Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Performance Comparative of Impedance Controllers for a Two Degrees of Freedom Robot(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-06) López González, Laura Daniela; Chong Quero, Jesús Enrique; emimmayorquin; Cervantes Culebro, Héctor; Morales Méndez, Rubén; Masters of Science in Engineering; Campus Ciudad de México; Cruz Villar, Carlos AlbertoThis thesis details a new design of a physical Human-Robot Interaction platform for the flexion/extension and pronation/supination rotations of the wrist. It was intended for it to be a compact, easy to manufacture system that is comfortable for each user regardless of whether they are right or left-handed. The robot has been programmed for it to function under three different modes of operations, each employing a specific compliance based controller: impedance, admittance and impedance Maxwell-based models. In order for the three of them to be correctly compared, a study was conducted with a total of 120 people where each individual performed a series of predefined movements, displayed on a screen, using the robot. During the experiments, the torque and trajectory error were sensed and computed in order to find the average error and maximum torque for each controller. Afterwards, in order to describe the human perception of each controller, a survey was carried out and the data collected was analyzed via the Kruskal-Wallis test and a factor analysis. Finally a correlation was found between the quantitative measurements, errors and torque, and the human perception results of the survey.
- Data-driven control of a five-bar parallel robot with compliant joints(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-09-01) Ramírez Martínez, Angel; Chong Quero, Jesús Enrique; emimmayorquin; Cruz Villar, Carlos Alberto; Escuela de Ingeniría y Ciencias; Cervantes Culebro, HéctorThis thesis presents a data-driven approach control to minimize trajectory tracking error of a five-bar robot with compliant joints. Adding compliance in the joints introduces modeling and control challenges due to the flexibility. Traditional model-based methods rely on accurate analytical models of the dynamics, which are difficult to obtain for compliant systems. This motivates a data-driven technique that learns online and offline model directly from experiments using vision-based measurements on the physical robot. The core methodology is divided in online training and offline training. The data-driven controller with online training bases its operation on letting the dynamics of the system run using a controller/compensator, in this case it is used a PID. The online training is based on a Neural Network whose input is the Cartesian position of the end-effector, obtained by the vision-based motion capture system. The Neural Network output is the control signal therefore approximates the inverse dynamic model. With this, it is possible to enhanced the control law of the robot to do tracking error minimization. The offline approach involves collecting time-series data capturing the robot's end-effector Cartesian position while moving in its available workspace. The Cartesian position is also obtained by the vision-based motion capture system. These data, which encapsulate the impact of the compliant joints, are used to train a Neural Network to represent the forward dynamics model. The network maps current state and control law inputs to predictions of the next state. Once trained, this Neural Network model is used by an implementation of Model Predictive Control framework to optimize control laws of the two motors to minimize tracking error of a desired end-effector trajectory. At each control step, a finite horizon optimal control problem is solved to find the control signals that minimizes tracking error over a future window. The Neural Network dynamics model is used to predict the outcomes resulting from candidate control actions. Solving this optimization in receding horizon fashion provides feedback correction to reject disturbances. Online training allows the controller to continuously learn from new data, but it relies on the controller used in order to approximate the dynamic system. Nevertheless, online training requires less compute resources and only one thread of execution. On the other hand offline training allows us to train on a fixed dataset all at once, but the implementation requires the existence of a big enough dataset to train the Neural Network, more computation effort due to the optimization problem solution in each sample time, and in this approximation, two or more execution threads to meet the sampling time proposed. Finally, both implementations are compare in order to clearly identify the advantages and disadvantages of each. Throughout this thesis it is presented two methodologies for data-driven modeling and control of compliant robot systems. These approaches could enhance the capability of next-generation compliant and flexible robots designed for safe human interaction and uncertain environments. Overall, the results validate the feasibility and advantages of data-driven methods for controlling compliant robots.
- Integral operator for chattering reduction in the training of a neural network in the control of a flexible robot(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023) Falcón Romero, Ariadna Carolina; Chong Quero, Jesús Enrique; emimmayorquin; Cruz Villar, Carlos Alberto; School of Engineering and Sciences; Campus Ciudad de México; Cervantes Culebro, HéctorThis thesis work aims to solve the problem of trajectory tracking and chattering attenuation at the end effector of the link of a flexible manipulator robot. Using the Generalized Proportional Integral (GPI) control technique in conjunction with a controller based on Feedforward Neural Network (FNN) trained online, to perform trajectory tracking and reduce chattering at the end effector as a result of flexible dynamics. The controller proposed in this research is divided into a linear control part and a data-driven controller to control the nonlinear dynamics of the system. The linear controller is based on a GPI controller, which uses integral reconstructors so that only the angular position of the manipulator is required to be measured to control the rigid dynamics of the system. The Neural Network based controller is known for its ability to approximate the dynamics of a system. In this case, two FNN trained online are used to approximate the dynamics of the flexible link. Likewise, an integral to the operator of the FNN adaptation algorithm is implemented to minimize the chattering of the end effector. The experimental platform to be used has a single actuator, which moves a single flexible link. The use of a single actuator complicates the control of the angular position of the end effector since the flexible dynamics cause i-th order vibration modes and the actuator has limited bandwidth to attenuate chattering. The advantage of using a single actuator to solve the control problem is that a low material cost is maintained. Increasing another actuator implies increasing its electronic components for control as well as the power stage. The system has a single input, which is the control signal that drives the actuator. On the other hand, the system has two outputs, an angular position signal of the link and an acceleration signal of the end effector of the link. The angular position signal is used to control the position of the link and perform trajectory tracking. While the acceleration signal of the end effector is used to control and compensate for unwanted accelerations caused by external oscillations and disturbances. Throughout this work, the changes made to the experimental platform are presented, as well as different NN to model the system. The contribution of this thesis work is presented, which is the implementation of an integral in the operator of the NN weight adaptation algorithm. The experimental results of a Feedforward Neural Network Module (FNNM) controller and a FNNM controller with the operator's integral are compared. Likewise, to obtain the position of the end effector in space, the results of the motion capture of the flexible manipulator robot of a link with each of the controllers are presented. Finally, the results obtained from the implementation of both controllers are compared and analyzed.