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El objetivo de la investigación es plantear una propuesta de mejora para una pequeña empresa dedicada a la fabricación de muebles de madera y melamine, localizada en la ciudad de Lima. Se seleccionó el sector de la carpintería debido al creciente requerimiento de este tipo de productos, evidenciado en el aumento del 6.6% de la demanda de muebles en el país (Cámara Peruana de la Construcción; SUNAT, 2015). Además, tras el análisis del sector y de acuerdo al PBI del Perú se observó una disminución de este indicador en el rubro de actividades relacionadas con la madera y muebles (BCRP, 2014). Con estos dos alcances se puede concluir que este sector tiene una baja capacidad de respuesta para la demanda existente en el mercado, lo cual define el principal problema encontrado. Por otro lado, la informalidad y la falta de capacitación en todos los niveles de cada una de las organizaciones que pertenecen al sector, amplifican el impacto del problema. La propuesta de mejora que se plantea a continuación será elaborada con la implementación de herramientas de Lean Manufacturing y Conceptos de Planificación de Operaciones cuya aplicación en conjunto logrará los siguientes objetivos: 1. Organización de la planta en cuanto a recursos. 2. Planificación de la producción de acuerdo a la capacidad de la planta. 3. Aumento de la capacidad de planta. 4. Reducción de los tiempos de inspección. Finalmente, tras el análisis económico se puede concluir que la propuesta es viable pues se obtienen indicadores como el VAN= S/. 27,808.19 y la TIR= 28.4% positivos y mayores a la inversión realizadas y al WACC de la empresa respectivamente. El periodo de evaluación del proyecto ha sido de dos años, siendo el tiempo de retorno de la inversión 13 meses. / Tesis

El cultivo bajo invernadero permite crear un microclima dentro de un espacio cerrado y proteger los cultivos de fenómenos climáticos derivados del ambiente externo. Tradicionalmente, estos invernaderos son operados manualmente por el agricultor en base a su experiencia. Sin embargo, existen sistemas automatizados que facilitan el monitoreo y control de las condiciones climáticas de acuerdo a las necesidades del cultivo. En este trabajo se propone el diseño de un sistema automatizado para el monitoreo y control de humedad de un invernadero que se construirá en el distrito de Abelardo Pardo Lezameta, ubicado en el departamento de Áncash. Para tal fin se planteó una estrategia de control que permita establecer un valor adecuado de humedad dentro del invernadero mediante un algoritmo de control on/off que ejecute una de las dos etapas de humidificación y deshumidificación, de acuerdo a la medición del sensor de humedad. Se usó el sensor de humedad DHT22 que provee al sistema diseñado de un rango de operación de 0-100% y una resolución de +/-2%. Además, se seleccionaron actuadores que permitan nebulizar y ventilar el invernadero para variar la humedad, se usó una electroválvula que permita el pase del agua hacia los nebulizadores, así mismo se usaron tres motores para la apertura y cierre de las ventanas del invernadero. Por otro lado, se desarrolló una interfaz de usuario, la cual sirva para monitorear los cambios de la variable controlada en una computadora, así como también operar los actuadores remotamente. Se realizaron pruebas y simulaciones del sistema diseñado, las cuales demostraron que el sensor de humedad elegido puede usarse para este tipo de aplicaciones, obteniendo mediciones de humedad de una manera rápida y sencilla. Por otro lado, se logró establecer la comunicación serial entre el sistema de control y la computadora a través de la interfaz de usuario implementada. La interfaz de usuario desarrollada ayuda a facilitar el control y monitoreo del parámetro climático dentro del invernadero. Todo esto permitirá dar autonomía al invernadero. / Tesis

Estando el Perú ubicado en una zona con alta probabilidad de ocurrencia de movimientos telúricos, resulta necesario el uso de maquinaria que permita remover escombros y equipos para casos donde debido a un movimiento sísmico se tenga como resultado el derrumbe de viviendas o instalaciones industriales. Debido a que el ingreso a estas zonas constituye un peligro e implicaría el riesgo del personal a quedar sepultado bajo un derrumbe, resultaría conveniente tener maquinaria operada con mando a distancia que minimice los posibles daños personales. Asimismo un control autónomo del sistema de carga y descarga de material permitiría una rápida limpieza del área, facilitando al operador la ejecución de su trabajo reduciendo los efectos de la baja visibilidad. Para realizar este proyecto se requerirá del uso de cilindros hidráulicos con sensores de posición que determinen la altura y ángulo de ataque del cucharón del equipo para permitir reproducir el proceso de carga y descarga de forma autónoma usando para ello un controlador difuso. La realización del presente estudio permitirá implementar el mando a distancia para la operación de equipos que utilizan sistemas hidráulicos para realizar funciones tales como desplazamiento, giro, frenado así como la carga y descarga de material. Esta implementación contribuirá elevar la seguridad en la operación de equipos en zonas de alto riesgo. / Tesis

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

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. / Tesis

Hu Guoqiang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 87-93). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Nonlinear Control Systems --- p.2 / Chapter 1.2 --- Output Regulation --- p.5 / Chapter 1.3 --- Semiglobal Stabilization --- p.7 / Chapter 1.4 --- A Benchmark Nonlinear Control Problem --- p.8 / Chapter 1.5 --- Contribution of this Thesis --- p.10 / Chapter 2 --- Semiglobal Robust Output Regulation of a Class of Nonlinear Systems via Output Feedback Control --- p.12 / Chapter 2.1 --- Introduction --- p.13 / Chapter 2.2 --- Semiglobal Backstepping Technique --- p.16 / Chapter 2.3 --- Output Regulation Converted to Stabilization --- p.18 / Chapter 2.4 --- Solvability of the Semiglobal Robust Stabilization Problem via Partial State Feedback --- p.23 / Chapter 2.5 --- Design of the Output Feedback Regulator --- p.35 / Chapter 2.6 --- An example --- p.39 / Chapter 2.7 --- Concluding Remarks --- p.46 / Chapter 3 --- Disturbance Rejection of the RTAC system --- p.50 / Chapter 3.1 --- Disturbance Rejection Problem Formulated into Output Regulation Problem --- p.51 / Chapter 3.2 --- Solvability of the Output Regulation Problem via Measurement Output Feedback Control --- p.53 / Chapter 3.3 --- Parameters Design and Simulation Results --- p.57 / Chapter 3.4 --- Concluding Remarks --- p.58 / Chapter 4 --- Robust Disturbance Rejection of the RTAC System --- p.63 / Chapter 4.1 --- Introduction --- p.63 / Chapter 4.2 --- A General Framework for Robust Output Regulation --- p.64 / Chapter 4.3 --- Robust Asymptotic Disturbance Rejection of the RTAC System --- p.69 / Chapter 4.4 --- Algorithms to Design and Optimize the Parameters Kx and L --- p.73 / Chapter 4.5 --- Parameters design and Simulation Results --- p.75 / Chapter 4.6 --- Concluding Remarks --- p.76 / Chapter 5 --- Conclusions --- p.86 / Biography --- p.87 / Bibliography --- p.88 / Appendix A. ITAE Prototype Design --- p.94

Models of dynamical systems are important in many disciplines of science, ranging from physics and traditional mechanical and electrical engineering to life sciences, computer science and economics. Engineers, for example, use models for development, analysis and control of complex technical systems. Dynamical models can be derived from physical insights, for example some known laws of nature, (which are models themselves), or, as considered here, by fitting unknown model parameters to measurements from an experiment. The latter approach is what we call system identification. A model is always (at best) an approximation of the true system, and for a model to be useful, we need some characterization of how large the model error is. In this thesis we consider model errors originating from stochastic (random) disturbances that the system was subject to during the experiment. Stochastic model errors, known as variance-errors, are usually analyzed under the assumption of an infinite number of data. In this context the variance-error can be expressed as a (complicated) function of the spectra (and cross-spectra) of the disturbances and the excitation signals, a description of the true system, and the model structure (i.e., the parametrization of the model). The primary contribution of this thesis is an alternative geometric interpretation of this expression. This geometric approach consists in viewing the asymptotic variance as an orthogonal projection on a vector space that to a large extent is defined from the model structure. This approach is useful in several ways. Primarily, it facilitates structural analysis of how, for example, model structure and model order, and possible feedback mechanisms, affect the variance-error. Moreover, simple upper bounds on the variance-error can be obtained, which are independent of the employed model structure. The accuracy of estimated poles and zeros of linear time-invariant systems can also be analyzed using results closely related to the approach described above. One fundamental conclusion is that the accuracy of estimates of unstable poles and zeros is little affected by the model order, while the accuracy deteriorates fast with the model order for stable poles and zeros. The geometric approach has also shown potential in input design, which treats how the excitation signal (input signal) should be chosen to yield informative experiments. For example, we show cases when the input signal can be chosen so that the variance-error does not depend on the model order or the model structure. Perhaps the most important contribution of this thesis, and of the geometric approach, is the analysis method as such. Hopefully the methodology presented in this work will be useful in future research on the accuracy of identified models; in particular non-linear models and models with multiple inputs and outputs, for which there are relatively few results at present. / QC 20100810