UC Berkeley

Interest in industrial automation by robots has been continuously growing ever since the first industrial robot was installed in 1961. This creates an increasing demand for robot performance enhancement by means of intelligent control. Difficulties in meeting stringent performance requirements, however, arise by some inherent characteristics of the robots, which are: 1) mismatched dynamics (i.e., the control input and the system uncertainty/disturbance are in different channels), and 2) mismatched sensing (i.e., the system lacks of direct sensing of the desired output).

A typical example of mismatched systems is an industrial robot with joint elasticity, the control objective of which is to obtain desired performances of the end-effector such as accurate positioning and tracking. This kind of robot can be characterized as a two-mass system with one mass being the motor side mass with control input and sensing and the other the load side/end-effector mass without control input or sensing, respectively. In this dissertation, several key technologies on intelligent control for this type of robot are introduced, including 1) handling mismatched sensing by sensor fusion, 2) handling mismatched dynamics by real-time control, and 3) handling mismatched dynamics by iterative learning control.

This dissertation develops several sensor fusion algorithms to estimate both the load side and the end-effector states from limited sensing. The dynamic and kinematic models are utilized with consideration to problems such as fictitious noises and computation requirement. The well-developed sensor fusion schemes provide the essential desired information of the system for control purposes. With the mismatched sensing problem solved by sensor fusion, the control approaches to handle the mismatched dynamics are investigated in both the real-time domain and the iteration domain. Considering the nature of the mismatched system as a two-mass system, the real-time and iterative approaches are utilized in a hybrid two-stage manner to deal with the mismatched dynamics. The developed algorithms for sensor fusion and control provide not only the theoretical insights, but also the capability to address the real-world problems especially in industrial automation. Experimental studies on a single-joint research testbed and a commercial 6-DOF industrial robot manipulator have demonstrated the superior performance of the proposed algorithms.