Tesis y Trabajos de Investigación PUCP
URI permanente para esta comunidadhttp://54.81.141.168/handle/123456789/6
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Ítem Texto completo enlazado Diseño e implementación de un sistema de control óptimo preview para la posición de una esfera sobre un plano(Pontificia Universidad Católica del Perú, 2020-01-16) Huamaní Gabriel, Samir Josué; Sotomayor Moriano, Juan JavierEl control óptimo preview se define como un control predictivo desde el enfoque de control óptimo; esto quiere decir que su funcionamiento se basa en el conocimiento de los valores futuros de la referencia, de tal manera que usa estos valores en la formulación de la ley de control; por ende, tiene un desempeño superior al control proporcional integral derivativo (PID), y también superior que el control óptimo general. En este trabajo se desarrolla un modelamiento matemático de una planta esfera sobre un plano utilizando como herramientas los análisis eléctricos, mecánico, físico, cinemático y dinámico, con el principal objetivo de conseguir una serie de ecuaciones analíticas de la planta y de esa manera poder comprender las principales variables que intervienen en dicho modelo. A partir del modelo obtenido, se elabora un controlador avanzado óptimo preview de posición y trayectoria, para luego realizar un análisis comparativo entre dicho controlador avanzado variando el tiempo preview y otro controlador convencional proporcional integral derivativo para el control de posición de una esfera sobre un plano. Se consiguió simular con éxito el control la posición, trayectoria definida y una trayectoria circular mediante el controlador avanzado diseñado, obteniendo resultados favorables a comparación con el controlador convencional proporcional integral derivativo.Ítem Texto completo enlazado Design of a mobile robot’s control system for obstacle identification and avoidance using sensor fusion and model predictive control(Pontificia Universidad Católica del Perú, 2017-10-14) Barreto Guerra, Jean Paul; Morán Cárdenas, Antonio Manuel; Hopfgarten, SiegbertThe aim of this master thesis is to design a control system based on model predictive control (MPC) with sensor data fusion for obstacle avoidance. Since the amount of obtained data is larger due to multiple sensors, the required sampling time has to be larger enough in comparison with the calculation time of the optimal problem. Then it is proposed a simplification of the mobile robot model in order to reduce this optimization time. The sensor data fusion technique uses the range information of a laser scanner and the data of a mono-camera acquired from image processing techniques. In image processing different detection algorithms are proposed such as shape and color detection. Therefore an estimation of the obstacles dimension and distance is explained obtaining accurate results. Finally a data fusion for obstacle determination is developed in order to use this information in the optimization control problem as a path constraint. The obtained results show the mobile robot behavior in trajectories tracking and obstacle avoidance problems by comparing two different sampling times. It is concluded that the mobile robot reaches the final desired position while avoiding the detected obstacles along the trajectory.Ítem Texto completo enlazado 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.Ítem Texto completo enlazado Implementation of a high performance embedded MPC on FPGA using high-level synthesis(Pontificia Universidad Católica del Perú, 2017-06-19) Araujo Barrientos, Antonio; Geletu, Abebe; Villota Cerna, ElizabethModel predictive control (MPC) has been, since its introduction in the late 70’s, a well accepted control technique, especially for industrial processes, which are typically slow and allow for on-line calculation of the control inputs. Its greatest advantage is its ability to consider constraints, on both inputs and states, directly and naturally. More recently, the improvements in processor speed have allowed its use in a wider range of problems, many involving faster dynamics. Nevertheless, implementation of MPC algorithms on embedded systems with resources, size, power consumption and cost constraints remains a challenge. In this thesis, High-Level Synthesis (HLS) is used to implement implicit MPC algo- rithms for linear (LMPC) and nonlinear (NMPC) plant models, considering constraints on both control inputs and states of the system. The algorithms are implemented in the Zynq@ -7000 All Programmable System-on-a-Chip (AP SoC) ZC706 Evaluation Kit, targeting Xilinx’s Zynq@-7000 AP SoC which contains a general purpose Field Programmable Gate Array (FPGA). In order to solve the optimization problem at each sampling instant, an Interior-Point Method (IPM) is used. The main computation cost of this method is the solution of a system of linear equations. A minimum residual (MINRES) algorithm is used for the solution of this system of equations taking into consideration its special structure in order to make it computationally efficient. A library was created for the linear algebra operations required for the IPM and MINRES algorithms. The implementation is tested on trajectory tracking case studies. Results for the linear case show good performance and implementation metrics, as well as computation times within the considered sampling periods. For the nonlinear case, although a high computation time was needed, the algorithm performed well on the case study presented. Because of resources constraints, implementation of the nonlinear algorithm on higher order systems was precluded.Ítem Texto completo enlazado Desarrollo de un sistema de control predictivo multivariable de un generador de vapor de tubos de agua(Pontificia Universidad Católica del Perú, 2014-06-03) Gonzales Lecaros, Sergio Nicolás; Morán Cárdenas, Antonio ManuelPartiendo de la motivación de buscar medios que permitan el ahorro de energía tanto por el aspecto económico como el ecológico se desarrolló este trabajo el cual pretende diseñar un controlador predictivo basado en modelo (CPBM) para controlar un generador de vapor de tubos de agua de forma más efectiva y eficiente que los sistemas actuales Para este fin se realizó una revisión del estado del arte de los generadores de vapor y de sus sistemas de control donde se identificaron las principales variables a controlar. Debido al bajo desempeño de estos sistemas de control se propuso, luego de un análisis previo, el uso de un controlador predictivo basado en modelo para su aplicación en el generador de vapor. Para lograr este objetivo se estudió un modelo matemático no lineal multivariable de un generador de vapor reportado en la literatura, el cual posteriormente se utilizó para realizar la simulación de la planta real. Luego para el diseño del controlador se utilizó el modelo linealizado con el fin de aligerar cálculos. El diseño del controlador multivariable está basado en un controlador predictivo que es computacionalmente más eficiente que el controlador predictivo convencional. Para la aplicación de este controlador se consideraron restricciones en la señal de control y durante las pruebas simuladas en Matlab/Simulink se le introdujo señales ruidosas y perturbaciones alcanzando buenos resultados en eficiencia energética y de control superando al sistema actual basado en controladores PID. Finalmente se propuso la implementación práctica del controlador haciendo uso de un DSP hibrido.