This article presents the development of a dynamic optimization method for controller tuning. This is proposed because most traditional tuning methods for complex coupled dynamic models are based on experience, and thus lacking accuracy. The case study is a Mobile Manipulator that consists of an anthropomorphic manipulator and a differential mobile platform. The system model has a trajectory generator that includes the coupled kinematic model, the Jacobian model, the coupled dynamic model, and a Proportional-Derivative plus controller. The tuning of the model is obtained by solving an optimization problem, using the Differential Evolution algorithm. This optimization approach allows to minimize simultaneously the energy consumption and the error on the trajectory tracking by the end effector. A novel strategy is applied to formulate the objective function, including constant weights for balancing the minimization effect. The objective is to avoid an energy consumption equal to zero that represents an error condition of no motion. The results of the case study and its statistical analysis are presented. The best result was modeled in Solidworks R and simulated in Matlab R. This model was exported to Simscape Multibody TM of Matlab, and its simulation produced satisfactory results, suggesting that the proposed optimization method can be a useful tool to solve real engineering problems.
Este artículo describe el diseño y construcción de un entorno de realidad virtual inmersiva con una secuencia de escenas interactivas que recrean la composición de movimientos de una partícula para instruir el principio de superposición de movimientos en estudiantes de Física en ingeniería. El entorno virtual se ha desarrollado en Unity y permite la interacción por medio de las manos con visualización a través de un Head Mounted Display. Este artículo muestra la etapa de diseño, la programación y las principales características del entorno inmersivo logrado. Para probar el entorno inmersivo final se ha interactuado con una población de 20 estudiantes usando un test de 9 preguntas antes y después del uso del entorno inmersivo en cada estudiante. La ganancia normalizada que produce su uso en el grupo de estudiantes, sin acompañamiento y sin interacción social, es 〈g〉=0.08.
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