Show simple item record

dc.contributor.advisorSelassie, Abebe Geletu W.es_ES
dc.contributor.advisorTafur, Julio C.es_ES
dc.contributor.authorPoma Aliaga, Luis Felipees_ES
dc.date.accessioned2017-06-19T22:33:50Zes_ES
dc.date.available2017-06-19T22:33:50Zes_ES
dc.date.created2017es_ES
dc.date.issued2017-06-19es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12404/8834
dc.description.abstractIn recent years, there is a growing interest in the development of systems capable of performing tasks with a high level of autonomy without human supervision. This kind of systems are known as autonomous systems and have been studied in many industrial applications such as automotive, aerospace and industries. Autonomous vehicle have gained a lot of interest in recent years and have been considered as a viable solution to minimize the number of road accidents. Due to the complexity of dynamic calculation and the physical restrictions in autonomous vehicle, for example, deterministic model predictive control is an attractive control technique to solve the problem of path planning and obstacle avoidance. However, an autonomous vehicle should be capable of driving adaptively facing deterministic and stochastic events on the road. Therefore, control design for the safe, reliable and autonomous driving should consider vehicle model uncertainty as well uncertain external influences. The stochastic model predictive control scheme provides the most convenient scheme for the control of autonomous vehicles on moving horizons, where chance constraints are to be used to guarantee the reliable fulfillment of trajectory constraints and safety against static and random obstacles. To solve this kind of problems is known as chance constrained model predictive control. Thus, requires the solution of a chance constrained optimization on moving horizon. According to the literature, the major challenge for solving chance constrained optimization is to calculate the value of probability. As a result, approximation methods have been proposed for solving this task. In the present thesis, the chance constrained optimization for the autonomous vehicle is solved through approximation method, where the probability constraint is approximated by using a smooth parametric function. This methodology presents two approaches that allow the solution of chance constrained optimization problems in inner approximation and outer approximation. The aim of this approximation methods is to reformulate the chance constrained optimizations problems as a sequence of nonlinear programs. Finally, three case studies of autonomous vehicle for tracking and obstacle avoidance are presented in this work, in which three levels probability of reliability are considered for the optimal solution.es_ES
dc.description.uriTesises_ES
dc.language.isoenges_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Perú*
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.sourcePontificia Universidad Católica del Perúes_ES
dc.sourceRepositorio de Tesis - PUCPes_ES
dc.subjectControl predictivoes_ES
dc.subjectControl automáticoes_ES
dc.subjectVehículoses_ES
dc.titleReliable autonomous vehicle control - a chance constrained stochastic MPC approaches_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ES
thesis.degree.nameMagíster en Ingeniería Mecatrónicaes_ES
thesis.degree.levelMaestríaes_ES
thesis.degree.grantorPontificia Universidad Católica del Perú. Escuela de Posgrado.es_ES
thesis.degree.disciplineIngeniería Mecatrónicaes_ES


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record