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Relevant bibliographies by topics / Vehicle Control System

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Academic literature on the topic 'Vehicle Control System'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Vehicle Control System"

1

Gaikwad, Anand, Shreya Shreya, and Shivani Patil. "Vehicle Density Based Traffic Control System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 511–14. http://dx.doi.org/10.31142/ijtsrd10938.

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Mahdinia, Iman, Ramin Arvin, Asad J. Khattak, and Amir Ghiasi. "Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 6 (May 31, 2020): 253–67. http://dx.doi.org/10.1177/0361198120918572.

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Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

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Shet, Raghavendra M. "Fault Tolerant Control System for Autonomous Vehicle: A Survey." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (July 30, 2020): 813–30. http://dx.doi.org/10.5373/jardcs/v12sp8/20202585.

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Suda, Yoshihiro. "1K11 Vehicle System Dynamics and Control for Sustainable Transportation." Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _1K11–1_—_1K11–15_. http://dx.doi.org/10.1299/jsmemovic.2010._1k11-1_.

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Yamazaki, Ichiro. "Vehicle vibration control system." Journal of the Acoustical Society of America 98, no. 1 (July 1995): 25. http://dx.doi.org/10.1121/1.413713.

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Ju, Chanyoung, and Hyoung Il Son. "A distributed swarm control for an agricultural multiple unmanned aerial vehicle system." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 233, no. 10 (February 21, 2019): 1298–308. http://dx.doi.org/10.1177/0959651819828460.

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In this study, we propose a distributed swarm control algorithm for an agricultural multiple unmanned aerial vehicle system that enables a single operator to remotely control a multi-unmanned aerial vehicle system. The system has two control layers that consist of a teleoperation layer through which the operator inputs teleoperation commands via a haptic device and an unmanned aerial vehicle control layer through which the motion of unmanned aerial vehicles is controlled by a distributed swarm control algorithm. In the teleoperation layer, the operator controls the desired velocity of the unmanned aerial vehicle by manipulating the haptic device and simultaneously receives the haptic feedback. In the unmanned aerial vehicle control layer, the distributed swarm control consists of the following three control inputs: (1) velocity control of the unmanned aerial vehicle by a teleoperation command, (2) formation control to obtain the desired formation, and (3) collision avoidance control to avoid obstacles. The three controls are input to each unmanned aerial vehicle for the distributed system. The proposed algorithm is implemented in the dynamic simulator using robot operating system and Gazebo, and experimental results using four quadrotor-type unmanned aerial vehicles are presented to evaluate and verify the algorithm.

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Men, Yu Zhuo, Hai Bo Yu, Xian Sheng Li, and Yue Wei Li. "Control Strategy of Vehicle Suspension Damping System Based on MATLAB." Applied Mechanics and Materials 253-255 (December 2012): 2117–20. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.2117.

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In order to study the impact of the suspension damping system on the vehicle riding stability, the PID controlling means for suspension damped neural network is presented, implementing a closed-loop control of yaw stability for vehicles. For the two typical working conditions of single lane changing and step steering, the MATLAB software is used for simulation. The result shows that controlling over the vehicle’s lateral deviation movement through suspension damper, it can reduce significantly the load transfers of both the left wheels and right wheels, so that to effectively restrain a vehicle’s over-steering.

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Kulkarni, Swarup Suresh, and Dr Roshani Ade. "Intelligent Traffic Control System Implementation for Traffic Violation Control, Congestion Control and Stolen Vehicle Detection." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 5, no. 2 (July 6, 2017): 57. http://dx.doi.org/10.3991/ijes.v5i2.7230.

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<p>Traffic is significant issue in our nation, particularly in urban ranges. Aftereffect of this, activity clog issue happens. Crisis vehicle like rescue vehicle, fire unit, squad cars confront bunches of issue to achieve their goal on account of congested driving conditions, coming about loss of human lives. To minimize this issue we approach new idea name as ”Traffic control framework for blockage control and stolen Vehicle location”. In this framework activity freedom done by transforming Red flag into Green flag. We demonstrate idea of what is called ”Green wave”. Alongside this, we distinguish stolen vehicle by utilizing extremely advantageous RFID innovation. In the event that stolen vehicle is been distinguished, the framework gives ready sign through ringer. Framework sends Message with the assistance of GSM to Police station. In this framework we Use diverse RFID labels for recognizing rescue vehicle, stolen Vehicles. On the off chance that Red flag is on and IR sensor is initiated, then framework gives ringer alarm to movement police. This is novel framework which encourage great answer for comprehend traffic clog.</p>

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James, Jesline, and S. Vasanthadev Suryakala. "Advanced vehicle security control and accident alert system." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 404. http://dx.doi.org/10.14419/ijet.v7i2.8.10679.

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Vehicles are becoming smarter by the combining of greater power to compute connectivity solutions and the improvement in software visions. In modern vehicles automotive designs are interfaced with these features. This particular design includes keyless entry system and immobilizer system as the main weapons to prevent the vehicle theft. But these type of systems provide or detect the unauthorized access of vehicles to a measurable limit only. These security frameworks have straightforward also, lacking nature. So car burglary has been a persevering issue far and wide and a greater test from the profficient criminals. This paper proposes an aim to design efficient security control for auto theft prevention system by adding notable enhancement features such as a fingerprint system, password and OTP generating system. It is also included with some rationalizing security features like GPS fencing, remote engine cut-off, and conveying location of vehicle as a message using GSM module. These features are implemented with the help of fingerprint recognition module, GPS Receiver, GSM cellular modem. Along with these feature accident detection module is also added.

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Yadav, Arun K., and Janusz Szpytko. "Safety problems in vehicles with adaptive cruise control system." Journal of KONBiN 42, no. 1 (June 1, 2017): 389–98. http://dx.doi.org/10.1515/jok-2017-0035.

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Abstract In today’s world automotive industries are still putting efforts towards more autonomous vehicles (AVs). The main concern of introducing the autonomous technology is safety of driver. According to a survey 90% of accidents happen due to mistake of driver. The adaptive cruise control system (ACC) is a system which combines cruise control with a collision avoidance system. The ACC system is based on laser and radar technologies. This system is capable of controlling the velocity of vehicle automatically to match the velocity of car, bus or truck in front of vehicle. If the lead vehicle gets slow down or accelerate, than ACC system automatically matches that velocity. The proposed paper is focusing on more accurate methods of detecting the preceding vehicle by using a radar and lidar sensors by considering the vehicle side slip and by controlling the distance between two vehicles. By using this approach i.e. logic for calculation of former vehicle distance and controlling the throttle valve of ACC equipped vehicle, an improvement in driving stability was achieved. The own contribution results with fuel efficient driving and with more safer and reliable driving system, but still some improvements are going on to make it more safe and reliable.

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Dissertations / Theses on the topic "Vehicle Control System"

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Deshpande, Anup S. "Computer Joystick Control and Vehicle Tracking System in Electric Vehicles." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282569869.

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Wu, Tahchang Jimmy. "Simulation and analysis of the control system of the hybrid vehicle." Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182180337.

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Al, Swailem Salah I. "Application of robust control in unmanned vehicle flight control system design." Thesis, Cranfield University, 2004. http://hdl.handle.net/1826/136.

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The robust loop-shaping control methodology is applied in the flight control system design of the Cranfield A3 Observer unmanned, unstable, catapult launched air vehicle. Detailed linear models for the full operational flight envelope of the air vehicle are developed. The nominal and worst-case models are determined using the v-gap metric. The effect of neglecting subsystems such as actuators and/or computation delays on modelling uncertainty is determined using the v-gap metric and shown to be significant. Detailed designs for the longitudinal, lateral, and the combined full dynamics TDF controllers were carried out. The Hanus command signal conditioning technique is also implemented to overcome actuator saturation and windup. The robust control system is then successfully evaluated in the high fidelity 6DOF non-linear simulation to assess its capability of launch stabilization in extreme cross-wind conditions, control effectiveness in climb, and navigation precision through the prescribed 3D flight path in level cruise. Robust performance and stability of the single-point non-scheduled control law is also demonstrated throughout the full operational flight envelope the air vehicle is capable of and for all flight phases and beyond, to severe launch conditions, such as 33knots crosswind and exaggerated CG shifts. The robust TDF control law is finally compared with the classical PMC law where the actual number of variables to be manipulated manually in the design process are shown to be much less, due to the scheduling process elimination, although the size of the final controller was much higher. The robust control law performance superiority is demonstrated in the non-linear simulation for the full flight envelope and in extreme flight conditions.

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Hamersma, H. A. (Herman Adendorff). "Longitudinal vehicle dynamics control for improved vehicle safety." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/40829.

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An autonomous vehicle is a vehicle that is capable of navigating and driving with no human intervention whatsoever through the utilization of various sensors and positioning systems. The possible applications of autonomous vehicles are widespread, ranging from the aerospace industry to the mining and military sectors where the exposure of human operators to the operating conditions is hazardous to their health and safety. Automobile accidents have become the leading cause of death in certain segments of the world population. Removing the human driver from the decision-making process through automation may result in significantly safer highways. Although full autonomy may be the ultimate goal, there is huge scope for systems that aid the driver in decision making or systems that take over from the driver under conditions where the human driver fails. The aim of the longitudinal control system to be implemented on the Land Rover test vehicle in this study is to improve the vehicle’s safety by controlling the vehicle’s longitudinal behaviour. A common problem with sports-utility-vehicles is the low rollover threshold, due to a high centre of gravity. Rather than modifying the vehicle to increase the rollover threshold, the aim of the control system presented here is to prevent the vehicle from exceeding speeds that would cause the vehicle to reach its rollover threshold. In order to develop a control system that autonomously controls the longitudinal degree of freedom, a model of the test vehicle (a 1997 Land Rover Defender 110 Wagon) was developed in MSC.ADAMS/View and validated experimentally. The model accurately captures the response of the test vehicle to supply forces as generated by the engine and demand forces applied through drag, braking and engine braking. Furthermore, the model has been validated experimentally to provide reliable simulation results for lateral and vertical dynamics. The control system was developed by generating a reference speed that the vehicle must track. This reference speed was formulated by taking into account the vehicle’s limits due to lateral acceleration, combined lateral and longitudinal acceleration and the vehicle’s performance capabilities. The control system generates the desired throttle pedal position, hydraulic pressure in the brake lines, clutch position and gear selection as output. The MSC.ADAMS\View model of the test vehicle was used to evaluate the performance of the control system on various racetracks of which the GPS coordinates were available. The simulation results indicate that the control system performs as expected. Finally, the control system was implemented on the test vehicle and the performance was evaluated by conducting field tests in the form of a severe double lane change manoeuvre. The results of the field tests indicated that the control system limited the acceleration vector of the vehicle’s centre of gravity to prescribed limits, as predicted by the simulation results.
Dissertation (MEng)--University of Pretoria, 2013.
gm2014
Mechanical and Aeronautical Engineering
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Azzeh, Abdel Rahman. "CAN Control System for an Electric Vehicle." Thesis, University of Canterbury. Electrical and Computer Engineering, 2007. http://hdl.handle.net/10092/1127.

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The University of Canterbury has purchased a 1992 Toyota MR2 and used it as the platform to construct a new electric car. Similar to the common combustion engine vehicle, electric vehicles require control systems to control the operation of 12Vdc auxiliary loads, such as lights, indicators and windscreen wipers, where traditional technology results in a large number of wires in the wiring harness. Also, with the added complexity of modern vehicles, the need for integrating independent control systems together has become very important in providing safer and more efficient vehicles. To reduce the number of wires and make it possible for different control systems to communicate, and so perform more complex tasks, a flexible and reliable control system is used. The CAN (Controller Area Network) control system is a simple two-wire differential serial bus system, which was developed by Bosch for automotive applications in the early 1980s. The power and control system within the vehicle is named the "Power Distribution Network" and it is implemented by using multiple power converters and the CAN control system. This thesis presents the design, implementation, and test results of the CAN control system for the MR2. The 312Vdc nominal battery voltage is converted to an intermediate voltage of 48Vdc. This configuration is considered more efficient than the usual 12Vdc distribution system since smaller and lighter wires can be used to carry the same amount of power. The power distribution network operates off the 48Vdc intermediate voltage, and provides 12Vdc output to power all auxiliaries within the vehicle. The Power Distribution Network is implemented with two major subsystems: the auxiliary power system, which consists of multiple converters to step-down voltage from the 48Vdc intermediate voltage to the 12Vdc, and the CAN control system, which is developed to control and integrate the 12Vdc auxiliary loads within the vehicle. The prototype CAN control system is fully operational and has been tested with 12Vdc loads which are used to simulate most of the auxiliary loads in the vehicle. Experimental measurements show that the prototype is able to successfully control and maintain the network of independent nodes. This confirms that in principle the CAN control system is suitable for controlling the auxiliary loads in an electric vehicle.

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Yatsko, Margaret Jane. "Development of a Hybrid Vehicle Control System." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1459890202.

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Kirsch, Patricia Jean. "Autonomous swarms of unmanned vehicles software control system and ground vehicle testing /." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2993.

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Thesis (M.S.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Dept. of Electrical and Computer Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Sharma, Aman. "System Identification of a Micro Aerial Vehicle." Thesis, Luleå tekniska universitet, Rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-73070.

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The purpose of this thesis was to implement an Model Predictive Control based system identification method on a micro-aerial vehicle (DJI Matrice 100) as outlined in a study performed by ETH Zurich. Through limited test flights, data was obtained that allowed for the generation of first and second order system models. The first order models were robust, but the second order model fell short due to the fact that the data used for the model was not sufficient.

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Spejcher, Clint. "A comprehensive fleet risk control system for Bill's Distributing." Online version, 1998. http://www.uwstout.edu/lib/thesis/1998/1998spejcherc.pdf.

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Gao, Jianmin. "Control and simulation of an active suspension system." Thesis, University of Wolverhampton, 1997. http://hdl.handle.net/2436/97364.

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Books on the topic "Vehicle Control System"

1

Cope, D. Vehicle emissions control system tampering. Ottawa, Ont: Environment Canada, 1988.

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2

Liu, Wei. Introduction to hybrid vehicle system modeling & control. Hoboken, N.J: Wiley, 2012.

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Liu, Wei. Hybrid Electric Vehicle System Modeling and Control. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119278924.

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Liu, Wei. Introduction to Hybrid Vehicle System Modeling and Control. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118407400.

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Antonelli, Gianluca. Underwater robots: Motion and force control of vehicle-manipulator systems. Berlin: Springer, 2003.

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Zhou, Qihuang. Zhan che huo kong xi tong yu zhi kong xi tong: Fire control system and command control system of combat vehicle. Beijing: Guo fang gong ye chu ban she, 2003.

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Peter, Gaspar, Bokor Jozsef, and SpringerLink (Online service), eds. Robust Control and Linear Parameter Varying Approaches: Application to Vehicle Dynamics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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8

Hallberg, Eric N. Design of a GPS aided guidance, navigation, and control system for trajectory control of an air vehicle. Monterey, Calif: Naval Postgraduate School, 1994.

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9

Onken, Reiner. System-ergonomic design of cognitive automation: Dual-mode cognitive design of vehicle guidance and control work systems. Berlin: Springer, 2010.

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10

Merz, Paul V. Development and testing of the digital control system for the Archytas Unmanned Air Vehicle. Monterey, Calif: Naval Postgraduate School, 1992.

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Book chapters on the topic "Vehicle Control System"

1

Ng, Tian Seng. "Unmanned Aerial Vehicle System." In Flight Systems and Control, 109–18. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8721-9_6.

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Efremov, Alexander. "Pilot-Vehicle System Modeling." In Encyclopedia of Systems and Control, 1071–76. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_22.

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Efremov, Alexander. "Pilot-Vehicle System Modeling." In Encyclopedia of Systems and Control, 1–7. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5102-9_22-1.

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Efremov, Aleksandr. "Pilot-Vehicle System Modeling." In Encyclopedia of Systems and Control, 1–6. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-5102-9_22-2.

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Efremov, Aleksandr. "Pilot-Vehicle System Modeling." In Encyclopedia of Systems and Control, 1739–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_22.

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Song, Changhui. "Semisubmersible Vehicle Autopilot Control System." In Encyclopedia of Ocean Engineering, 1–9. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-6963-5_287-1.

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Imai, Y., Y. Fukumori, R. Hayashi, K. Nakano, and Y. Suda. "Evaluation of coupled suspension system using four electromagnetic dampers by motor HILS system." In Advanced Vehicle Control AVEC’16, 285–92. CRC Press/Balkema, P.O. Box 11320, 2301 EH Leiden, The Netherlands, e-mail: Pub.NL@taylorandfrancis.com, www.crcpress.com – www.taylorandfrancis.com: Crc Press, 2016. http://dx.doi.org/10.1201/9781315265285-46.

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Loof, Jan, Igo Besselink, and Henk Nijmeijer. "In vehicle truck steering-system modeling and validation." In Advanced Vehicle Control AVEC’16, 359–64. CRC Press/Balkema, P.O. Box 11320, 2301 EH Leiden, The Netherlands, e-mail: Pub.NL@taylorandfrancis.com, www.crcpress.com – www.taylorandfrancis.com: Crc Press, 2016. http://dx.doi.org/10.1201/9781315265285-58.

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Yoshioka, T., Y. Takahara, F. Kato, O. Sunahara, T. Tsukano, Y. Takeda, K. Takemura, et al. "Development of G-Vectoring Control system based on engine torque control." In Advanced Vehicle Control AVEC’16, 599–604. CRC Press/Balkema, P.O. Box 11320, 2301 EH Leiden, The Netherlands, e-mail: Pub.NL@taylorandfrancis.com, www.crcpress.com – www.taylorandfrancis.com: Crc Press, 2016. http://dx.doi.org/10.1201/9781315265285-95.

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Tüschen, T., S. Ernst, D. Beitelschmidt, G. Prokop, and W. Tischer. "Control system of a wheel-based “auto.mobile-driving simulator”." In Advanced Vehicle Control AVEC’16, 275–80. CRC Press/Balkema, P.O. Box 11320, 2301 EH Leiden, The Netherlands, e-mail: Pub.NL@taylorandfrancis.com, www.crcpress.com – www.taylorandfrancis.com: Crc Press, 2016. http://dx.doi.org/10.1201/9781315265285-44.

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Conference papers on the topic "Vehicle Control System"

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Hamersma, Herman, and Schalk Els. "The Development of a Longitudinal Control System for a Sports-Utility-Vehicle." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12048.

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A common problem with sports-utility-vehicles is the low rollover threshold, due to a high center of gravity. Instead of modifying the vehicle to increase the rollover threshold, the aim of the control system is to prevent the vehicle from exceeding speeds that would cause the vehicle to reach its rollover threshold. The aim of the autonomous longitudinal control system, discussed here, is to improve the vehicle’s safety by controlling the vehicle’s longitudinal behavior. In order to develop a control system that autonomously controls the longitudinal degree of freedom, an experimentally validated mathematical model of the test vehicle (a 1997 Land Rover Defender 110 Wagon) was used — the model was developed in MSC.ADAMS/View. The control system was developed by generating a reference speed that the vehicle must track. This reference speed was formulated by taking into account the vehicle’s limits due to lateral acceleration, combined lateral and longitudinal acceleration and the vehicle’s performance capabilities. The MSC.ADAMS/View model of the test vehicle was used to evaluate the performance of the control system on various racetracks for which the GPS coordinates were available. The simulation results indicate that the control system performed as expected by limiting the vehicle’s acceleration vector to the prescribed limits.

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Coombes, Matthew, William Eaton, Owen McAree, and Wen-Hua Chen. "Development of a generic network enabled autonomous vehicle system." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915211.

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Tsampardoukas, Georgios, and Alexandros Mouzakitis. "Deployment of full vehicle simulator for electrical control system validation." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334689.

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Xiao, Hansong, Wuwei Chen, Changbao Chu, and Jean W. Zu. "Integrated Control and Coordination of Vehicle System Dynamics." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86096.

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Current and future motor vehicles are incorporating more and more sophisticated chassis control systems to improve vehicle handling, stability and comfort. These control systems often operate independently and thus interactions and performance conflicts among the control systems occur inevitably. To address the problem, this study proposes a two-layer hierarchical control architecture for integrated control of electric power steering (EPS) system and anti-lock brake system (ABS). The upper layer controller is designed to coordinate the interactions between the EPS system and the ABS. While in the lower layer, the two controllers including the EPS system and the ABS, are designed independently to achieve their local control objectives. Simulation results show that the proposed hierarchical control system is able to improve the vehicle lateral stability, and at the same time ensure the vehicle steering agility, and the braking performance.

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Wu, Xing, Peihuang Lou, Qixiang Cai, Chidong Zhou, ke Shen, and chen Jin. "Design and control of material transport system for automated guided vehicle." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334726.

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James, Sebastian, and Sean R. Anderson. "Linear System Identification of Longitudinal Vehicle Dynamics Versus Nonlinear Physical Modelling." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516756.

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Chang, K. S., W. Li, P. Devlin, A. Shaikhbahai, P. Varaiya, J. K. Hedrick, D. McMahon, V. Narendran, D. Swaroop, and J. Olds. "Experimentation with a Vehicle Platoon Control System." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912868.

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Zhang, Wei-Bin. "A Roadway Information System for Vehicle Guidance/Control." In Vehicle Navigation & Instrument Systems. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1991. http://dx.doi.org/10.4271/912867.

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Romer, Richard A. "Vehicle Control System Validation Testing." In International Truck & Bus Meeting & Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1995. http://dx.doi.org/10.4271/952614.

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Liu, Yi, Zhaoye Li, and Xinguo Cui. "New Vehicle Auxiliary Control System." In 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2019. http://dx.doi.org/10.1109/iaeac47372.2019.8997979.

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Reports on the topic "Vehicle Control System"

1

Yamashita, Toshiakik, Takanari Arai, Akihisa Wada, and Eiji Nakamori. Flight Control System for a Mini-Aerial Vehicle. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0286.

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2

Nishira, Hikaru, Yoji Seto, Yoshinori Yamamura, and Taketoshi Kawabe. Research on an Advanced Adaptive Cruise Control System Using Vehicle-to-Vehicle Communication and Vehicle Behavior Prediction. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0295.

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3

Aceves-Saborio, S., and W. J. III Comfort. Load calculation and system evaluation for electric vehicle climate control. Office of Scientific and Technical Information (OSTI), October 1993. http://dx.doi.org/10.2172/10146054.

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4

Smith, Paul G. SIMNET Combat Vehicle Command and Control (CVC2) System User's Guide. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada244217.

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5

Geder, Jason. Onboard Stability Control System for a Flapping Wing Nano Air Vehicle. Fort Belvoir, VA: Defense Technical Information Center, April 2009. http://dx.doi.org/10.21236/ada499960.

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6

Wigginton, Donald, and Lawrence H. O'Brien. Task Analysis for the Combat Vehicle Command and Control (CVCC) System. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada240292.

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7

DDL OMNI ENGINEERING MCLEAN VA. Corrosion Control Options for the U.S. Marine Corps Logistics Vehicle System (LVS). Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada401385.

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8

Leibrecht, Bruce C., Glen A. Meade, Jeffrey H. Schmidt, William J. Doherty, and Carl W. Lickteig. Combat Vehicle Command and Control (93) Technical Report (Draft Final). Evaluation of the Combat Vehicle Command and Control System. Operational Effectiveness of an Armor Battalion. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada282920.

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9

Podhradský, Michal. A Multi-Agent System for Adaptive Control of a Flapping-Wing Micro Air Vehicle. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.3282.

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10

Meade, Glen A., Ryszard Lozicki, Bruce C. Leibrecht, Paul G. Smith, and William E. Myers. The Combat Vehicle Command and Control System. Combat Performance of Armor Battalions Using Interactive Simulation. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada282744.

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