A Model Compensation-Prediction Scheme for Control of Micromanipulation Systems With a Single Feedback Loop

Many micromanipulation systems employ sensorless actuators and possess unknown modeling errors, feedback measurement noises, and time delays. Conventional model-based control schemes ignore some of these uncertainties, and thus sacrifice the control system performance. This paper presents a new model compensation-prediction scheme for micromanipulation systems that can be described by two-dimensional state-space models, estimate the unknown modeling errors from noisy single feedback measurement, and predict and compensate the system time delay. This approach combines two modeling errors into a single equivalent modeling error through mathematical transformation, and estimates the combined term using a noise-insensitive extended high-gain observer. After removing the unknown term, the system is then transformed into a time-invariant form, and a Smith predictor is implemented to predict and compensate the time delay. The effectiveness of the proposed compensation-prediction scheme is demonstrated by both numerical simulations and experiments on two typical micromanipulation systems, namely a robotic biosample stimulator and a material characterization microgripper. The results show that this method can significantly improve the control performance of a conventional proportional-integral-derivative controller, by simultaneously reducing the settling time and overshoot of the micromanipulation systems.