Ingeniería de Control y Automatización

Permanent URI for this collectionhttp://98.81.228.127/handle/20.500.12404/767

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search.filters.applied.f.advisor: search.filters.advisor.Morán Cárdenas, Antonio ManuelSubject: search.filters.subject.Control automático

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Now showing 1 - 4 of 4
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    Sintonización de un controlador PID utilizando algoritmos genéticos aplicada a una planta concentradora de cobre
    (Pontificia Universidad Católica del Perú, 2021-05-04) Martínez Ordoñez, Renato Javier; Morán Cárdenas, Antonio Manuel
    El objetivo principal de esta tesis es establecer un método para el modelamiento del proceso en un lazo cerrado de control PID (Controlador Proporcional integrativo y derivativo) y con este poder encontrar los parámetros óptimos usando sintonización basada en algoritmos genéticos. En primer lugar, se explica cuál es la problemática que actualmente se tiene en la industria para realizar la sintonización de los lazos de control PID y se detalla el estado del arte de la sintonización de los controladores PID. En segundo lugar, se evalúa cual es el método de identificación en lazo cerrado que mejor representa la respuesta real del proceso de inyección de agua cruda al cajón de alimentación de las bombas de ciclones. En tercer lugar, se realiza la sintonía basada en algoritmos genéticos con el modelo obtenido con la identificación y se evalúa cual es la función de aptitud más adecuada para poder encontrar los parámetros del controlador PID. Finalmente, se presenta los resultados de la sintonización del controlador PID obtenidos para el proceso de inyección de agua cruda al cajón de alimentación de las bombas de ciclones y el proceso de control de nivel de espuma de una celda de flotación Rougher.
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    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, Siegbert
    The 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.
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    High performance implementation of MPC schemes for fast systems
    (Pontificia Universidad Católica del Perú, 2016-06-22) Correa Córdova, Max Leo; W. Selassie, Abebe Geletu; Morán Cárdenas, Antonio Manuel
    In recent years, the number of applications of model predictive control (MPC) is rapidly increasing due to the better control performance that it provides in comparison to traditional control methods. However, the main limitation of MPC is the computational e ort required for the online solution of an optimization problem. This shortcoming restricts the use of MPC for real-time control of dynamic systems with high sampling rates. This thesis aims to overcome this limitation by implementing high-performance MPC solvers for real-time control of fast systems. Hence, one of the objectives of this work is to take the advantage of the particular mathematical structures that MPC schemes exhibit and use parallel computing to improve the computational e ciency. Firstly, this thesis focuses on implementing e cient parallel solvers for linear MPC (LMPC) problems, which are described by block-structured quadratic programming (QP) problems. Speci cally, three parallel solvers are implemented: a primal-dual interior-point method with Schur-complement decomposition, a quasi-Newton method for solving the dual problem, and the operator splitting method based on the alternating direction method of multipliers (ADMM). The implementation of all these solvers is based on C++. The software package Eigen is used to implement the linear algebra operations. The Open Message Passing Interface (Open MPI) library is used for the communication between processors. Four case-studies are presented to demonstrate the potential of the implementation. Hence, the implemented solvers have shown high performance for tackling large-scale LMPC problems by providing the solutions in computation times below milliseconds. Secondly, the thesis addresses the solution of nonlinear MPC (NMPC) problems, which are described by general optimal control problems (OCPs). More precisely, implementations are done for the combined multiple-shooting and collocation (CMSC) method using a parallelization scheme. The CMSC method transforms the OCP into a nonlinear optimization problem (NLP) and de nes a set of underlying sub-problems for computing the sensitivities and discretized state values within the NLP solver. These underlying sub-problems are decoupled on the variables and thus, are solved in parallel. For the implementation, the software package IPOPT is used to solve the resulting NLP problems. The parallel solution of the sub-problems is performed based on MPI and Eigen. The computational performance of the parallel CMSC solver is tested using case studies for both OCPs and NMPC showing very promising results. Finally, applications to autonomous navigation for the SUMMIT robot are presented. Specially, reference tracking and obstacle avoidance problems are addressed using an NMPC approach. Both simulation and experimental results are presented and compared to a previous work on the SUMMIT, showing a much better computational e ciency and control performance.
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    Diseño de un sistema de control neural para el monitoreo y control de calidad en una columna de destilación de multicomponentes
    (Pontificia Universidad Católica del Perú, 2013-12-05) Dávila Tapia, Segundo Feliberto; Morán Cárdenas, Antonio Manuel
    Los sistemas de destilación, desde hace muchas décadas, vienen siendo ampliamente usados en la industria de procesos químicos, especialmente en refinerías y procesos de acondicionamiento y tratamiento de gas natural. Los objetivos típicos en estos sistemas están asociados al cumplimiento de especificaciones sobre la calidad de los productos, y para lo cual usualmente se cuenta con analizadores online para monitoreo de estas especificaciones como es el caso de cromatógrafos, así como análisis en laboratorio mediante técnicas específicas.