| Teleoperation holds a promising future as humans push the limits of technology by allowing human presence in otherwise hostile or remote environments. This thesis examines specifically teleoperation with the use of force reflecting haptic devices as it pertains to robotic surgery. Neural networks operate online to learn the unknown system dynamics and provide a completely adaptive control design. There are three novel contributions made in this thesis. First, a unique error definition allows for force control in constrained motion while permitting position control in unconstrained motion. Secondly, the backstepping technique smoothes out control signals and ensures that high frequency vibrations of the robot/environment dynamics are not excited by the proposed controller. Finally, a novel supervisory neural network update law ensures fast convergence of neural network weights and improves robustness. The entire system is shown to be globally Lyapunov stable. Using the Lyapunov redesign method a robust control law is also derived. |
