Academic literature on the topic 'Self-balancing vehicle'
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Journal articles on the topic "Self-balancing vehicle"
Ajitha, S. P. "Two Wheeled Self Balancing Vehicle." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (January 31, 2018): 824–32. http://dx.doi.org/10.22214/ijraset.2018.1126.
Full textQi, Ben Sheng, Kang Wang, Xuan Xuan Xiao, and Hong Xia Miao. "Design and Implementation of Self-Balancing Electric Vehicle Control System." Applied Mechanics and Materials 738-739 (March 2015): 950–54. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.950.
Full textLiu, Yunping, Xijie Huang, Tianmiao Wang, Yonghong Zhang, and Xianying Li. "Nonlinear dynamics modeling and simulation of two-wheeled self-balancing vehicle." International Journal of Advanced Robotic Systems 13, no. 6 (November 16, 2016): 172988141667372. http://dx.doi.org/10.1177/1729881416673725.
Full textLi, Huanping, Jian Wang, Guopeng Bai, and Xiaowei Hu. "Research on Self-Balancing System of Autonomous Vehicles Based on Queuing Theory." Sensors 21, no. 13 (July 5, 2021): 4619. http://dx.doi.org/10.3390/s21134619.
Full textDai, Min, Jian Wang, Xiao Gang Sun, Shuang Hu, and Jun Xiang Jia. "Design and Implementation of the Control System for Two-Wheeled Self-Balancing Vehicles." Advanced Materials Research 588-589 (November 2012): 1606–10. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1606.
Full textMaddahi, A., A. H. Shamekhi, and A. Ghaffari. "A Lyapunov controller for self-balancing two-wheeled vehicles." Robotica 33, no. 1 (March 5, 2014): 225–39. http://dx.doi.org/10.1017/s0263574714000307.
Full textVu, Ngoc Kien, and Hong Quang Nguyen. "Design Low-Order Robust Controller for Self-Balancing Two-Wheel Vehicle." Mathematical Problems in Engineering 2021 (May 24, 2021): 1–22. http://dx.doi.org/10.1155/2021/6693807.
Full textShabana, Ahmed A. "Geometric self-centering and force self-balancing of railroad-vehicle hunting oscillations." Acta Mechanica 232, no. 8 (May 25, 2021): 3323–29. http://dx.doi.org/10.1007/s00707-021-02983-w.
Full textXu, Jun, Shi Shang, Guizhen Yu, Hongsheng Qi, Yunpeng Wang, and Shucai Xu. "Are electric self-balancing scooters safe in vehicle crash accidents?" Accident Analysis & Prevention 87 (February 2016): 102–16. http://dx.doi.org/10.1016/j.aap.2015.10.022.
Full textGao, Mei Xia, and Jian Pu Bai. "The Research of Self-Balancing Vehicle Based on Posture Sensor System." Applied Mechanics and Materials 599-601 (August 2014): 735–38. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.735.
Full textDissertations / Theses on the topic "Self-balancing vehicle"
CHOQUEHUANCA, CESAR RAUL MAMANI. "DESIGN AND ROBUST CONTROL OF A SELF-BALANCING PERSONAL ROBOTIC TRANSPORTER VEHICLE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17228@1.
Full textNesta dissertação, um transportador pessoal robótico auto-equilibrante (TPRE) foi desenvolvido, consistindo de uma plataforma com duas rodas que funciona a partir do equilíbrio do indivíduo que o utiliza, assemelhando-se ao funcionamento do clássico pêndulo invertido. Entre as características que o TPRE tem, podem-se destacar a rapidez na movimentação, o uso de um espaço reduzido, alta capacidade de carga, e capacidade de fazer curvas de raio nulo. Ao contrário de veículos motorizados tradicionais, o TPRE utiliza alimentação elétrica, portanto não gera emissões poluentes e, além disso, não contribui com poluição sonora. Para a locomoção, são utilizados dois motores de corrente contínua de potências entre 0,7HP e 1,6HP. Para medir o ângulo de inclinação e a velocidade da variação do ângulo de inclinação, é utilizado um acelerômetro de três eixos e um girômetro de um eixo. Para indicar a direção do TPRE, foi utilizado um potenciômetro deslizante. A modelagem dinâmica do sistema foi feita usando o método de Kane, utilizada posteriormente em simulações na plataforma Matlab. O controlador lê os sinais provenientes do acelerômetro, do girômetro e do potenciômetro deslizante, e envia o sinal de controle, em forma de PWM, a placas controladoras de velocidade dos motores, usando a linguagem eLua. Os algoritmos de controle desenvolvidos neste trabalho foram PID, Fuzzy e Robusto, tendo como variáveis de controle o erro e a velocidade da variação do erro do ângulo de inclinação. Experimentos demonstram que os controles Fuzzy e Robusto reduzem significativamente as oscilações do sistema em terrenos planos em relação ao PID. Verifica-se também uma maior estabilidade para terrenos irregulares ou inclinados.
A Self Balancing Personal Transporter (SBPT) is a robotic platform with two wheels that functions from the balance of the individual who uses it, resembling the operation of classic inverted pendulum. In this thesis, a SBPT is designed, built and controlled. Among the features from the developed SBPT, it can be mentioned: relatively high speeds, agility, compact aluminum structure, zero turn radius, and high load capacity, when compared to other SBPT in the market. Unlike traditional motor vehicles, the SBPT uses electric power, so there is no polluent emissions to the environment and no noise pollution. It is powered by two motors with output powers between 0.7HP and 1.6HP. To measure the tilt angle and its rate of change, a three-axis accelerometer and a gyroscope are used. The turning commands to the SBPT are sent through a potentiometer attached to the handle bars. The method of Kane is used to obtain the system dynamic equations, which are then used in Matlab simulations. The controller, programmed in eLua, reads the signals from the accelerometer, gyroscope and potentiometer slider, process them, and then sends PWM output signals to the speed controller of the drive motors. This thesis studies three control implementations: PID, Fuzzy and Robust Control. The control variables are the error and error variation of the tilt angle. It is found that the Fuzzy and Robust controls are more efficient than the PID to stabilize the system on inclined planes and on rough terrain.
Matějásko, Michal. "Návrh bezpečného řídicího systému pro dvoukolové balancující vozidlo." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232043.
Full textDobossy, Barnabás. "Odhad parametrů jezdce na vozítku segway a jejich použití pro optimalizaci řídícího algoritmu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-399406.
Full textWu, Ying-Te, and 吳應德. "Development of a self-balancing controller for two wheeled carrying vehicle." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/31270856737946255250.
Full text聖約翰科技大學
電機工程系碩士班
99
In this thesis, a self-balancing fuzzy controller for the two wheeled carry vehicle is presented. From the opening condition of the vehicle, the fuzzy control rules were adapted and implemented in triangular membership function. And then used product inference engine to obtain the control effort of each fuzzy control rule. Finally the resulted control effort was synthesized by the center average defuzzification method. The aforementioned fuzzy controller was implemented in an embedded system which consists of Sunplustm spce061A and Altera EPM7128SLC84, furthermore the sensor and motor drive control system were also implemented in the embedded system. It provide a total solution for a two wheeled carry system. From the experimented results showed the propose fuzzy controller can achieve balancing control in normal operating condition.
Chen, Yu-Ching, and 陳雨慶. "System Design and Control Analysis of a Novel Two Wheel Self- Balancing Vehicle." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/8vbw59.
Full text遠東科技大學
機械工程研究所
105
In recent years, due to technological progress leads to environmental pollution and global warming and is becoming increasingly serious. In order to reduce this phenomenon, the demand of electrical vehicle is gradually increasing for transportation. Among all the electric type vehicles, one kind called the two-wheeled balancing vehicle is a pollution-free and convenient personal transportation. However, it takes a lot of efforts and attention for the driver to control the balance of the vehicle especially in the initial learning phase. The reason why such type of vehicle requires so much effort is that it requires the delicate simultaneous motors torque control to maintain the balance of the vehicle. However, such system characteristic will increases the burden of the driver as well as the control difficulty. Therefore, in order to improve the drawback of the traditional two wheel balancing robot designed, a novel design is proposed and studied in this thesis. The new type of active center of gravity to adjust the two wheel self balancing robot to replace the human center of gravity and maintain vehicle balance, first of all to calculate a new initiative with the center of gravity to adjust the two-wheel balance vehicle dynamic model design belongs to the study used in the controller, dynamic model itself determined by the nonlinear system after it must first be converted to a linear system to facilitate subsequent design, originally designed in the traditional smooth modal lead vehicle into a non chatter chatter formula cis Sliding state to solve the problem of vehicle flutter during walking and finally to simulate the actual way to test this study design The effectiveness of the control system. From the results of the study of the new design with the initiative to adjust the two wheel balancing robot is indeed the same as the general vehicles can drive and road, it will be able to significantly reduce the original two wheel balancing robot of the problems brought about by the original.
Wang, Jung-Chang, and 汪榮章. "Intelligent Algorithm Design by Using Fuzzy Inference on Two-Wheeled Self-Balancing Vehicle." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/jshf9f.
Full text臺北市立大學
資訊科學系
105
Based on the merits of flexibility and small occupied volumes of two-wheeled self-balancing vehicles (TWSBV), some of the practicable applications are widespread in miscellaneous fields. In this thesis, by imposing adapting control on TWSBV with different transition states, we propose three kinds of fuzzy controllers with standing state, moving state and loaded state and further verify their efficiency. For the TWSBV can equip adaptive controller corresponding to the moving and the varying weight of carried goods, we involve a fuzzy rule base to controller design. Based on adapting control mechanism of fuzzy control, we can not only achieve the stability but also reduce the amplitude of shaking for different transition states of TWSBV. For fuzzy rules of the fuzzy control system, we use the experimentally measuring parameters to construct the required rules with membership functions, where there expects to attain that the transitions in different states including standing, moving, loaded-standing and loaded-moving, have good performance. To achieve fuzzy control on TWSBV, we then carry out the control algorithm in the platform and analyze the obtained results. By comparison, it show that the proposed method has better performance than the traditional PID controller during all transition states of standing balance, moving balance and loaded balance.
Cao, Jia-Rui, and 曹家瑞. "Stabilizing Controller Design Using Fuzzy T-S Model on Two Wheeled Self-Balancing Vehicle." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/75376825038740777417.
Full text臺北市立大學
資訊科學系
104
In this research, we proposed a controller design method of the Two Wheeled Self-Balancing Vehicle (TWSBV) based on Fuzzy T-S Model (T-S Fuzzy) associated with genetic algorithm (GA). To achieve the stable controller of TWSBV, we used GA to properly seek for the feasible state feedback gains for the T-S Fuzzy controller, which is constructed by the heuristic experiment with two fuzzy membership functions of vehicle body angle and vehicle angular velocity to conquer some nonlinear parameters. With the convergent state feedback gains via GA, we can ensure the TWSBV has the better performances. Through analyzing the TWSBV impulse response characteristics produced from the state-space equation of TWSBV dynamic model, the system’s performance of the controllers can be evaluated. The fitness function of genetic algorithm is formulated by some performance indexes so that we can search for better state feedback gains. Furthermore, using GA’s ability of natural selection can reduce the tuning time of the satisfying state feedback gains. By the well-tuned state feedback gains, we thus can achieve a fuzzy model controller of the TWSBV system. The experimental simulation demonstrates that the proposed method has less balancing time and states’ oscillation, which mean that it indeed has better performance than others.
SUN, GUO-GANG, and 孫國剛. "The cost-effective GPS Guided Autonomous Vehicle - A Feasibility Study based on Self-balancing Scooter." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/kp3667.
Full text國立聯合大學
電子工程學系碩士班
105
The GPS guided autonomous vehicle seems to be more and more popular in recent years. Not only for military and science purposes, but also for civil use. There were many events which catched people’s eyes, for example, the Google’s self-driving car project(2009~) and the nuTonomy’s self-driving taxi(2016~). On the other hand, due to the booming of mobile device, the GPS related function has been built in almost every smartphone (or pad). That is to say, with a suitable app installed, your phone becomes a capable GPS navigation device. In some recent researches, the smartphone has become the core control device of autonomous vehicle. Based on this idea, we have implemented a GPS guided autonomous vehicle based on Self-balancing Scooter with the least effort on mechanical modification. Using the smartphone as the main control device, the autonomous vehicle have accomplished the dedicated function: automated parcel delivery in campus. Eventually, we hope to suggest a cost-effective approach for developing autonomous vehicle.
LU, NAN-YAN, and 呂南鴈. "Upper Cover Mold flow Analysis of Different Cooling Types for Two-Wheel Self-Balancing Vehicle." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/68894633438501391600.
Full text高苑科技大學
機械與自動化工程研究所
105
The analysis of the object is to do simulation analysis upper cover mold flow analysis of different cooling types for two-wheel self-balancing vehicle, the material we used is PIM CAE CAE-MIM-002 instead of MIM Catamold 316 LA in Moldex3D material library, so we used this material to discuss. Using PIM CAE CAE-MIM-002 to research packing, warpage, thermal stress and total displacement is this paper analysis. The Moldex3D simulation analysis can effectively predict a defect formed from material we choose, and reduce production cost, reduce production time and increase reliability.
SU, PO YUAN, and 蘇鉑原. "Lower Cover Mold flow Analysis of Different Cooling Types for Two-Wheel Self-Balancing Vehicle." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/71096211216872618075.
Full text高苑科技大學
機械與自動化工程研究所
105
The analysis of the object is to do simulation analysis lower cover mold flow analysis of different cooling types for two-wheel self-balancing vehicle, the material we used is PIM CAE CAE-MIM-002 instead of MIM Catamold 316 LA in Moldex3D material library, so we used this material to discuss. Using PIM CAE CAE-MIM-002 to research packing, warpage, thermal stress and total displacement is this paper analysis. The Moldex3D simulation analysis can effectively predict a defect formed from material we choose, and reduce production cost, reduce production time and increase reliability.
Book chapters on the topic "Self-balancing vehicle"
Wang, Wenqing, Yuan Yan, Ruyue Zhang, Li Zhang, Hongbo Kang, and Chunjie Yang. "Design of Self-balancing Vehicle System." In Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications, 700–707. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03766-6_79.
Full textPatil, Omkar, Sujay Jadhav, and R. Ramakrishnan. "Development of Reaction Wheel Controlled Self-Balancing Bicycle for Improving Vehicle Stability Control." In Advances in Automotive Technologies, 187–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5947-1_15.
Full textBabu, Sunu S., and Anju S. Pillai. "Design and Implementation of Two-Wheeled Self-Balancing Vehicle Using Accelerometer and Fuzzy Logic." In Advances in Intelligent Systems and Computing, 45–53. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2526-3_6.
Full textGrepl, R. "Model Based Design of a Self-balancing Vehicle: A Mechatronic System Design Case Study." In Mechatronics 2013, 869–76. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02294-9_110.
Full textHashimoto, Naohisa, Kohji Tomita, Akiya Kamimura, Yusuke Takinami, and Osamu Matsumoto. "Application for a Personal Mobility Sharing System Using Two-Wheeled Self-balancing Vehicles." In Internet of Things. IoT Infrastructures, 157–62. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19743-2_24.
Full textMa, Siyuan, Man Wang, Chunye Du, and Yang Zhao. "Measure of Compatibility Based Angle Computing for Balanced Posture Control on Self-balancing Vehicles." In Intelligent Computing Methodologies, 551–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42297-8_51.
Full textConference papers on the topic "Self-balancing vehicle"
Kumar, Sooraj, Vijaydeep, and Pooja Gupta. "Self-balancing vehicle using Kalman Filter." In 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2017. http://dx.doi.org/10.1109/iccsp.2017.8286635.
Full textMohtasib, A. M., and M. H. Shawar. "Self-balancing two-wheel electric vehicle (STEVE)." In 2013 9th International Symposium on Mechatronics and its Applications (ISMA). IEEE, 2013. http://dx.doi.org/10.1109/isma.2013.6547384.
Full textRiattama, Desna, Eko Henfri Binugroho, Raden Sanggar Dewanto, and Dadet Pramadihanto. "PENS-wheel (one-wheeled self balancing vehicle) balancing control using PID controller." In 2016 International Electronics Symposium (IES). IEEE, 2016. http://dx.doi.org/10.1109/elecsym.2016.7860971.
Full textChen, Chia-Hong, Jong-Hann Jean, and Dao-Xiang Xu. "Application of fuzzy control for self-balancing two-wheel vehicle." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016900.
Full textLujun, Wang, and Xu Yong. "Design of Motor Driver for a Two-Wheeled Self Balancing Vehicle." In 2010 International Conference on Optoelectronics and Image Processing (ICOIP). IEEE, 2010. http://dx.doi.org/10.1109/icoip.2010.192.
Full textLi, Jiayi, Min Yan, and Jianjun Zhu. "RESEARCH ON SELF BALANCING VEHICLE CONTROL BASED ON KINECT HUMAN POSTURE RECOGNITION." In International Conference on New Materials and Intelligent Manufacturing (ICNMIM). Volkson Press, 2018. http://dx.doi.org/10.26480/icnmim.01.2018.130.132.
Full textRamadhan, Bakhtiar, Eko Henfri Binugroho, Raden Sanggar Dewanto, and Dadet Pramadihanto. "PENS-wheel (self balancing one-wheel vehicle) mechanical design and sensor system." In 2016 International Electronics Symposium (IES). IEEE, 2016. http://dx.doi.org/10.1109/elecsym.2016.7861046.
Full textAbdullah Bin Azhar, M., Waseem Hassan, and Usman Rahim. "PID control behavior and sensor filtering for a self balancing personal vehicle." In 2012 International Conference on Robotics and Artificial Intelligence (ICRAI). IEEE, 2012. http://dx.doi.org/10.1109/icrai.2012.6413419.
Full textSostaric, D., G. Martinovic, and D. Zagar. "GPS tracking of self-balancing vehicle for extreme environment based on AndroidOS." In 2012 20th Telecommunications Forum Telfor (TELFOR). IEEE, 2012. http://dx.doi.org/10.1109/telfor.2012.6419393.
Full textCiezkowski, Maciej, and Ewa Pawluszewicz. "Determination of interactions between two-wheeled self-balancing vehicle and its rider." In 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR ). IEEE, 2015. http://dx.doi.org/10.1109/mmar.2015.7283988.
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