Ciencias Exactas y Ciencias de la Salud

This 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.

Ciencias Exactas y Ciencias de la Salud

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