 2245-2257页

作者： Singh, Pritpal Huang, Yo-Ping Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan Natl Taipei Univ Dept Comp Sci & Informat Engn New Taipei 23741 Taiwan

This article proposed a novel hybrid time series forecasting model using neutrosophic set (NS) theory, artificial neural network (ANN) and gradient descent algorithm. This study deals with three main problems of time series dataset, viz., representation of time series dataset using NS, three degrees of memberships of NS together, and generation of the forecasting results. To resolve these three domain specific problems, this study advocated the application of neutrosophic-neuro-gradient approach. NS theory was utilized to represent the uncertainty associated with time series dataset and was referred to as neutrosophic time series (NTS). In NTS, various decision rules were created in the form of IF-THEN rules, which were termed as neutrosophic entropy decision rules (NEDRs). An ANN-based architecture took NEDRs as input to evolve the forecasting results. To improve the performance of ANN and to obtain optimal forecasting results, this study additionally utilized the gradient descent algorithm to minimize the differences between the calculated and target output values during the simulation. The proposed model was verified and validated with three different datasets, including TAIFEX index, university enrollment dataset of Alabama and Taiwan Stock Exchange Corporation (TSEC) weighted index. Experimental results showed that the proposed model outperformed existing benchmark models with average forecasting error rates of 1.02%, 0.74% and 1.27% for the TAIFEX, university enrollment and TSEC, respectively.

关键词： Neutrosophic set Artificial neural network gradient descent algorithm Entropy Time series forecasting

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Self-tuning Grey Prediction Control System using gradient descent algorithm

International Journal of Uncertainty and Innovation Research

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International Journal of Uncertainty and Innovation Research 2020年第2期2卷 101-113页

作者： Ming-Feng Yeh Ti-Hung Chen Ming-Hung Chang

A typical grey prediction control system is composed of a proportional-integral-derivative controller and a grey predictor. Rather than using differential evolution algorithms to optimize the grey prediction control system as in our previous work, this study attempts to apply the gradient descent algorithm to derive the self-tuning rules for both the coefficients of proportional-integral-derivative controller and the parameters of grey model. This study also attempts to replace the original GM(1,1) with a simple equivalent GM(1,1) in order to reduce computational complexity. Analogously, an objective function in terms of the maximal overshoot, rising time, steady-state error, and settling time is used to evaluate the performance of the comparison control systems. While using the self-tuning rule, simulation results on two single-variable plants demonstrate that the grey prediction control system with equivalent GM(1,1) could perform better than the original grey prediction control system.

关键词： Differential evolution gradient descent algorithm Grey model PID control

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Backflow Power Optimization of DAB with gradient descent algorithm Based Extended-Phase-Shift Control in EER Application 3

Backflow Power Optimization of DAB with Gradient Descent Alg...

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3rd IEEE International Electrical and Energy Conference (CIEEC)

作者： Zhou, Bingkai Yang, Xiaofeng Zhang, Zhi Li, Zejie Zheng, Trillion Q. You, Xiaojie Beijing Jiaotong Univ Sch Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728116754

To reduce the backflow power of dual-active-bridge (DAB) DC-DC converter and improve the operating efficiency, this paper proposes a gradient descent algorithm (GD-EPS) based extended-phase-shift (EPS) control. Firstly, the transmission and backflow power models of DAB converter with EPS and single-phase-shift (SPS) control are established, and the influencing factors of backflow power are analyzed. Secondly, according to the backflow power model, the gradient descent algorithm for solving the optimal solution of backflow power is obtained. On this basis, the output voltage closed-loop control is added to derive the DAB overall optimal control scheme. At last, the simulation models of DAB converter with GD-EPS and SPS control are built and compared, it is verified that the GD-EPS control can optimize the backflow power better than the SPS control when the load and input voltage change.

关键词： dual-active-bridge extended-phase-shift backflow power optimization gradient descent algorithm

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Optimization of industrial energy consumption for sustainability using time-series regression and gradient descent algorithm based on historical electricity consumption data

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Sustainability Analytics and Modeling 2022年 2卷

作者： Richard Opoku George Y. Obeng Louis K. Osei John P. Kizito Department of Mechanical Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana The Brew-Hammond Energy Centre College of Engineering KNUST Kumasi Ghana Technology Consultancy Centre College of Engineering KNUST Kumasi Ghana Bulk Oil Storage and Transportation (BOST) Company Kumasi Ghana North Carolina Agricultural and Technical State University Greensboro United States

Optimizing electricity consumption to minimize wastage and reduce cost is a major challenge in many industries. This is because, in many cases, the effect of the independent variables contributing to the total electricity consumption and cost are latent. The purpose of this study is to apply numerical techniques to identify and optimize these independent variables in order to improve sustainable energy management in industries to minimize wastage. Regression analysis was first applied to identify and decouple the independent variables to determine their individual effects on electricity consumption and cost. A cost function called the Mean Square Error (MSE) was then used to optimize these independent variables using gradient descent algorithm (GDA). In a case study, the developed approach that combines time series regression analysis with gradient descent optimization was used to analyze the electricity consumption data of an oil distribution company for the period 2015 to 2018. The results showed potential electricity savings of 124,684 kWh and cost savings of US$ 25,375 annually, when the facility is operated at optimum parameters of 0.95 power factor, 260 kVA maximum demand and 25,000 kWh active electricity consumption. The novelty of this study is that a procedure that combines time series regression analysis (RA) and gradient descent algorithm (GDA) has been developed and applied to decouple and optimize the independent variables that affect electricity consumption in an industry.

关键词： minimizing industrial energy wastage sustainability regression analysis optimization mean square error gradient descent algorithm

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A gradient descent based algorithm for lp minimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第1期283卷 47-56页

作者： Jiang, Shan Fang, Shu-Cherng Nie, Tiantian Xing, Wenxun Xiamen Univ Sch Management Xiamen 361005 Peoples R China North Carolina State Univ Edward P Fitts Dept Ind & Syst Engn Raleigh NC 27695 USA Univ North Carolina Charlotte Charlotte NC 28223 USA Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

In this paper, we study the linearly constrained l(p) minimization problem with p is an element of (0, 1). Unlike those known works in the literature that propose solving relaxed epsilon-KKT conditions, we introduce a scaled KKT condition without involving any relaxation of the optimality conditions. A gradient-descent-based algorithm that works only on the positive entries of variables is then proposed to find solutions satisfying the scaled KKT condition without invoking the nondifferentiability issue. The convergence proof and complexity analysis of the proposed algorithm are provided. Computational experiments support that the proposed algorithm is capable of achieving much better sparse recovery in reasonable computational time compared to state-of-the-art interior-point based algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Global optimization Nonsmooth optimization Nonconvex optimization gradient descent algorithm KKT condition

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Maximum Torque Per Ampere Control for IPMSM Using gradient descent algorithm Based on Measured Speed Harmonics

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第4期14卷 1424-1435页

作者： Lai, Chunyan Feng, Guodong Mukherjee, Kaushik Tjong, Jimi Kar, Narayan C. Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Indian Inst Engn Sci & Technol Sibpur 711103 Howrah India

This paper proposes a novel gradient descent based maximum torque per ampere (MTPA) control algorithm for interior permanent magnet synchronous machines (IPMSMs) by using the measured speed harmonics. The proposed approach does not require machine parameters and thus is not influenced by the machine and drive nonlinearities. Hence, the proposed approach can ensure a robust MTPA control under different loading conditions. Specifically, in the proposed approach, a small q-axis harmonic voltage is injected into the machine to induce a small harmonic component in the machine speed. Based on the PMSM torque equation, the mathematical relation between the induced speed harmonic and the output torque is derived, which shows that the magnitude of the induced speed harmonic is proportional to the output torque of an IPMSM. Therefore, the speed harmonic is explored to seek the MTPA angle, in which the MTPA angle is found when the speed harmonic magnitude is maximized. In particular, the gradient descent algorithm is employed to detect the MTPA angle, which is computationally efficient and converges quickly. The proposed approach is evaluated with both simulations and experiments based on a laboratory IPMSM drive system.

关键词： gradient descent algorithm interior permanent magnet synchronous machine (IPMSM) maximum torque per ampere (MTPA) control speed harmonic

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Maximum Efficiency Control of PMSM Drives Considering System Losses Using gradient descent algorithm Based on DC Power Measurement

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2018年第4期33卷 2240-2249页

作者： Balamurali, Aiswarya Feng, Guodong Lai, Chunyan Tjong, Jimi Kar, Narayan C. Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

This paper proposes a novel method of efficiency improvement in a vector controlled permanent magnet synchronous motors (PMSM) through system level maximum efficiency point determination using current angle as a control variable. Loss models for the inverter and the motor fundamental and harmonic losses, which are capable of being solved online using available terminal measurements in the system are initially developed. The loss models and dc-link power measurement are then used to seek the maximum efficiency angle for different operating conditions using a gradient descent optimization algorithm. The developed method is robust against changes in inductances due to saturation and cross saturation with loading conditions as well as temperature effects. The effectiveness of the developed method in improving the system efficiency is verified and compared with conventional maximum torque per ampere method. The proposed strategy has been validated on a laboratory interior PMSM, and the efficiency has been calculated for different speed and torque conditions. The experimental validations confirm the effectiveness of the proposed solution in improving the motor drive system energy efficiency.

关键词： Current angle DCpower losses gradient descent algorithm maximum efficiency permanent magnet motor

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gradient descent algorithm for Small UAV Parameter Estimation System

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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第6期34卷 680-687页

作者： Guo Jiandong Liu Qingwen Wang Kang Key Laboratory of Unmanned Aerial Vehicle Technology of the Ministry of Industry and Information Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China

A gradient descent algorithm with adjustable parameter for attitude estimation is developed,aiming at the attitude measurement for small unmanned aerial vehicle(UAV)in real-time flight *** accelerometer and magnetometer are introduced to construct an error equation with the gyros,thus the drifting characteristics of gyroscope can be compensated by solving the error equation utilized by the gradient descent *** of the presented algorithm is evaluated using a self-proposed micro-electro-mechanical system(MEMS)based attitude heading reference system which is mounted on a tri-axis *** on-ground,turntable and flight experiments indicate that the estimation attitude has a good ***,the presented system is compared with an open-source flight control system which runs extended Kalman filter(EKF),and the results show that the attitude control system using the gradient descent method can estimate the attitudes for UAV effectively.

关键词： gradient descent algorithm attitude estimation quaternions small unmanned aerial vehicle(UAV)

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A Robust Quaternion based Kalman Filter Using a gradient descent algorithm for Orientation Measurement

A Robust Quaternion based Kalman Filter Using a Gradient Des...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Discovering New Horizons in Instrumentation and Measurement

作者： Woehle, Lukas Gebhard, Marion Westphalian Univ Appl Sci Elect Engn & Appl Sci Gelsenkirchen Germany

ISBN: (纸本)9781538622223

This paper presents a robust quaternion based filter approach to estimate orientation from Magnetic Angular Rate Gravity (MARG)-sensors. These sensors consist of tri-axis accelerometer and gyroscope as well as a tri-axis magnetometer which allow a complete measurement of orientation relative to the direction of gravity and magnetic field of the earth. The proposed filter uses a gradient descent algorithm (GDA) to compute an orientation from magnetometer and accelerometer data. This calculated orientation is directly used as an input in a Kalman filter framework (KFF) which predicts the orientation estimation from gyroscope data. The embedding of the gradient descent algorithm into the Kalman filter allows the computation of a weighted orientation represented as quaternion. Furthermore, the designed filter can overcome short time magnetic disturbance by switching between MARG and IMU equations inside the gradient descent filter stage (GDFS) and therefore enables a more robust orientation estimation without the need for additional algorithms. Tests show the proposed filter to be superior to a commercially available sensor fusion algorithm related to orientation estimation at slow angular rates. Moreover, the proposed filter is able to maintain good orientation estimation under short term magnetic disturbance.

关键词： sensor fusion orientation estimation magnetic disturbances gradient descent algorithm Kalman filter

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