{'fraction_non_alphanumeric': 0.015373352855051245, 'fraction_numerical': 0.0, 'mean_word_length': 5.904040404040404, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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 noise, and time delays. Conventional modelbased control schemes ignore some of these characteristics, and thus sacrifice the control performance of the system. This paper presents a new model compensation-prediction scheme for control of micromanipulation systems, which estimates the unknown modeling errors from single noisy feedback measurement and predicts and compensate the system time delay. This approach combines two modeling errors into a single equivalent error through mathematical transformation, and estimates the combined term using a noise-insensitive extended high-gain observer (EHGO). 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 simulation and experiments of two typical micromanipulation systems. The results show that this method is able to significantly improve the control performance of a conventional PID controller by simultaneously reducing the settling time and overshoot of the system.