This dissertation presents a model-based approach to perform system integration of a novel positioning sensing method, termed \u27Direct Position Sensing.\u27 Direct Position Sensing can actively monitor the planar position changes of motion control devices without the dependency of the conventional position sensor combined with kinematic model to estimate the planar position. Instead, Direct Position Sensing uses the technology of computer vision and digital display to directly monitor the planar position displacement of a motion control device by actively tracking the desired position of the device based on the displayed target showed on the digital screen. The integration of the computer vision as the feedback system to the motion controller, introduces intermittency and latency in the controller\u27s feedback loop. In order to integrate the slower computer vision sensor to the motion controller, a model-based controller architecture, Smith Predictor approach was first implemented to the Direct Position Sensing system. The Smith Predictor uses a mathematical plant model that is running in parallel with the actual plant so that the model predicts the plant output when the actual output of the system is unavailable. Due to the intermittency feedback of the system, a path prediction algorithm was developed to minimize the model residual during the intermittent feedback so that the tracking performance of the system can be improved. Furthermore, a model input corrector was also developed to correct the control action to the plant model based on the model residual to enhance the path prediction. Simulations and hardware experiments results show that the model-based strategy provides improved tracking performance of the system when latency and intermittency exist in the controller feedback loop
