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.

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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.

In this review article, the most popular types of neural network control systems are briefly introduced and their main features are reviewed. Neuro control systems are defined as control systems in which at least one artificial neural network (ANN) is directly involved in generating the control command. Initially, neural networks were mostly used to model system dynamics inversely to produce a control command which pushes the system towards a desired or reference value of the output (1989). At the next stage, neural networks were trained to track a reference model, and ANN model reference control appeared (1990). In that method, ANNs were used to extend the application of adaptive reference model control, which was a well-known control technique. This attitude towards the extension of the application of well-known control methods using ANNs was followed by the development of ANN model-predictive (1991), ANN sliding mode (1994) and ANN feedback linearization (1995) techniques. As the first category of neuro controllers, inverse dynamics ANN controllers were frequently used to form a control system together with other controllers, but this attitude faded as other types of ANN control systems were developed. However, recently, this approach has been revived. In the last decade, control system designers started to use ANNs to compensate/cancel undesired or uncertain parts of systems' dynamics to facilitate the use of well-known conventional control systems. The resultant control system usually includes two or three controllers. In this paper, applications of different ANN control systems are also addressed.Key Words: Control, neural network, adaptive, feedback linearization, predictive, model reference, perceptron, radial basis. I. ARTIFICIAL NEURAL NETWORKS USABLE IN CONTROLArtificial neural networks (ANNs) are special mathematical models inspired by human neural networks. An ANN usually includes neurons, connections and biases. Neurons are arranged in 'layers'. There are a variety of neural networks suitable for different purposes [1,2]. In neuro control, it is a difficult and unsolved problem to find the best ANN structure for each specific application; thus, a fairly large ANN is usually employed to deal with relatively complex approximation problems [3]. In this paper, the most common types of artificial neural networks in the area of control are introduced: multi layer perceptrons (MLPs) and radial basis function networks (RBFNs). Both of these are known as universal approximators of systems [4,5]. The advantage of RBFNs is their quick training process compared with MLPs; however, for complicated systems with many inputs, the number of neurons in RBFNs are usually considerably higher than MLPs [1,2]. Regardless of the main structure, if the output of an ANN depends not only on the current q

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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