SUMMARY
Many machines---from hydraulic excavators to mobile wheelchairs---are manually controlled by a human operator. In practice, the operator assumes responsibility for completing a given task at maximum utility, for example in minimum time or with the least energy consumption, while unaware of the true optimal command inputs. This thesis discusses a simple technique termed Blended Shared Control, whereby the human operator's commands are merged with the commands of an electronic agent in real time. This approach is shown analytically to result in a lower task completion time than manual control alone when applied to the control of a four-DOF hydraulic manipulator. A hydraulic excavator is used as an application example, and two types of models are presented: a fully dynamic model incorporating the actuator and linkage systems suitable for high-fidelity user studies, and a reduced-order velocity-constrained kinematic model amenable for real-time optimization. Intended operator tasks are estimated with a recursive algorithm. Experimental results compare Blended Shared Control to other types of controllers including manual control and haptic feedback. Trials indicate that Blended Shared Control decreases task completion time when compared to manual operation.