Piezoelectric actuators are commonly used for nanopositioning due to their high resolution, low power consumption and wide operating frequency, but they suffer hysteresis, which affects linearity. In this paper, a novel digital charge amplifier is presented. Results show that hysteresis is reduced by 91% compared with a voltage amplifier, but over long operational periods the digital charge amplifier approach suffers displacement drift. A non-linear ARX model with long-term accuracy is used with a data fusion algorithm to remove the drift. Experimental results are presented.

Piezoelectric actuators are the most commonly used actuators in nanopositioning. They provide excellent operating bandwidth and precision. These actuators are normally driven by voltage amplifiers; however, the relationship of the applied voltage on the actuator and its position is nonlinear and complex due to phenomena such as hysteresis. This adversely influences actuator’s position control which is the core component of nanopositioning. However, the electrical charge applied on a piezoelectric actuator has a significantly less complex relationship with its position. As a result, driving piezoelectric actuators with charge amplifiers has attracted significant attention. In this article, charge driving methods are classified, and their advantages, major issues and the solutions to these issues are critically reviewed.

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In this paper the concept of Control Inertia is introduced and based on this concept, unexpectedly inadequate control behaviour of High Control Inertia systems is explained. Fuzzy compensators are then suggested to improve the control behaviour. This work is in the area of non‐model‐based control. In order to indicate the merit of the proposed technique, a neuro‐predictive (NP) control is designed and implemented on a highly non‐linear system, a lab helicopter, in a constrained situation. It is observed that the behaviour of the closed loop system under the NP controller either displays considerable function (with a low value of a particular design parameter) or is very slow (with high values of the same design parameter). In total, the control behaviour is very poor in comparison to existing fuzzy controllers, whereas NP is used effectively in the control of some other systems. Considering the concept of Control Inertia, a Sugeno‐type fuzzy compensator was added to the control loop to modify the control command. A newly designed neuro‐predictive control with fuzzy compensator (NPFC) improves the performance of the closed loop system significantly by the reduction of both overshoot and settling time. Furthermore, it is shown that the disturbance rejection of the NPFC controlled system as well as it parameter robustness is satisfactory. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

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