Abstract
This paper presents an intelligent control strategy to overcome nonlinear and time-varying characteristics of a diaphragm-type pneumatic vibration isolator (PVI) system. By combining an adaptive rule with fuzzy and sliding-mode control, the method has online learning ability when it faces the system's nonlinear and time-varying behaviors during an active vibration control process. Since the proposed scheme has a simple structure, it is easy to implement. To validate the proposed scheme, a composite control which adopts both chamber pressure and payload velocity as feedback signal is implemented. During experimental investigations, sinusoidal excitation at resonance and random-like signal are input on a floor base to simulate ground vibration. Performances obtained from the proposed scheme are compared with those obtained from passive system and PID scheme to illustrate the effectiveness of the proposed intelligent control.
Highlights
Precision instruments such as laser interferometers, electron-beam microscopes and many others applied in the processing of semiconductors are highly sensitive to ground or environmental vibrations.Since high precision is needed at the presence of these apparatus, requirements on the ground vibrationSensors 2013, 13 level have become more stringent in regulations or standards [1,2]
pneumatic vibration isolation (PVI) have found many applications in precision industry, especially in those operating at low-frequency range or supporting heavy static payloads with small dynamic loadings
A pneumatic isolation table system is usually supported by several PVIs which can be actively controlled by servo valves to attenuate vibration transmitted from the floor and suppress any vibration of the table itself
Summary
Precision instruments such as laser interferometers, electron-beam microscopes and many others applied in the processing of semiconductors are highly sensitive to ground or environmental vibrations. The experimental results reported in [4] demonstrated efficient isolation performance with less energy compared to conventional systems that use nozzle-flapper type servo valves, the approach required detailed modeling of the pressure differentiator. The design of a traditional fuzzy controller depends immensely on an expert, or the experience of an operator in establishing the fuzzy rule bank. Knowledge of the latter is generally difficult to obtain. Huang et al [14,15] to monitor the alloying process temperature and suppress vehicle vibration Since this AFSMC approach has learning ability, it can continuously establish and regulate the fuzzy rule bank and parameters. A composite control scheme using both chamber pressure and payload velocity measurements as feedback signals will be implemented
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