The growing need in industrial applications of high-performance robots has led to designs of lightweight robot arms. However the light-weight robot arm introduces accuracy and vibration problems. The classical robot design and control method based on the rigid body assumption is no longer satisfactory for the light-weight manipulators. The effects of flexibility of light-weight manipulators have been an active research area in recent years. A new approach to correct the quasi-static position and orientation error of the end-effector of a manipulator with flexible links is studied in this project. In this approach, strain gages are used to monitor the elastic reactions of the flexible links due to the weight of the manipulator and the payload in real time, the errors are then compensated on-line by a control algorithm. Although this approach is designed to work for general loading conditions, only the bending deflection in a plane is investigated in detail. It is found that a minimum of two strain gages per link are needed to monitor the deflection of a robot arm subjected to bending. A mathematical model relating the deflections and strains is developed using Castigliano's theorem of least work. The parameters of the governing equations are obtained using the identification method. With the identification method, the geometric details of the robot arms and the carrying load need not be known. The deflections monitored by strain gages are fed back to the kinematic model of the manipulator to find the position and orientation of the end-effector of the manipulator. A control algorithm is developed to compensate the deflections. The inverse kinematics that includes deflections as variables is solved in closed form. If the deflections at target position are known, this inverse kinematics will generate the exact joint command for the flexible manipulator. However the deflections of the robot arms at the target position are unknown ahead of time, the current deflections at each sampling time are used to predict the deflections at target position and the joint command is modified until the required accuracy is obtained. An experiment is set up to verify the mathematical model relating the strains to the deflections. The results of the experiment show good agreement with the model. The compensation control algorithm is first simulated in a computer program. The simulation also shows good convergence. An experimental manipulator with two flexible links is built to prove this approach. The experimental results show that this compensation control improves the position accuracy of the flexible manipulator significantly. The following are the brief advantages of this approach: the deflections can be monitored without measuring the payload directly and without the detailed knowledge of link geometry~ the manipulator calibrates itself with minimum human intervention; the compensation control algorithm can be easily integrated with the existing uncompensated rigid-body algorithm~ it is inexpensive and practical for implementation to manipulators installed in workplaces.

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To cope with dynamics and uncertainties, a novel penalty function-based hybrid, multi-agent shop floor control system is proposed in this dissertation. The key characteristic of the proposed system is the capability of adaptively distributing decision-making power across different levels of control agents in response to different levels of disturbance. The subordinate agent executes tasks based on the schedule from the supervisory level agent in the absence of disturbance. Otherwise, it optimizes the original schedule before execution by revising it with regard to supervisory level performance (via penalty function) and disturbance. Penalty function, mathematical programming formulations, and quantitative metrics are presented to indicate the disturbance levels and levels of autonomy. These formulations are applied to diverse performance measurements such as completion time related metrics, makespan, and number of late jobs. The proposed control system is illustrated, tested with various job shop problems, and benchmarked against other shop floor control systems. In today's manufacturing system, man still plays an important role together with the control system Therefore, better coordination of humans and control systems is an inevitable topic. A novel BDI agent-based software model is proposed in this work to replace the partial decision-making function of a human. This proposed model is capable of 1) generating plans in real-time to adapt the system to a changing environment, 2) supporting not only reactive, but also proactive decision-making, 3) maintaining situational awareness in human language-like logic to facilitate real human decision-making, and 4) changing the commitment strategy adaptive to historical performance. The general purposes human operator model is then customized and integrated with an automated shop floor control system to serve as the error detection and recovery system. This model has been implemented in JACK software; however, JACK does not support real-time generation of a plan. Therefore, the planner sub-module has been developed in Java and then integrated with the JACK. To facilitate integration of an agent, real-human, and the environment, a distributed computing platform based on DOD High Level Architecture has been used. The effectiveness of the proposed model is then tested in several scenarios in a simulated automated manufacturing environment.