Control of complex biomechanical systems

Humans show stunning performance on a variety of manipulation tasks. However, little is known about the computation that the human brain performs to accomplish these tasks. Recently, anatomically correct tendon-driven models of the human hand have been developed, but controlling them remains an issue. In this thesis, we present a computationally efficient feedback controller, capable of dealing with the complexity of these models. We demonstrate its abilities by successfully performing tracking and reaching tasks for an elaborated model of the human index finger. The controller, called One-Step-Ahead controller, is designed in a hierarchical fashion, with the high-level controller determining the desired trajectory and the low-level controller transforming it into muscle activations by solving a constrained linear least squares problem. It was proposed to use equilibrium controls as a feedforward command, and learn the controller's parameters online by stabilizing the plant at various configurations. The conducted experiments suggest the feasibility of the proposed learning approach for the index finger model.Science, Faculty ofComputer Science, Department ofGraduat