Indiana University-Purdue University Indianapolis (IUPUI) / An Adaptive Cruise Control (ACC) system is a driver assistance system that assists a driver to improve driving safety and driving comfort. The design of ACC controller often involves the design of a switching logic that decides where and when to switch between the two modes in order to ameliorate driving comfort, mitigate the chance of a potential collision with the preceding vehicle while reduce long-distance driving load from the driver. In this thesis, a new strategy for designing ACC controller is proposed. The proposed control strategy utilizes Range vs. Range-rate chart to illustrate the relationship between headway distance and velocity difference, and then find out a constant deceleration trajectory on the chart, which the following vehicle is controlled to follow. This control strategy has a shorter elapsed time than existing ones while still maintaining a relatively safe distance during transient process. String stability issue has been addressed by many researchers after the adaptive cruise control (ACC) concept was developed. The main problem is when many vehicles with ACC controller forming a vehicle platoon end to end, how the control algorithm is designed to ensure that the spacing error, which is the deviation of the actual range from the desired headway distance, would not amplify as the number of following vehicles increases downstream along the platoon. In this thesis, string stability issues have been taken into consideration and constraints of parameters of an ACC controller are derived to mitigate steady state error propagation.

M.Ing. (Electrical and Electronic Engineering) / Adaptive Cruise Control (ACC) is proposed to assist drivers tedious manual acceleration or braking of the vehicle, as well as with maintaining a safe headway time gap. This thesis proposes, simulates, and implements a vision-based ACC system which uses a single camera to obtain the clearance distance between the preceding vehicle and the ACC vehicle. A three-step vehicle detection framework is used to obtain the position of the lead vehicle in the image. The vehicle coordinates are used in conjunction with the lane width at that point to estimate the longitudinal clearance range. A Kalman filter filters this range signal and tracks the vehicle’s longitudinal position. Since image processing algorithms are computationally intensive, this document addresses how adaptive image cropping improves the update frequency of the vision-based range sensor. A basic model of a vehicle is then derived for which a Proportional-Integral (PI) controller with gain scheduling is used for ACC. A simulation of the system determines whether the ACC algorithm will work on an actual vehicle.

Orientador: Paulo Sergio Graziano Magalhães / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola / Made available in DSpace on 2018-08-17T11:11:54Z (GMT). No. of bitstreams: 1 Baldo_RodrigoFernandoGalzerano_D.pdf: 7551305 bytes, checksum: 79820cb112b0bd3a361e8fcb752ebfba (MD5) Previous issue date: 2011 / Resumo: Um dos problemas encontrados na colheita mecanizada da cana-de-açúcar e a falta de sincronismo entre a colhedora e o transbordo. Este problema gera perdas tanto de matéria prima como de eficiência operacional. A primeira delas ocorre quando as maquinas ficam desalinhadas e parte dos rebolos de cana-de-açúcar e lançada fora do transbordo. A perda operacional ocorre quando as maquinas se desalinham e são obrigadas a realizar manobras para voltarem a posição de trabalho, estas manobras demandam tempo e por isso representa redução da eficiência da colheita. A presente pesquisa tem por objetivo desenvolver um sistema capaz de identificar e controlar a velocidade e o paralelismo entre a colhedora de cana-de-açúcar picada e o veiculo de transbordo. Com a hipótese de que e possível conseguir sincronismo entre a colhedora e o veiculo de transbordo por meio de controladores baseado em lógica fuzzy, sensores e GPS. Como sistema de controle utilizou-se a lógica fuzzy que foi modelada no "toolbox fuzzy" do MATLAB e simulado no MATLAB Simulink, apresentando erro Maximo de deslocamento entre as maquinas de 0,2 m que corresponde a 0,12% e erro de paralelismo de 5,13% com um offset de 1,5 m. Dessa simulação obteve-se a equação fuzzy e as constantes proporcionais, derivativas e integrativas do controlador que foram utilizados no sistema de controle de velocidade de um trator escravo baseado na velocidade de um mestre. O primeiro trator foi instrumentado com GPS, encoder, transmissor de radiofreqüência e acionamento mecânico de aceleração. Já o segundo foi instrumentado com GPS, encoder e transmissor. Os resultados foram satisfatórios uma vez que a velocidade do trator escravo acompanhou a velocidade do mestre com o erro variando de 0,10% a 2,04% em um deslocamento total médio de 115 m. Para controlar a direção do trator utilizou se o piloto automático modificando as informações enviadas pelo receptor de GPS-RTK de modo que o sistema trabalhe como escravo de outro veiculo chamado de mestre. Para avaliar o tempo de resposta de acomodação do controle de direção, o sistema foi submetido a variações do offset que apresentou respostas entre 7,4 s a 7,9 s. Tanto o sistema de controle de velocidade como o de direção foram testados separadamente em campo / Abstract: One problems of the mechanical harvest of sugar cane is the lack of synchronization between the harvester and field wagon. This problem can causes crop losses as well as reducing operational capacity. The first occurs when the machines are misaligned and part of the sugar cane is thrown out of the wagon. The operational capacity reduce occurring when the machines become misaligned and it is required maneuver to return to working position, these maneuvers take time and therefore represents a reduction of the harvest efficiency. This research aims to develop a system capable of identifying and controlling the speed and parallelism between the sugar cane harvester and the wagon. With the hypothesis that it is possible to synchronize the harvester and the wagon based on logic fuzzy controllers, sensors and GPS. As a control system was used the logic fuzzy that was modeled on the "fuzzy toolbox" of MATLAB Simulink and simulated in MATLAB, with maximum displacement error between the machines of 0.2 m which corresponds to 0.12% and a parallelism error of 5.13% with an offset of 1.5 m. In this simulation, we got the equation and the fuzzy constant as proportional, integrative and derivative that were used in the system to control a slave tractor speed based on the master speed. The first tractor was equipped with GPS, encoder, RF transmitter and mechanical drive acceleration. The second was equipped with GPS, encoder and transmitter. The results were satisfactory since the slave tractor speed followed the master tractor speed with the ranging error from 0.10% to 2.04% in a total displacement of 115 m. To control the direction of the tractor was used the autopilot modifying the information sent by RTK so that the system works as a slave of another vehicle called a master. To evaluate the response time the system was subjected to variations of the offset, the responses was between 7.4 s to 7.9 s. Both the control system speed and steering were tested separately in the field / Doutorado / Maquinas Agricolas / Doutor em Engenharia Agrícola