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You searched for +publisher:"Pontificia Universidad Católica del Perú" +contributor:("Tafur, Julio C."). One record found.

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1. Poma Aliaga, Luis Felipe. Reliable autonomous vehicle control - a chance constrained stochastic MPC approach.

Degree: 2017, Pontificia Universidad Católica del Perú

In 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.

Tesis

Advisors/Committee Members: Selassie, Abebe Geletu W., Tafur, Julio C..

Subjects/Keywords: Control predictivo; Control automático; Vehículos

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APA (6th Edition):

Poma Aliaga, L. F. (2017). Reliable autonomous vehicle control - a chance constrained stochastic MPC approach. (Masters Thesis). Pontificia Universidad Católica del Perú. Retrieved from http://tesis.pucp.edu.pe/repositorio/handle/123456789/8834

Chicago Manual of Style (16th Edition):

Poma Aliaga, Luis Felipe. “Reliable autonomous vehicle control - a chance constrained stochastic MPC approach.” 2017. Masters Thesis, Pontificia Universidad Católica del Perú. Accessed March 21, 2019. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8834.

MLA Handbook (7th Edition):

Poma Aliaga, Luis Felipe. “Reliable autonomous vehicle control - a chance constrained stochastic MPC approach.” 2017. Web. 21 Mar 2019.

Vancouver:

Poma Aliaga LF. Reliable autonomous vehicle control - a chance constrained stochastic MPC approach. [Internet] [Masters thesis]. Pontificia Universidad Católica del Perú 2017. [cited 2019 Mar 21]. Available from: http://tesis.pucp.edu.pe/repositorio/handle/123456789/8834.

Council of Science Editors:

Poma Aliaga LF. Reliable autonomous vehicle control - a chance constrained stochastic MPC approach. [Masters Thesis]. Pontificia Universidad Católica del Perú 2017. Available from: http://tesis.pucp.edu.pe/repositorio/handle/123456789/8834

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