Nanopositioning systems driven by piezoelectric actuators are widely used in different fields. However, the hysteresis phenomenon is a major factor in reducing the positioning accuracy of piezoelectric actuators. This effect makes the task of accurate modeling and position control of piezoelectric actuators challenging. In this paper, the learning and generalization capabilities of the model are efficiently enhanced to describe and compensate for the rate-independent and rate-dependent hysteresis using a kernel-based learning method. The proposed model is inspired by the classical Preisach hysteresis model, in which a set of hysteresis operators is used to address the problem of mapping, and then least-squares support-vector machines (LSSVM) combined with a particle swarm optimization (PSO) algorithm are used for identification. Two control schemes are proposed for hysteresis compensation, and their performance is evaluated through real-time experiments on a nanopositioning platform. First, an inverse model-based feedforward controller is designed based on the LSSVM model, and then a combined feedback/feedforward control scheme is designed using a classical control strategy (PID) to further enhance the tracking performance. For performance evaluation, different datasets with a variety of hysteresis loops are used during the simulation and experimental procedures. The results show that the proposed method is successful in enhancing the generalization capabilities of LSSVM training and achieving the best tracking performance based on the combination of feedforward control and PID feedback control. The proposed control scheme outperformed the inverse Preisach model-based control scheme in terms of both positioning accuracy and execution time. The control scheme that uses the LSSVM based on nonlinear autoregressive exogenous (NARX) models has significantly less computational complexity compared to our control scheme but at the expense of accuracy.
Electricity theft is a common problem in electric power systems around the world. It causes heavy economic losses and badly affects the reliability of the power grid. One of the most common and simplest methods of stealing electricity is tapping energy directly from the overhead power feeder. The other most common method of theft is the tampering with meters to reduce the recorded consumption by illegal ways. In this paper, we present a cost-effective remote detection and identification method for detecting illegal electricity consumption. It also identifies the illegal user in real time without any pre-processing or extensive analysis of a huge amount of collected data. Moreover, it preserves the privacy of customers by destroying the high-resolution data of instantaneous power consumption collected from customers' meters. The system can detect suspicious consumer(s) online and sends notifications to the utility control center with the ID number(s) of the suspicious meter(s) or the amount of load that has been tapped to the power feeder within the area served by a single distribution transformer. The extensive simulations using Simulink were conducted to validate the proposed scheme. For further validation of the scheme, hardware-in-the-loop (HIL) simulation was conducted using three microcontroller-based meters and Simulink environment. The results of both types of experiments showed that the proposed scheme can successfully detect and identify fraudulent users in real time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.