In order to study the hysteresis nonlinear characteristics of piezoelectric actuators, a novel hybrid modeling method based on Long-Short-Term Memory (LSTM) and Nonlinear autoregressive with external input (NARX) neural networks is proposed. First, the input–output curve between the applied voltage and the produced angle of a piezoelectric tip/tilt mirror is measured. Second, two hysteresis models named LSTM and NARX neural networks were, respectively, established mathematically, and then were tested and verified experimentally. Third, a novel adaptive weighted hybrid hysteresis model which combines LSTM and NARX neural networks was proposed through analyzing and comparing the unique characteristics of the above two hysteresis models. The proposed hybrid model combines LSTM’s ability to approximate nonlinear static hysteresis and NARX’s high dynamic-fitting ability. Experimental results show that the RMS errors of the hybrid model are smaller than those of LSTM model and NARX model. That is to say, the proposed hybrid model has a relatively high accuracy. Compared with the traditional differential equation-based and operator-based hysteresis models, the presented hybrid neural network method has higher flexibility and accuracy in modeling performance, and is a more promising method for modeling piezoelectric hysteresis.
Autonomous four-wheeled robots have been widely studied and developed for various purposes over several decades. We have developed an All Terrain Vehicle(ATV)-based, four-wheeled, car-like robot to support the environmental field survey of landfills. Navigating the robot toward an observation spot must have feedback control of the vehicle. We propose a path-generating regulator (PGR) for car-like robots and show the properties of its control performance. Originally, the PGR was a control method for two-wheeled mobile robots to converge at the origin of a coordinate frame, of which the heading angle is controlled so as to align the tangential angle of one of the path among the path function group. Unlike
In this paper, the path-generating regulator is extended to tracking problem along a straight passage for two-wheeled mobile robots. As most of mobile robots are with nonholonomic constraints, it is difficult for us to make them converge to the target state with a control law. To solve this problem, many methods have been proposed. One of them is Path-generating Regulator(PGR) which designs a nonlinear regulator carrying out asymptotic convergence to a given trajectory family. However, the original method is not well suited for passages. In this paper, we will present the extended PGR for the tracking problem along a straight passage. Numerical simulations and experiments are also performed to show the effectiveness of this method.
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