This dissertation emphasizes the use of end-effector sensor information for the performance enhancement of indirect drive systems for motion control. Indirect drive systems, unlike direct drive systems, use gear transmission mechanisms in the chain of links and actuator for the purpose of speed reduction and torque amplification. As a result, they are widely used in applications that require high torque capacity such as robotic applications. The gear transmission mechanisms, however, introduce compliance and nonlinear properties such as friction and hysteresis to the system. Moreover, when robots are driven at high speeds to increase productivity and quality, oscillations on the end-effector often occur caused by the transmission mechanism. Thus, the use of gear mechanisms brings great challenges to the design of servo control systems for robot manipulators that requires high precision at high speed.

To enhance the performance of servo control systems for robot manipulators, this dissertation first presents a tuning method that automatically finds the servo gains of fixed structure controllers for a specified trajectory. The current practice is to tune the controller gains manually, which is a time-consuming task even for experienced control engineers. An automated gain tuning process saves not only time but also the labor cost. The tuning method presented in this dissertation finds the optimal controller gains using real-time nonlinear programming. The controller gains are tuned and the effectiveness of the tuning method is demonstrated by experiments. The dissertation then presents an adaptive disturbance cancellation scheme to reduce the oscillations caused by the transmission error from speed reducers. To enhance performance of the adaptive scheme while maintaining a good transient response, two modifications are introduced to the basic compensation structure. Experimental results confirm the effectiveness of the proposed schemes and the improvement in load side performance.

Robots often perform the same task repeatedly in industrial applications and thus the tracking error becomes repetitive from one run to another. Iterative learning control is a practical and promising method that reduces the error which repeats in every cycle. An optimization-based iterative learning controller design for the purpose of disturbance rejection is proposed in the third part of this dissertation. It is a model-based design method, where the trade-off between performance and robustness can be handled. Two iterative learning controllers based on different sensor information are designed and compared. Due to the lack of load side position measurements, a load side position estimation algorithm based on Kalman filtering is proposed. The experimental results are presented to confirm the effectiveness of the estimation scheme and the benefits of applying learning controllers for rejecting load side vibrations.

Experiments of the research issues mentioned above are performed on a single axis test stand. As the first step to generalize the control algorithms developed in this dissertation to actual multi-degree-of-freedom robots, system identification of the FANUC M-16iB robot is presented in the last part of this dissertation.