Ingeniería Mecatrónica (Mag.)

Now showing 1 - 3 of 3

Modeling and track planning for the automation of BMW model car
(Pontificia Universidad Católica del Perú, 2017-06-28) Tabuchi Fukuhara, Rubén Toshiharu; Lin, Shih-Jan; Tafur, Julio
In recent years, autonomous driving technologies have become a topic of growing interest due to the promise of safer and more convenient mode of transportation. An essential element in every autonomous driving system is the control algorithm. Classical control schemes, like PID, are not able to manage Multiple Inputs-Multiple Outputs, complex, non-linear systems. A more recent control strategy is Model predictive control (MPC), a modern control method that has shown promising results in systems with complex dynamics. In MPC, a sequence of optimal control inputs are predicted within a short time horizon based on the car dynamics, and soft or hard restriction of the system. In this work, three different nonlinear-MPC (NMPC) controllers were formulated based on a kinematic, and two dynamic models (double-track and single-track). The steering system’s dynamics were additionally identified using experimental data. Each MPC was solved applying direct methods, by transforming the optimal control problem to a Nonlinear programming (NLP) problem using the Multiple shooting scheme with a Runge-Kutta 4 integrator. The NLPs were solved using the state-of-the-art optimization solver IpOpt. Before the real-time implementation, all the NMPC controllers were simulated in different scenarios and multiple configurations. The results allowed to select the most suitable controllers to be implemented in a 1:5 scale robotic car. Finally, two NMPC controllers based on the kinematic, and the single-track dynamic model were implemented in the robotic car. The algorithms were tested in two different scenarios at the maximum possible speed. The obtained results from the tests were very promising, and provide compelling evidence that MPC could be implemented as the core of future autonomous driving algorithms, since it computes the optimal control inputs, taking in consideration the restrictions inherent to the system.
Reliable autonomous vehicle control - a chance constrained stochastic MPC approach
(Pontificia Universidad Católica del Perú, 2017-06-19) Poma Aliaga, Luis Felipe; Selassie, Abebe Geletu W.; Tafur, Julio C.
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.
Sistema automático de estabilización para un vehículo submarino operado remotamente utilizando visión por computadora
(Pontificia Universidad Católica del Perú, 2013-02-20) Hidalgo Herencia, Franco; Kato Ishizawa, Gustavo
Se presenta el diseño e implementación de un vehículo submarino operado remotamente con un sistema de estabilización automático. El vehículo submarino o ROV, por sus siglas en inglés (Remotly Operated Vehicle), tiene tres grados de libertad que le permiten realizar el movimiento arriba-abajo, adelante-atrás y el giro izquierda-derecha. El sistema de estabilización permite que el ROV se mantenga enfocado a un objetivo predeterminado y pueda seguirlo gracias a técnicas de visión por computadora que determinan la distancia y orientación del objetivo y, a un controlador de lógica difusa que gobierna un sistema de propulsión a chorro direccionado por un sistema de transmisión.

Ingeniería Mecatrónica (Mag.)

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