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You searched for +publisher:"Pontificia Universidad Católica del Perú" +contributor:("Selassie, Abebe Geletu W."). Showing records 1 – 2 of 2 total matches.

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1. Tam Tapia, Augusto José. Space craft reliable trajectory tracking and landing using model predictive control with chance constraints.

Degree: 2017, Pontificia Universidad Católica del Perú

This work considers the study of chance constrained Model Predictive Control (MPC) for reliable spacecraft trajectory tracking and landing. Objectives of the master thesis: • To identify and study mathematical dynamic models of a spacecraft. • To study the trajectory design and landing schemes for a given mission. • To study the source of uncertainty in the model parameters and external disturbances. • To study the chance constrained MPC scheme for the reliable and optimal trajectory tracking and landing. • To testing the new analytic approximation approaches, Inner and Outer, for chance constraints. • To study appropriate MPC algorithms and implement on case-studies. In the first part of the thesis considers deterministic dynamical models of spacecraft are discussed. The first example is about the tracking of trajectory and soft landing on the surface of an asteroid EROS433, this model uses Cartesian coordinates. In the second example, in a similar way to the first example, the trajectory and soft landing is performed on the surface of a celestial body. It is assumed that the celestial body is a perfect sphere, something that does not happen in the first example. Thus, the second example uses a Spherical coordinate system. The third example is about a Lander that enters the Martian atmosphere. This Lander follows a designed trajectory until reaching a certain altitude over the Martian surface. At this altitude the Lander deploys a parachute to make the landing. To solve the deterministic examples described above, the following sequence of steps are: • pose the deterministic Nonlinear Optimal Control Problem (NOCP), • convert the infinite Optimal Control Problem (OCP) to a finite Nonlinear Programming Problem (NLP), applying the Runge-Kutta 4th order discretization method, • apply the Quasi-sequential method to the deterministic NLP obtained from the previous step, • solution of the reduced NLP obtained from the previous step using IpOpt software. The steps outlined above are also part of the Nonlinear Model Predictive Control (NMPC) approach. In the second part of the thesis, the same examples of the first part are used but now with stochastic variables. To find the control law in each model, the stochastic NMPC was used. The above mentioned approach begins with a chance constrained OCP. The latter is discretized obtaining an NLP. The problem with this NLP, with chance constraints, is that is very difficult to solve in analytic form. So these chance constraints are approached by a different method that exist in the state of the art. This thesis work is focused on approaching the chance constraints through Analytic Approximation Strategies, specifically by the recent: Inner and Outer Approximation methods. The chance constrained MPC is expensive from a computational point of view, but it allows to find a control law for a more reliable trajectory-tracking and soft landing . That is suitable for applications with random disturbances, model inaccuracies, and measurement… Advisors/Committee Members: Selassie, Abebe Geletu W..

Subjects/Keywords: Modelos matemáticos; Vehículos espaciales; Navegación

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tam Tapia, A. J. (2017). Space craft reliable trajectory tracking and landing using model predictive control with chance constraints. (Masters Thesis). Pontificia Universidad Católica del Perú. Retrieved from http://tesis.pucp.edu.pe/repositorio/handle/123456789/8897

Chicago Manual of Style (16th Edition):

Tam Tapia, Augusto José. “Space craft reliable trajectory tracking and landing using model predictive control with chance constraints.” 2017. Masters Thesis, Pontificia Universidad Católica del Perú. Accessed March 22, 2019. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8897.

MLA Handbook (7th Edition):

Tam Tapia, Augusto José. “Space craft reliable trajectory tracking and landing using model predictive control with chance constraints.” 2017. Web. 22 Mar 2019.

Vancouver:

Tam Tapia AJ. Space craft reliable trajectory tracking and landing using model predictive control with chance constraints. [Internet] [Masters thesis]. Pontificia Universidad Católica del Perú 2017. [cited 2019 Mar 22]. Available from: http://tesis.pucp.edu.pe/repositorio/handle/123456789/8897.

Council of Science Editors:

Tam Tapia AJ. Space craft reliable trajectory tracking and landing using model predictive control with chance constraints. [Masters Thesis]. Pontificia Universidad Católica del Perú 2017. Available from: http://tesis.pucp.edu.pe/repositorio/handle/123456789/8897

2. 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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 22, 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. 22 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 22]. 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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