Yu Fang

Bio: Yu Fang is an academic researcher from Xihua University. The author has contributed to research in topics: Control theory & Hysteresis. The author has an hindex of 2, co-authored 3 publications receiving 5 citations.

Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

Abstract: Piezoelectric actuators (PAs) require high precision positioning for the applications of micro electrical mechanical systems, but it exhibits hysteresis nonlinearity which deteriorates positioning accuracy if no proper compensation is given. Hysteresis nonlinear modeling of PAs is a prime choice for hysteresis compensation. This paper proposes a novel intelligent positioning control algorithm based on Bouc-Wen (BW) model for the compensation of a bi-morph type piezoelectric actuator (PA) suffering rate-dependent hysteresis. A region based mixed-species swarm optimization (RMSO) algorithm is proposed for BW modeling to capture the dynamic nonlinearity of a piezoelectric actuator which exhibits rate-dependent hysteresis. Results of numerical simulations have been disclosed to illustrate the performance enhancement of RMSO over classical algorithm while they are applied to the parameter fitting problem of BW model for experimentally acquired datasets. An model based adaptive Fuzzy neural network (Fuzzy-NN) controller of PA is utilized to compensate the hysteresis for the positioning tracking control. Experimental results also illustrate the good performance of the proposed RMSO-BW based control scheme for the hysteresis compensation control of the PA.

Adaptive novel MSGA-RBF neurocontrol for piezo-ceramic actuator suffering rate-dependent hysteresis

Abstract: Piezo-ceramic actuators are widely used in micro-electro-mechanical systems. An adaptive inverse neurocontrol design is proposed for positioning control of the piezo-ceramic actuator (PA). Piezo-ceramic actuators exhibit rate-dependent hysteresis which changes its hysteretic behavior when the rate or the frequency of its driving signal varies. Radial basis function neural network (RBFNN) is utilized to model the input/output relation of the PA with rate-dependent hysteresis to make it work as an accurate hysteretic model for positioning control. And an adaptive inverse nonlinear controller is introduced for the rate-dependent hysteresis compensation of PA. A novel membrane structure genetic algorithm (MSGA) is proposed for the adaptive inverse nonlinear neurocontrol design of PA. Compared with the classical genetic algorithm, the experimental results illustrate the performance of the proposed control system as well as the efficiency of adaptive membrane structure genetic algorithm for positioning control of PA system.

Optimization of heart sound feature combination for HCM analysis based on BPSO

Abstract: An optimization method of heart sound feature combination based on binary particle swarm optimization (BPSO) is proposed to analyze hypertrophic cardiomyopathy (HCM), which is a typical cardiovascular disease. The analysis procedure mainly includes feature extraction, feature combination optimization and HCM analysis. Firstly, 13-dimension energy features are extracted by the wavelet transformation. Secondly, feature combinations are selected by the proposed optimization method for classifying the normal and HCM heart sounds as well as the different types of HCM heart sounds. Lastly, 362 clinical cases including normal and HCM heart sounds are used to verify the validity of the proposed optimization method for heart sound feature combination, compared with principle component analysis (PCA), genetic algorithm (GA) and the analysis method without optimization of heart sound features. The average accuracy by our proposed optimization method, considering the number of features selected out, reaches to 20.91%, higher than the results utilizing feature combinations obtained by the other methods. In addition, the proposed optimization method of feature combination has potential for analyzing different kinds of HCM cases.

Single-Neuron Adaptive Hysteresis Compensation of Piezoelectric Actuator Based on Hebb Learning Rules.

Abstract: This paper presents an adaptive hysteresis compensation approach for a piezoelectric actuator (PEA) using single-neuron adaptive control. For a given desired trajectory, the control input to the PEA is dynamically adjusted by the error between the actual and desired trajectories using Hebb learning rules. A single neuron with self-learning and self-adaptive capabilities is a non-linear processing unit, which is ideal for time-variant systems. Based on the single-neuron control, the compensation of the PEA’s hysteresis can be regarded as a process of transmitting biological neuron information. Through the error information between the actual and desired trajectories, the control input is adjusted via the weight adjustment method of neuron learning. In addition, this paper also integrates the combination of Hebb learning rules and supervised learning as teacher signals, which can quickly respond to control signals. The weights of the single-neuron controller can be constantly adjusted online to improve the control performance of the system. Experimental results show that the proposed single-neuron adaptive hysteresis compensation method can track continuous and discontinuous trajectories well. The single-neuron adaptive controller has better adaptive and self-learning performance against the rate-dependence of the PEA’s hysteresis.

Modeling and Identification of Rate Dependent Hysteresis in Piezoelectric Actuated Nano-Stage: A Gray Box Neural Network Based Approach

Abstract: A modeling and parameter identification method for rate dependent hysteresis of piezoelectric actuated nano-stage is presented in this work. A system level quasi-static hysteresis model is employed to construct a neural network. To better describe the rate dependent behavior of hysteresis in piezoelectric actuated stage, a Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) based dynamic model is incorporated with the quasi-static hysteresis model, where the weights of specifically designed neural network corresponds to the model parameters. To handle the multivalued problem of hysteresis, generalized input gradient is proposed to convert multivalued mapping of hysteresis into one-to-one mapping. The parameters of the nonlinear rate dependent hysteresis in piezoelectric actuated stage is identified by neural network training, taking advantage of their universal function approximation capabilities. The proposed scheme is also compared with conventional black box and particle swarm optimization identification based methods, simulation and experimental results demonstrate significant performance improvement with an error of 20.77nm for proposed method whereas 96.56nm and 31.46nm for black box and particle swarm optimization respectively.

Direct Inverse Hysteresis Compensation of Piezoelectric Actuators Using Adaptive Kalman Filter

Abstract: The hysteresis of the piezoelectric actuator exhibits obvious rate dependence, especially in fast scanning or tracking. This article proposes an adaptive hysteresis compensation method that integrates the direct inverse modeling (DIM) method and an adaptive Kalman filter (AKF). DIM is adopted to directly obtain the inverse hysteresis model and use it as the hysteresis compensator. AKF is utilized to dynamically update the weights of the inverse hysteresis model so as to eliminate the manual tuning of control parameters. Further, a forgetting factor is adopted in AKF to reduce the influence of the old data. Experimental results on tracking of step, sinusoidal, and triangular trajectories demonstrate the effectiveness of DIM + AKF in hysteresis compensation. The transient and steady state performances of the closed-loop system are significantly improved. A closed-loop bandwidth higher than 200 Hz is achieved. The robustness of DIM + AKF against the rate dependence is further verified using a 0–200 Hz swept sinusoidal trajectory. As the onsite tuning is successfully eliminated, DIM + AKF is easy to follow for inexperienced users.