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Magnetic shape memory alloy based actuator (MSMA-BA) has the capability to generate the micro-nano scale precision positioning, which has unparalleled virtue in contrast with the traditional motion driving mechanism. Nevertheless, the output displacement of the MSMA-BA exhibits complex hysteresis nonlinearity, which hinders its utility in the high-precision positioning field. In this paper, an online Volterra series model based on wavelet neural network (VSM-WNN) is proposed to describe the rate-dependent hysteresis of the MSMA-BA. The kernel function of the VSM is extracted with a WNN, which the feasibility of this extraction is proved in theory via Taylor expansion of the wavelet function. Finally, the validity of the proposed model is confirmed by means of experiments. Experimental results indicate that the VSM-WNN has a remarkable performance in describing the traits of hysteresis behavior of the MSMA-BA.
Yewei Yu; Chen Zhang; Zhiwu Han; Miaolei Zhou. Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Volterra Series. IEEE Transactions on Magnetics 2021, 57, 1 -4.
AMA StyleYewei Yu, Chen Zhang, Zhiwu Han, Miaolei Zhou. Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Volterra Series. IEEE Transactions on Magnetics. 2021; 57 (7):1-4.
Chicago/Turabian StyleYewei Yu; Chen Zhang; Zhiwu Han; Miaolei Zhou. 2021. "Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Volterra Series." IEEE Transactions on Magnetics 57, no. 7: 1-4.
The typical characteristic of a magnetic shape memory alloy (MSMA) based actuator is rate-dependent and stress-dependent hysteresis. In addition, the hysteresis in an MSMA-based actuator includes memory, multi-valued mapping, and asymmetry properties, which seriously degrade the positioning accuracy. In this study, a feedforward neural network (FNN) based nonlinear autoregressive moving average with exogenous inputs (NARMAX) model is employed to describe the hysteresis in an MSMA-based actuator. Through a Taylor expansion, it is revealed how the FNN constructs the nonlinear function of the NARMAX model. For the controller implementation, an FNN-based iterative learning control (ILC) strategy is proposed to achieve the desired tracking performance. The contributions of this study include exploring the update rule of the FNN-based ILC and analyzing the convergence properties of the proposed controller when the initial state of the system varies. To verify the effectiveness of the proposed method, experiments on an MSMA-based actuator were conducted. The results illustrate that the proposed modeling and control methods achieve an excellent performance and that the theoretical results are correct.
Yewei Yu; Chen Zhang; Yifan Wang; Miaolei Zhou. Neural Network-based Iterative Learning Control for Hysteresis in Magnetic Shape Memory Alloy Actuator. IEEE/ASME Transactions on Mechatronics 2021, PP, 1 -1.
AMA StyleYewei Yu, Chen Zhang, Yifan Wang, Miaolei Zhou. Neural Network-based Iterative Learning Control for Hysteresis in Magnetic Shape Memory Alloy Actuator. IEEE/ASME Transactions on Mechatronics. 2021; PP (99):1-1.
Chicago/Turabian StyleYewei Yu; Chen Zhang; Yifan Wang; Miaolei Zhou. 2021. "Neural Network-based Iterative Learning Control for Hysteresis in Magnetic Shape Memory Alloy Actuator." IEEE/ASME Transactions on Mechatronics PP, no. 99: 1-1.
The magnetic shape memory alloy (MSMA) is a new family of the smart materials, which exhibits great strain deformation and high energy density. Based on these properties, the MSMA has excellent potential to represent an available means for developing a novel generation of actuators in the micro-positioning application. However, the MSMA-based actuator suffers from the inherent hysteresis and it has become a bottleneck in the industrial application. A hybrid hysteresis model, which consists of a simple dynamic hysteresis operator (SDHO) and chaotic neural network (CNN), is proposed in this paper. This developed model possesses a concise construction and distinguished generalization capability. By conducting comparative experiments, the proposed approach has a superior ability to predict the hysteresis behaviors under various input signals.
Chen Zhang; Yewei Yu; Yifan Wang; Zhiwu Han; Miaolei Zhou. Chaotic Neural Network-Based Hysteresis Modeling With Dynamic Operator for Magnetic Shape Memory Alloy Actuator. IEEE Transactions on Magnetics 2021, 57, 1 -4.
AMA StyleChen Zhang, Yewei Yu, Yifan Wang, Zhiwu Han, Miaolei Zhou. Chaotic Neural Network-Based Hysteresis Modeling With Dynamic Operator for Magnetic Shape Memory Alloy Actuator. IEEE Transactions on Magnetics. 2021; 57 (6):1-4.
Chicago/Turabian StyleChen Zhang; Yewei Yu; Yifan Wang; Zhiwu Han; Miaolei Zhou. 2021. "Chaotic Neural Network-Based Hysteresis Modeling With Dynamic Operator for Magnetic Shape Memory Alloy Actuator." IEEE Transactions on Magnetics 57, no. 6: 1-4.
Miaolei Zhou; Yifan Wang; Yannan Zhang; Wei Gao. Hysteresis inverse compensation-based model reference adaptive control for a piezoelectric micro-positioning platform. Smart Materials and Structures 2020, 30, 015019 .
AMA StyleMiaolei Zhou, Yifan Wang, Yannan Zhang, Wei Gao. Hysteresis inverse compensation-based model reference adaptive control for a piezoelectric micro-positioning platform. Smart Materials and Structures. 2020; 30 (1):015019.
Chicago/Turabian StyleMiaolei Zhou; Yifan Wang; Yannan Zhang; Wei Gao. 2020. "Hysteresis inverse compensation-based model reference adaptive control for a piezoelectric micro-positioning platform." Smart Materials and Structures 30, no. 1: 015019.
A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system’s tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs’ approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.
Guoqiang Zhu; Linlin Nie; Miaolei Zhou; Xiuyu Zhang; Lingfang Sun; Cheng Zhong. Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer. IEEE Access 2020, 8, 163163 -163175.
AMA StyleGuoqiang Zhu, Linlin Nie, Miaolei Zhou, Xiuyu Zhang, Lingfang Sun, Cheng Zhong. Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer. IEEE Access. 2020; 8 (99):163163-163175.
Chicago/Turabian StyleGuoqiang Zhu; Linlin Nie; Miaolei Zhou; Xiuyu Zhang; Lingfang Sun; Cheng Zhong. 2020. "Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer." IEEE Access 8, no. 99: 163163-163175.
The piezoceramic actuated stages have rate-dependent hysteresis nonlinearity, which is not simply related to the current and historical input, but also related to the frequency of the input signal, seriously affects its positioning accuracy. Consider the influence of frequency on hysteresis modeling, a rate-dependent hysteresis nonlinearity model that is based on Krasnoselskii–Pokrovskii (KP) operator is proposed in this paper. A hybrid optimization algorithm of improved particle swarm optimization and cuckoo search is employed in order to identify the density function of rate-dependent KP model, avoiding the blind search process caused by the high randomness of Levy’s flight in the cuckoo search algorithm, and improving the parameter identification performance. For the sake of eliminating the hysteresis characteristics, an inverse feed-forward compensation control that is based on recursive method is proposed without any additional conditions, and a feed-forward compensation controller is designed accordingly. The experimental results show that, under different frequency input signals, as compared with the classic KP model, the proposed rate-dependent KP model can accurately describe the rate-dependent hysteresis characteristics of the piezoceramic actuated stages, and the recursive inverse feed-forward compensation control method can effectively mitigate the hysteresis behaviors.
Wenjun Li; Linlin Nie; Ying Liu; Miaolei Zhou. Rate Dependent Krasnoselskii-Pokrovskii Modeling and Inverse Compensation Control of Piezoceramic Actuated Stages. Sensors 2020, 20, 5062 .
AMA StyleWenjun Li, Linlin Nie, Ying Liu, Miaolei Zhou. Rate Dependent Krasnoselskii-Pokrovskii Modeling and Inverse Compensation Control of Piezoceramic Actuated Stages. Sensors. 2020; 20 (18):5062.
Chicago/Turabian StyleWenjun Li; Linlin Nie; Ying Liu; Miaolei Zhou. 2020. "Rate Dependent Krasnoselskii-Pokrovskii Modeling and Inverse Compensation Control of Piezoceramic Actuated Stages." Sensors 20, no. 18: 5062.
The piezoelectric micro-positioning platform(PMP) has advantages in high displacement resolution and fast response, however, the serious hysteresis nonlinearity in the PMP restricts its positioning accuracy. This paper presents a discretization of Krasnosel’skii-Pokrovskii model to describe the hysteresis nonlinearity of the PMP. Then, the density function is identified online by the adaptive linear neural network. To compensate the intrinsic hysteresis nonlinearity in the PMP, a feedforward compensation control method is implemented by an innovative iterative learning control algorithm. Moreover, a hybrid control method based on iterative learning and fractional order PID control is proposed to further improve the control accuracy. A series of comparative experiments are carried out to validate the feasibility and effectiveness of the proposed control method.
Miaolei Zhou; Yewei Yu; Jingai Zhang; Wei Gao. Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform. IEEE Access 2020, 8, 144654 -144664.
AMA StyleMiaolei Zhou, Yewei Yu, Jingai Zhang, Wei Gao. Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform. IEEE Access. 2020; 8 (99):144654-144664.
Chicago/Turabian StyleMiaolei Zhou; Yewei Yu; Jingai Zhang; Wei Gao. 2020. "Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform." IEEE Access 8, no. 99: 144654-144664.
Piezoelectric actuators (PEA) have been widely used in the ultra-precision manufacturing fields. However, the hysteresis nonlinearity between the input voltage and the output displacement, which possesses the properties of rate dependency and multivalued mapping, seriously impedes the positioning accuracy of the PEA. This paper investigates a control methodology without the hysteresis model for PEA actuated nanopositioning systems, in which the inherent drawback generated by the hysteresis nonlinearity aggregates the control accuracy of the PEA. To address this problem, a neural network self-tuning control approach is proposed to realize the high accuracy tracking with respect to the system uncertainties and hysteresis nonlinearity of the PEA. First, the PEA is described as a nonlinear equation with two variables, which are unknown. Then, using the capabilities of super approximation and adaptive parameter adjustment, the neural network identifiers are used to approximate the two unknown variables automatically updated without any off-line identification, respectively. To verify the validity and effectiveness of the proposed control methodology, a series of experiments is executed on a commercial PEA product. The experimental results illustrate that the established neural network self-tuning control method is efficient in damping the hysteresis nonlinearity and enhancing the trajectory tracking property.
Wenjun Li; Chen Zhang; Wei Gao; Miaolei Zhou. Neural Network Self-Tuning Control for a Piezoelectric Actuator. Sensors 2020, 20, 3342 .
AMA StyleWenjun Li, Chen Zhang, Wei Gao, Miaolei Zhou. Neural Network Self-Tuning Control for a Piezoelectric Actuator. Sensors. 2020; 20 (12):3342.
Chicago/Turabian StyleWenjun Li; Chen Zhang; Wei Gao; Miaolei Zhou. 2020. "Neural Network Self-Tuning Control for a Piezoelectric Actuator." Sensors 20, no. 12: 3342.
The magnetic shape memory alloy (MSMA)-based actuator, as a new type of actuator, has a great application prospect in the micro-precision positioning field. However, the input-to-output hysteresis nonlinearity largely hinders its wide application. In this paper, a Takagi–Sugeno fuzzy neural network (TSFNN) model based on the modified bacteria foraging algorithm (MBFA) is innovatively utilized to describe the complex hysteresis nonlinearity of the MSMA-based actuator, and the parameters of TSFNN are optimized by the MBFA. The TSFNN is a combination of the fuzzy-logic system and neural network; thus, it has the capability of approximating the nonlinear mapping function and self-adjustment and is suitable for hysteresis modeling. The MBFA, which can obtain better optimization values, is employed for the parameter identification procedure. To demonstrate the effectiveness of the proposed model, a TSFNN based on the gradient descent algorithm (GDA) is used for comparison. Experimental results clearly show that the proposed modeling method can accurately describe the hysteresis nonlinearity of the MSMA-based actuator and has significance for its future application.
Chen Zhang; Yewei Yu; Yifan Wang; Miaolei Zhou. Takagi–Sugeno Fuzzy Neural Network Hysteresis Modeling for Magnetic Shape Memory Alloy Actuator Based on Modified Bacteria Foraging Algorithm. International Journal of Fuzzy Systems 2020, 22, 1314 -1329.
AMA StyleChen Zhang, Yewei Yu, Yifan Wang, Miaolei Zhou. Takagi–Sugeno Fuzzy Neural Network Hysteresis Modeling for Magnetic Shape Memory Alloy Actuator Based on Modified Bacteria Foraging Algorithm. International Journal of Fuzzy Systems. 2020; 22 (4):1314-1329.
Chicago/Turabian StyleChen Zhang; Yewei Yu; Yifan Wang; Miaolei Zhou. 2020. "Takagi–Sugeno Fuzzy Neural Network Hysteresis Modeling for Magnetic Shape Memory Alloy Actuator Based on Modified Bacteria Foraging Algorithm." International Journal of Fuzzy Systems 22, no. 4: 1314-1329.
Magnetic shape memory alloy (MSMA) actuator has potential application value in the aerospace, robotics and precision positioning due to the advantages such as small size, high precision, long stroke length and large energy density. However, the asymmetrical rate-dependent hysteresis between input and output of the MSMA actuator makes it difficult to build precise model of the MSMA actuator-based micropositioning system, so that the application of the MSMA actuator is seriously hindered. In this paper, a Bouc-Wen (BW) model is adopted to describe the hysteresis of the MSMA actuator. The parameters of BW model are identified online by Hopfield neural network (HNN). Then, the effectiveness of HNN-based BW model is fully certified using the experiments. The experimental results show that the BW model identified in this paper can accurately describe the hysteresis of the MSMA actuator at different input excitation.
Yifan Wang; Chen Zhang; Zhongshi Wu; Wei Gao; Miaolei Zhou. A hopfield neural network-based Bouc-Wen model for magnetic shape memory alloy actuator. AIP Advances 2020, 10, 015212 .
AMA StyleYifan Wang, Chen Zhang, Zhongshi Wu, Wei Gao, Miaolei Zhou. A hopfield neural network-based Bouc-Wen model for magnetic shape memory alloy actuator. AIP Advances. 2020; 10 (1):015212.
Chicago/Turabian StyleYifan Wang; Chen Zhang; Zhongshi Wu; Wei Gao; Miaolei Zhou. 2020. "A hopfield neural network-based Bouc-Wen model for magnetic shape memory alloy actuator." AIP Advances 10, no. 1: 015212.
Magnetic shape memory alloy (MSMA) based actuators are extensively applied in the fields of precision manufacturing and micro/nano technology. Nevertheless, the inherent hysteresis in the MSMA-based actuator severely hinders its further application. In this study, the characteristics of hysteresis behavior under different input signals are investigated. Then, a nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model based on a diagonal recurrent neural network (DRNN) is used to construct the rate-dependent hysteresis model. To improve the capability of characterizing the multi-valued mapping of the hysteresis loop, the play operator is adopted as the exogenous variable function of the NARMAX model. To verify the effectiveness of the proposed model, a series of comparisons are implemented. The experimental results show that the proposed NARMAX model based on the DRNN exhibits excellent modeling performance.
Yewei Yu; Chen Zhang; Miaolei Zhou. NARMAX Model-Based Hysteresis Modeling of Magnetic Shape Memory Alloy Actuators. IEEE Transactions on Nanotechnology 2019, 19, 1 -4.
AMA StyleYewei Yu, Chen Zhang, Miaolei Zhou. NARMAX Model-Based Hysteresis Modeling of Magnetic Shape Memory Alloy Actuators. IEEE Transactions on Nanotechnology. 2019; 19 (99):1-4.
Chicago/Turabian StyleYewei Yu; Chen Zhang; Miaolei Zhou. 2019. "NARMAX Model-Based Hysteresis Modeling of Magnetic Shape Memory Alloy Actuators." IEEE Transactions on Nanotechnology 19, no. 99: 1-4.
Giant magnetostrictive actuator (GMA) becomes more promising in precision positioning, sophisticated manufacturing, and microelectronic system, because of the excellent performance such as high resolution, rapid response, and large mechanical force. However, the intrinsic hysteresis of GMA is the main sticking point preventing its application in the high precision positioning. In this paper, a Prandtl-Ishlinskii (PI) model is established. To obtain the parameters of the model, the internal time-delay recurrent neural network (RNN) with self-tuning ability is proposed. A series of simulations are conducted to testify the availability of proposed method and the results indicates that the PI model identified by the internal time-delay RNN can accurately describe the hysteresis of GMA with the increase of frequency.
Yifan Wang; Rui Xu; Miaolei Zhou. Prandtl-Ishlinskii Modeling for Giant Magnetostrictive Actuator Based on Internal Time-Delay Recurrent Neural Network. IEEE Transactions on Magnetics 2018, 54, 1 -4.
AMA StyleYifan Wang, Rui Xu, Miaolei Zhou. Prandtl-Ishlinskii Modeling for Giant Magnetostrictive Actuator Based on Internal Time-Delay Recurrent Neural Network. IEEE Transactions on Magnetics. 2018; 54 (11):1-4.
Chicago/Turabian StyleYifan Wang; Rui Xu; Miaolei Zhou. 2018. "Prandtl-Ishlinskii Modeling for Giant Magnetostrictive Actuator Based on Internal Time-Delay Recurrent Neural Network." IEEE Transactions on Magnetics 54, no. 11: 1-4.
Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation (PE) condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.
Rui Xu; Miaolei Zhou. A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network. Smart Materials and Structures 2018, 27, 045002 .
AMA StyleRui Xu, Miaolei Zhou. A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network. Smart Materials and Structures. 2018; 27 (4):045002.
Chicago/Turabian StyleRui Xu; Miaolei Zhou. 2018. "A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network." Smart Materials and Structures 27, no. 4: 045002.
Piezoelectric positioning platform is widely applied in the micro-nano positioning field. However, the inherent hysteresis nonlinearity in piezoelectric materials seriously damages the positional accuracy of the platform. In this study, an integral sliding mode control with compensating hysteresis-observer is developed for high-precision positioning control of the piezoelectric positioning platform. Hysteresis-observer is used to estimate and compensate the hysteresis nonlinearity part of the platform. The observation errors and parameter uncertainties part are considered as external disturbance of systems, which are eliminated using the designed integral sliding mode controller. Then, the stability of the proposed control strategy is demonstrated using the Lyapunov stability theory. Finally, a series of tracking experiments are implemented on the piezoelectric positioning platform. Experimental results reveal that the proposed integral sliding mode control with compensating hysteresis-observer can achieve the high precision tracking control of the piezoelectric positioning platform.
Rui Xu; Miaolei Zhou. Integral sliding mode tracking control of piezoelectric positioning platform with compensating hysteresis-observer. 2017 International Conference on Advanced Mechatronic Systems (ICAMechS) 2017, 472 -476.
AMA StyleRui Xu, Miaolei Zhou. Integral sliding mode tracking control of piezoelectric positioning platform with compensating hysteresis-observer. 2017 International Conference on Advanced Mechatronic Systems (ICAMechS). 2017; ():472-476.
Chicago/Turabian StyleRui Xu; Miaolei Zhou. 2017. "Integral sliding mode tracking control of piezoelectric positioning platform with compensating hysteresis-observer." 2017 International Conference on Advanced Mechatronic Systems (ICAMechS) , no. : 472-476.
In order to describe the inherent hysteresis nonlinearity of the piezo-actuated stages, a Krasnosel'skii-Pokrovskii (KP) model is presented in the paper. The parameters of the KP model are identified by a modified bat optimization algorithm based on Levy fights trajectory. The modified bat optimization algorithm uses the Levy fights trajectory to improve the global searching ability and break away from the local optimum. Under different conditions of iterations, the performance of the modified bat optimization algorithm is compared with the performance of the bat algorithm, the cuckoo search algorithm in terms of the identification precision. It is proved that the proposed algorithm has great convergence rate with high degree of precision in identifying the KP model parameters from the simulation results. The output data of the KP model based on the modified bat optimization algorithm is in great agreement with the experimental data of the piezo-actuated stages.
Rui Xu; Miaolei Zhou. Parameters identification of Krasnosel'skii-Pokrovskii model for piezo-actuated stages using a modified bat optimization algorithm based on levy fights trajectory. 2017 Chinese Automation Congress (CAC) 2017, 5304 -5309.
AMA StyleRui Xu, Miaolei Zhou. Parameters identification of Krasnosel'skii-Pokrovskii model for piezo-actuated stages using a modified bat optimization algorithm based on levy fights trajectory. 2017 Chinese Automation Congress (CAC). 2017; ():5304-5309.
Chicago/Turabian StyleRui Xu; Miaolei Zhou. 2017. "Parameters identification of Krasnosel'skii-Pokrovskii model for piezo-actuated stages using a modified bat optimization algorithm based on levy fights trajectory." 2017 Chinese Automation Congress (CAC) , no. : 5304-5309.
Piezoelectric ceramic micro-positioning platform has been widely used in micro-positioning, vibration control, and manufacturing applications as a core component of precision manufacturing equipment. However, the main drawback of the piezoelectric ceramic micro-positioning platform is the inherent hysteresis nonlinearity, which affects the positioning accuracy because of its non-memoryless as well as multi-valuedness. In this paper, we propose a generalized rate-dependent Prandtl-Ishlinskii (GRPI) model with rate-dependent asymmetric properties by introducing dynamic envelope functions into the play operators and polynomial input function. Then, the parameters of the proposed model are identified by optimization toolbox of MATLAB based on the measured data of piezoelectric ceramic micro-positioning platform. Finally, different frequencies signals are adopted to test the effectiveness of GRPI model. Compared with the Prandtl-Ishlinskii (PI) model, the simulation results verify that the hysteresis nonlinearity of the piezoelectric ceramic micro-positioning platform can be described accurately by the proposed GRPI model.
Yiling Luo; Miaolei Zhou; Rui Xu. Modeling of hysteresis nonlinearity in piezoelectric ceramic micro-positioning platform based on generalized rate-dependent Prandtl-Ishlinskii model. 2017 Chinese Automation Congress (CAC) 2017, 574 -578.
AMA StyleYiling Luo, Miaolei Zhou, Rui Xu. Modeling of hysteresis nonlinearity in piezoelectric ceramic micro-positioning platform based on generalized rate-dependent Prandtl-Ishlinskii model. 2017 Chinese Automation Congress (CAC). 2017; ():574-578.
Chicago/Turabian StyleYiling Luo; Miaolei Zhou; Rui Xu. 2017. "Modeling of hysteresis nonlinearity in piezoelectric ceramic micro-positioning platform based on generalized rate-dependent Prandtl-Ishlinskii model." 2017 Chinese Automation Congress (CAC) , no. : 574-578.
A rate-dependent modeling method and a real-time tracking control scheme are designed to eliminate the hysteresis nonlinearity of the piezoelectric micro positioning (PMP) platform. First, we use Duhem model cascaded a transfer function to structure the rate-dependent hysteresis nonlinearity of the PMP platform. Then, based on the established Duhem inverse model and phase compensation for transfer function, a hybrid control scheme is introduced by adjusting PID parameters online with RBF neural network. Experimental results show that the average modeling error is reduced from 4.02% to 2.47% compared with the general Duhem model with the input voltage frequency of 100 Hz. On the basis of the proposed control strategy to track the sinusoidal signal with the amplitude of 42pm and in a frequency range of 100 Hz, the average modeling error range is from 0.11% to 0.95%. Experimental results preferably validate that the proposed model can effectively describe the rate-dependent hysteresis nonlinearity, and the proposed control strategy has better realtime control performance.
Chenyang Wang; Miaolei Zhou; Rui Xu. Rate-dependent modeling and tracking control for piezoelectric micro-positioning platform. 2017 Chinese Automation Congress (CAC) 2017, 568 -573.
AMA StyleChenyang Wang, Miaolei Zhou, Rui Xu. Rate-dependent modeling and tracking control for piezoelectric micro-positioning platform. 2017 Chinese Automation Congress (CAC). 2017; ():568-573.
Chicago/Turabian StyleChenyang Wang; Miaolei Zhou; Rui Xu. 2017. "Rate-dependent modeling and tracking control for piezoelectric micro-positioning platform." 2017 Chinese Automation Congress (CAC) , no. : 568-573.
Magnetic shape memory alloys (MSMA), which are a class of innovative functional materials, are used as the actuators to be applied widely in high-precision positioning. However, the hysteresis nonlinearity in the MSMA seriously affects the positioning precision of the MSMA-based actuator. In this paper, to study the hysteresis nonlinearity in the MSMA, the Krasnosel’skii-Pokrovskii (KP) model is employed to describe the hysteresis nonlinearity in the MSMA-based actuator and the density function of the KP model is identified by the Elman neural network. The simulations show that the modeling error rate of the KP model using the Elman neural network is 0.81%, which is reduced by 63.5% compared with that of the KP model based on recursive least-squares method. This result demonstrates that the KP model based on the Elman neural network can accurately describe the hysteresis nonlinearity in the MSMA-based actuator.
Rui Xu; Miaolei Zhou. Elman Neural Network-Based Identification of Krasnosel’skii–Pokrovskii Model for Magnetic Shape Memory Alloys Actuator. IEEE Transactions on Magnetics 2017, 53, 1 -4.
AMA StyleRui Xu, Miaolei Zhou. Elman Neural Network-Based Identification of Krasnosel’skii–Pokrovskii Model for Magnetic Shape Memory Alloys Actuator. IEEE Transactions on Magnetics. 2017; 53 (11):1-4.
Chicago/Turabian StyleRui Xu; Miaolei Zhou. 2017. "Elman Neural Network-Based Identification of Krasnosel’skii–Pokrovskii Model for Magnetic Shape Memory Alloys Actuator." IEEE Transactions on Magnetics 53, no. 11: 1-4.
Hybrid Control Method of Magnetically Controlled Shape Memory Alloy Actuator Based on Inverse Prandtl-Ishlinskii Model Magnetically controlled shape memory alloy;Hysteresis nonlinearity;Prandtl-Ishlinskii model;Particle swarm optimization;
Miaolei Zhou; Rui Xu; Qi Zhang; Ziying Wang; Yu Zhao. Hybrid Control Method of Magnetically Controlled Shape Memory Alloy Actuator Based on Inverse Prandtl-Ishlinskii Model. Journal of Electrical Engineering & Technology 2016, 11, 1457 -1465.
AMA StyleMiaolei Zhou, Rui Xu, Qi Zhang, Ziying Wang, Yu Zhao. Hybrid Control Method of Magnetically Controlled Shape Memory Alloy Actuator Based on Inverse Prandtl-Ishlinskii Model. Journal of Electrical Engineering & Technology. 2016; 11 (5):1457-1465.
Chicago/Turabian StyleMiaolei Zhou; Rui Xu; Qi Zhang; Ziying Wang; Yu Zhao. 2016. "Hybrid Control Method of Magnetically Controlled Shape Memory Alloy Actuator Based on Inverse Prandtl-Ishlinskii Model." Journal of Electrical Engineering & Technology 11, no. 5: 1457-1465.
Magnetic shape memory alloy actuator have hysteresis characteristics, in order to describe the hysteresis nonlinearity of magnetic shape memory alloy actuator and study the methods of eliminating the hysteresis nonlinearity of magnetic shape memory alloy actuator, the Krasnosel'skii-Pokrovskii (KP) model is firstly established in this paper. Secondly, BP neural network and adaptive linear neural network are applied to identify the density functions of the KP model respectively. Finally, it is obtained that the modeling accuracy of the KP model is 0.37% by BP algorithm and the modeling accuracy of the KP model based on adaptive linear neural network is 0.19% through the simulation results, which prove that the KP model can characterize the hysteresis nonlinearity of magnetic shape memory alloy actuator.
Miaolei Zhou; Tingting Han; Qi Zhang. Krasnosel'skii-Pokrovskii hysteresis model for magnetic shape memory alloy actuator. 2016 35th Chinese Control Conference (CCC) 2016, 2206 -2211.
AMA StyleMiaolei Zhou, Tingting Han, Qi Zhang. Krasnosel'skii-Pokrovskii hysteresis model for magnetic shape memory alloy actuator. 2016 35th Chinese Control Conference (CCC). 2016; ():2206-2211.
Chicago/Turabian StyleMiaolei Zhou; Tingting Han; Qi Zhang. 2016. "Krasnosel'skii-Pokrovskii hysteresis model for magnetic shape memory alloy actuator." 2016 35th Chinese Control Conference (CCC) , no. : 2206-2211.