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The parallel driving soft manipulator with multiple extensors and contractile pneumatic artificial muscles (PAMs) is able to operate continuously and has varying stiffness, achieving smooth movements and a fundamental trade-off between flexibility and stiffness. Owing to the hysteresis of PAMs and actuator couplings, the manipulator outputs display coupled hysteresis behaviors with stiffness dependence, causing significant positioning errors. For precise positioning control, this paper takes the lead in proposing a comprehensive model aimed at accurately predicting the coupled hysteresis behavior with the stiffness dependence of the soft manipulator. The model consists of an inherent hysteresis submodule, an actuator coupling submodule, and a stiffness-dependent submodule in series. The asymmetrical hysteresis nonlinearity of the PAM is established by the generalized Prandtl–Ishlinskii model in the inherent hysteresis submodule. The serial actuator coupling submodule is dedicated to modeling the actuator couplings, and the stiffness-dependent submodule is implemented with a fuzzy neural network to characterize the stiffness dependence and other system nonlinearities. In addition, an inverse compensator on the basis of the proposed model is conducted. Experiments demonstrate that this model possesses high accuracy and good generalization, and its compensator is effective in decoupling and mitigating hysteresis coupling of the manipulator. The proposed model and control methods significantly improve the positioning accuracy of the pneumatic soft manipulator. [ABSTRACT FROM AUTHOR]

Accurate force tracking of an electro-hydraulic force system (EHFS) is of great significance in various industrial applications. As the EHFS is inevitably confronted with hydraulic nonlinearities, dynamic variations and uncertain disturbances, the real-time force tracking performance is generally unsatisfactory, especially for high frequency command force signals. For reducing the force tracking error with relatively high frequency inputs, a Legendre polynomials expanded adaptive controller with inverse compensation is proposed in this paper. The proposed controller is constructed by introducing an offline designed inverse compensation (IC) controller and an online adaptive controller to the EHFS governed by traditional Proportional Integral (PI) controller, in which the former IC controller is acting as the inner loop controller and the latter adaptive controller is regarded as an outer controller. The inner loop IC controller with fixed control parameters is offline designed on the basis of traditional PI controller by means of the generalized projection identification algorithm and the zero magnitude error tracking technology, focusing on extending the real-time force tracking bandwidth of the EHFS. The outer loop online adaptive controller is developed by Legendre polynomials expanded adaptive filters with nonlinear mapping ability, whose weights are online updated by an adaptive tuning algorithm with the aim of handling system's dynamic variations, nonlinearities and uncertain disturbances. The proposed controller is implemented on a real EHFS test rig by the xPC/Target rapid prototyping technique, and comparative experimental results demonstrate that the proposed controller can achieve much higher high frequency tracking performance than the traditional PI and IC controller commonly used in industry. [ABSTRACT FROM AUTHOR]

In this article, a new intelligent fault-tolerant control (FTC) is designed to control a flexible-link manipulator with uncertain dead-zone and intermittent actuator faults. Initially, a smooth dead-zone inverse model using a hyperbolic tangent function is introduced to handle dead-zone nonlinearity and suppress input chattering. An adaptive law is proposed to estimate an unknown coupling item, combining the upper bounds of compensation error and floating bias faults, achieving robust adaptive control of the system. A new FTC strategy is subsequently developed to address intermittent actuator faults. Finally, the bounded convergence of system state errors is proven using direct Lyapunov methods, and the effectiveness and superiority of the proposed controller are demonstrated through numerical simulation and experiment. [ABSTRACT FROM AUTHOR]

This paper proposes the design and validation of a novel rate-dependent direct inverse Preisach model with input iteration (RDIPMII) dedicated to feedforward compensation of hysteresis nonlinearity in piezoelectric actuators (PEAs). Unlike existing similar works, the proposed RDIPMII avoids deriving the parameters of the inverse compensator from the hysteresis model, and could be directly employed as the inverse compensator. Furthermore, RDIPMII is capable of achieving rate-dependent inverse compensation while reducing the experimental burden in identifying models by the use of newly proposed data expression method (DEM). In addition, by integrating iterative learning control (ILC), RDIPMII accomplishes online input iteration to further suppress the hysteresis effect. The feasibility and efficiency of the presented scheme are demonstrated through experimental investigations conducted on a PEA. [ABSTRACT FROM AUTHOR]

The parallel driving soft manipulator with multiple extensors and contractile pneumatic artificial muscles (PAMs) is able to operate continuously and has varying stiffness, achieving smooth movements and a fundamental trade-off between flexibility and stiffness. Owing to the hysteresis of PAMs and actuator couplings, the manipulator outputs display coupled hysteresis behaviors with stiffness dependence, causing significant positioning errors. For precise positioning control, this paper takes the lead in proposing a comprehensive model aimed at accurately predicting the coupled hysteresis behavior with the stiffness dependence of the soft manipulator. The model consists of an inherent hysteresis submodule, an actuator coupling submodule, and a stiffness-dependent submodule in series. The asymmetrical hysteresis nonlinearity of the PAM is established by the generalized Prandtl–Ishlinskii model in the inherent hysteresis submodule. The serial actuator coupling submodule is dedicated to modeling the actuator couplings, and the stiffness-dependent submodule is implemented with a fuzzy neural network to characterize the stiffness dependence and other system nonlinearities. In addition, an inverse compensator on the basis of the proposed model is conducted. Experiments demonstrate that this model possesses high accuracy and good generalization, and its compensator is effective in decoupling and mitigating hysteresis coupling of the manipulator. The proposed model and control methods significantly improve the positioning accuracy of the pneumatic soft manipulator.

The soft manipulator actuated parallel with contractile and extensor pneumatic artificial muscles (PAMs) can perform continuous operations and vary stiffness. Due to the hysteresis nonlinearity of PAMs and parallel driving, the manipulator shows coupled hysteresis with stiffness-dependent and rate-dependent characteristics, causing inaccurate positioning performance. This paper firstly proposes a comprehensive dynamic model consisting of two parts in series to predict the manipulator dynamical behaviors with coupled hysteresis. Furthermore, a decoupled inverse compensation and decoupling control on the basis of the proposed model is conducted on the soft manipulator. Experimental results and comparisons illustrate the high precision and strong generalization ability of the proposed model and the validity of the compensator in mitigating the coupled dynamic hysteresis of the system. • A coupled dynamic hysteresis model is proposed for the PAM-based manipulator system. • The model is based on UPI operators and the fuzzy recurrent neural network. • An inverse compensation control is developed to decouple and compensate the system. • Validations of the proposed model and compensation method are conducted. [ABSTRACT FROM AUTHOR]

In this article, a new intelligent fault-tolerant control (FTC) is designed to control a flexible-link manipulator with uncertain dead-zone and intermittent actuator faults. Initially, a smooth dead-zone inverse model using a hyperbolic tangent function is introduced to handle dead-zone nonlinearity and suppress input chattering. An adaptive law is proposed to estimate an unknown coupling item, combining the upper bounds of compensation error and floating bias faults, achieving robust adaptive control of the system. A new FTC strategy is subsequently developed to address intermittent actuator faults. Finally, the bounded convergence of system state errors is proven using direct Lyapunov methods, and the effectiveness and superiority of the proposed controller are demonstrated through numerical simulation and experiment.

Digital light processing (DLP) is a relatively mature ceramic additive manufacturing technology widely applied in medical bone implantation, electronic communication, and other fields. However, the size error caused by the light scattering from the complex contour can seriously affect the precision and forming quality of the printed green body. This paper studied the scattering behavior of complex structure ceramics under different exposure energies through two structural parameters: exposure width and contour shape. A formula for excess cure width is presented. The formula will be then applied to simplify the machine learning algorithms. Finally, by inverse compensation for the complex contour, we optimized some pore structures and complex shapes to greatly improve the fidelity of the structure. [ABSTRACT FROM AUTHOR]

To control a nonlinear system with both hysteresis and disturbance, it is necessary to establish a hysteresis model and improve the disturbance rejection ability. However, the input signal implicitly involved in the classical hysteresis model can lead to difficulty in constructing a compensator. In this study, a hysteresis model in explicit form is proposed with a bounded auxiliary variable. Then, a model‐based inverse is constructed for approximate compensation for the hysteresis. Moreover, the compensation error, which is considered a part of the disturbance, is proved to be bounded. Disturbance estimation triggered control (DETC) is utilized to address the compensation error coupled with the external disturbance. According to the disturbance effect indicator (DEI), DETC can improve the system control performance by considering the disturbance effect judgment. Experimental results are presented to illustrate the potential of the proposed technique. [ABSTRACT FROM AUTHOR]

It has been widely accepted that a hysteretic system can be controlled by combining inverse compensation with feedback. Among the strategies to identify hysteretic systems, data-driven models have been received great attention due to its flexibility and ability to online and adaptive estimation. Nevertheless, less attention has been paid to determine its inversion, which is essential to use such models in control applications. The novelty of this paper is twofold. First, we propose a method to obtain analytically the inverse compensation of a hysteretic system modeled by a Nonlinear Auto-Regressive Model with eXougenous input (NARX) representation with a bounding structure. Second, this paper presents an adapted nonautonomous electronic circuit with rate-independent hysteresis and linear dynamics, which is used as a benchmark to test the proposed methodology. The experimental results have shown the efficiency of the proposed technique.

The feedforward inverse compensation is a typical approach to compensate for the hysteresis nonlinearity in smart materials based actuators, which often requires to develop a hysteresis model first and to compute the parameters of the inverse compensator based on the hysteresis model. Different from the above method, a direct inverse model (DIM) constructed by newly proposed clockwise relay operators is developed in this article. The direct inverse modeling approach is free from constructing the inverse compensator, and can be directly used as the inverse compensator. The parameters of DIM can be obtained via the model identification using the experimental data. In order to facilitate the numerical implementation and reduce the model complexity of DIM, a QR factorization based selection mechanism is employed to select the dominant clockwise relay operators. The reduced DIM is capable of preserving the accuracy of the full DIM. The simulation study and experiments are conducted to validate the effectiveness of the developed compensation approach. [ABSTRACT FROM AUTHOR]

In this article, a generalized inverse multiplicative structure (GIMS) is developed for the purpose of compensating for the hysteresis effect represented by the differential-equation-based hysteresis model. The GIMS approach is an approximate inverse compensation approach, and it does not require the analytical solution of the differential equations. The novelty of the proposed GIMS is that it extends the classical inverse multiplicative structure and it can be applied to construct the inverse compensator for differential-equation-based hysteresis models that can be written into the proposed unified expression. The LuGre model and the backlash-like model are employed as examples in the simulations and experiments. Both results validate the effectiveness of the proposed compensation approach. [ABSTRACT FROM AUTHOR]

Tracking control of piezoelectric actuators is considered in the article. A Hammerstein model is used to depict the rate-dependent hysteresis characteristics of piezoelectric actuators, in which a Bouc–Wen model is to describe the static hysteresis characteristic, and a linear time-invariant system is to describe its rate-dependent characteristics. An inverse Bouc–Wen model connected in series with the piezoelectric actuator is used to compensate the static hysteresis nonlinearity of piezoelectric actuators. Furthermore, an extended state observer–based fractional order sliding-mode control is designed to deal with higher order unmodelled dynamics and inverse compensation errors. Moreover, the bounds of the estimation error of the extended state observer are estimated, and the convergence of the proposed control strategy is proved. Experimental results show that the proposed scheme can track both single and composite input signals within a certain frequency range. Compared with extended state observer–based conventional sliding-mode controller, the proposed scheme has faster response time and smaller tracking error. [ABSTRACT FROM AUTHOR]

Summary: Delay compensation method plays an important role in maintaining the stability and accuracy of the real‐time hybrid simulation (RTHS). Inverse compensation (IC) method establishes a first‐order discrete transfer function model to represent servo‐hydraulic dynamics and provides an easy‐to‐implement compensation for RTHS. Only one parameter is required for the IC method, and it can be adjusted through different adaptive laws to accommodate error in initial delay estimation or time‐varying delay throughout the test. Different techniques have been developed to improve the performance of the IC method such as dual compensation and adaptive IC (AIC). A windowed frequency domain evaluation index‐based (WFEI) compensation is experimentally verified in this study through predefined tests and RTHS of a single‐degree of freedom (SDOF) system. The frequency domain evaluation index (FEI) is applied through short‐time Fourier transform (STFT) to estimate actuator delay in a real‐time manner and to adapt the parameter of IC method to minimize possible delay variation and/or inaccurate estimation. The efficacy and robustness of the FEI‐based compensation is further compared with the AIC method for various initial estimates, different window lengths, and bounds of estimated delay. Experimental results demonstrated that the WFEI method provides an effective and easy‐to‐implement method for RTHS. [ABSTRACT FROM AUTHOR]

The tendon-sheath actuated system is applicable to the mechanism where flexibility and long-distance transmission are required, such as surgical hand and rescue robot. Nevertheless, the severe nonlinearity and flexible structure make it hard to control precisely. Moreover, in some applications, where sensors are difficult to install, precise control is even more arduous. In this brief, a noncollocated position control of a tendon-sheath actuated manipulator is studied. First, the tendon-sheath actuated elastic manipulator of a slender rescue robot is introduced. A linear relationship was measured between the driven displacement and desired displacement of the elastic manipulator. Then, the drive system of the manipulator is modeled as a single-tendon-sheath system, and the transmission model is presented. The sheath configuration of the manipulator actuation system can be identified online, using the method called 3-D reconstruction. To control the tip position of the system without collocated feedback, a combined control method which is based on the transmission model and adaptive sliding mode control is put forward. Finally, the improved accuracy in position control is demonstrated by a series of experiments. [ABSTRACT FROM AUTHOR]

• A new Hammerstein model is established for the rate-dependent hysteresis of MFC. • Inverse compensation is designed to eliminate the hysteresis nonlinearity of MFC. • Adaptive iterative learning control is further designed for tracking control of MFC. • The convergence and robustness of the feedback controller of MFC are analyzed. • The effectiveness of the proposed control is verified through different signals. Macro fiber composite (MFC) actuators, which exhibit excellent flexibility and strong deformability, are widely used in aerospace exploration, flexible robotics, and other applications. However, the rate-dependent hysteresis nonlinearity of the flexible beam driven by MFCs significantly affects positioning accuracy. This study utilizes a new Hammerstein model to describe the hysteresis of an MFC system and proposes a control strategy that combines adaptive iterative learning control and inverse compensation. The adaptive iterative learning control allows the learning gain parameters to be adjusted based on the tracking error. A high gain ensures the convergence speed of iterative learning, whereas a small learning gain provides high robustness and convergence accuracy. Trajectory tracking experiments are conducted to verify the effectiveness of the proposed control strategy. Compared with the case of direct inverse compensation, after the sixth iteration, the tracking maximum absolute error, relative error, and corresponding root mean square error of the proposed control strategy reduce by 73% on average. Moreover, compared with the case of P-type iterative learning control combined with inverse compensation, the metrics above reduce by 12% on average. The proposed control strategy offers higher tracking accuracy and greater practicality in the trajectory tracking of MFC systems. [ABSTRACT FROM AUTHOR]

Electro-hydraulic shake table (EHST) is vital seismic testing equipment in earthquake engineering for the assessment of structures subject to dynamic vibration excitations. The EHST system can be generally simplified as an electro-hydraulic servo system with prominent acceleration replication requirement. In order to improve the acceleration tracking performance of a typical EHST system, a novel realtime acceleration tracking strategy combining inverse compensation technique and neural-based adaptive controller is presented in this paper. The traditional three variable controller (TVC) is applied to the EHST system for obtaining a preliminary acceleration tracking accuracy in advance, and then the multi-innovation stochastic gradient algorithm is utilized to estimate the discrete parametric transfer function of the TVC controlled EHST system. Next, the zero magnitude error tracking technique, which is capable of handling non-minimum phase zeros, is exploited to design a stable and casual inverse model, and subsequently the parametric inverse compensation technique for the EHST system is constructed. Finally, a neural-based online adaptive controller is incorporated to the offline designed parametric inverse compensator as an outer loop, and the side effects of the system's inherent nonlinearities, varying dynamics, and external uncertainties are further addressed. The proposed controller is successfully implemented in the control system of a unidirectional EHST test rig using xPC target rapid prototyping technique, and experimental results reveal that a superior acceleration replication performance is achieved in contrast to the other comparative controllers.

• An WFEI method is proposed for delay compensation for real-time hybrid simulation. • The time delay calculated by frequency-domain evaluation index is integrated with inverse compensation method. • Window technique is utilized for time delay calculation in an almost instantaneous manner. • Performance of WFEI Compensation is evaluated and compared with IC and AIC through the RTHS benchmark model. • Robustness of WFEI Compensation is demonstrated using perturbed simulations of the benchmark model. Actuator control plays an essential role to achieve stable and accurate real-time hybrid simulation (RTHS) results. Delay compensation is often used to minimize the desynchronization at the interface between numerical and experimental substructures. In this study, a new delay compensation method is proposed for RTHS, which integrates the inverse compensation method (IC) and frequency-domain evaluation index (FEI). Window technique is utilized to enable FEI for calculation of almost instantaneous time delay and the IC parameter is then adjusted accordingly for optimal compensation. The performance of this windowed FEI compensation (WFEI) is evaluated and compared with that of the IC and the adaptive inverse compensation (AIC) through computational simulations of a benchmark model with different initial estimates of time delay. It is demonstrated that the WFEI compensation not only provides accurate actuator control when initial estimated time delay deviates from actual values but also have good robustness under unpredicted uncertainties of the servo-hydraulic system. [ABSTRACT FROM AUTHOR]

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The hysteresis characteristics, which commonly existed in smart materials–based actuators, play a significant role in precision control technology. In this article, a modified Bouc–Wen model which can describe the asymmetric hysteresis characteristics of piezoelectric ceramic actuators is investigated. The corresponding parameters of the modified Bouc–Wen hysteresis model are identified through a genetic algorithm–based particle swarm optimization algorithm. Compared with independent particle swarm optimization method which is easily trapped in the local extremum, the proposed genetic algorithm–based particle swarm optimization features the strong searching ability both in early global search period and the later local search period. The experimental results show that the asymmetric Bouc–Wen model identified via genetic algorithm–based particle swarm optimization algorithm are more accurate than that identified through independent particle swarm optimization or genetic algorithm approach, and the maximum displacement error and the maximum relative error between the genetic algorithm–based particle swarm optimization model and the experimental value are 0.20 µm and 14.28%, respectively, which are much smaller than that of particle swarm optimization method with 0.67 µm and 47.85% and genetic algorithm method with 0.35 µm and 25%. In order to further verify the accuracy of the identified model, the hysteresis compensation of piezoelectric ceramic actuator was realized using the feedforward controller based on the inverse Bouc–Wen model. [ABSTRACT FROM AUTHOR]

Rate-dependent hysteretic nonlinearity, which is an inherent characteristic of piezoelectric actuators (PEAs), causes a significant challenge in precise motion control of piezoelectric nanopositioning stages. In this paper, by assuming that the model of PEA takes a Hammerstein structure, a novel control strategy that combines iterative learning control (ILC) and the direct inverse of hysteresis is proposed to compensate for both nonlinearities and uncertainties of system simultaneously. Different from those existing direct inverse compensation methods whose control performance highly relies on the accuracy of the hysteresis model, the proposed control strategy is more robust by adding an additional ILC loop. Since ILC is essentially a feedforward control scheme that fully utilizes the input and output information in previous iterations, the tracking precision can be improved promptly in the iteration domain. Comparative experiments are performed to test the efficacy of the proposed algorithm for polynomial, triangular, and step signals. Results show that it is superior to pure proportional–integral (PI) controller and even PI controller combined with inverse compensator in the sense that the root mean square, relative, as well as maximal absolute errors of output tracking have been decreased remarkably within five iterations. [ABSTRACT FROM AUTHOR]

In the fast tool servo (FTS) system for microstructure surface cutting, the dynamic voltage hysteresis of piezoelectric actuators (PEAs) and the cutting force produced in the manufacturing affect the driving accuracy and the cutting performance. For a multi-input-single-output (MISO) cutting system, in this paper, a dynamic hysteresis model based on a rate-dependent Prandtl–Ishlinskii model is proposed. A backpropagation neural network (BPNN) is established to describe the cross-coupling effect between the applied voltage and external load. An inverse dynamic model is developed to compensate the nonlinearity of PEAs. The accuracy of the model and its inverse is discussed and the performance of the inverse feedforward compensator is validated through experiments.

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.

The Krasnoselskii-Pokrovskii (KP) model, as one of the popular operator-based hysteresis models, is commonly used to describe the hysteresis nonlinearities, especially in the smart materials-based actuators. Due to the complex formulation of the KP model, it is a great challenge to construct the inverse for the KP model. In this paper, an inverse multiplicative structure (IMS) is employed to find the inverse of the KP model. The merit of IMS is that this approach is simple to implement and the detailed knowledge of the hysteresis model is not required. However, the IMS technique cannot be directly applied to construct the inverse compensator for the KP model due to its complexity. Toward this problem, a new expression of the KP model is developed, where the input variable is expressed explicitly. With this new expression, the KP model can fit into IMS. Experiments are conducted to validate the effectiveness of the developed inverse compensator on a piezoelectric platform. [ABSTRACT FROM PUBLISHER]

An imaging Fabry–Perot spectrometer (FPS) utilizes multiple piezoelectric actuators (PEAs) to tune the gap spacing between two optical lenses at nanometer level in order to scan the desired optical band. Due to the nonlinear hysteresis in PEAs and the mechanical coupling, the output responses of the PEAs are quite different, which cause unparallel movement of the FPS positioning stage. For precise positioning control, a model for describing the coupled hysteresis effect is proposed in this paper and a decoupling hysteresis feedforward compensator is constructed based on this model. Experimental results show that the proposed model predicts the coupled hysteresis effect very well and the compensator mitigates the coupled hysteresis efficiently. [ABSTRACT FROM PUBLISHER]

Vanadium dioxide (VO_2) undergoes a thermally induced solid-to-solid phase transition, which can be exploited for actuation purposes. VO_2-coated silicon cantilevers demonstrate abrupt curvature changes when their temperature is varied across the phase transition. Unlike the monotonic hysteresis phenomena observed in many other smart materials, the curvature–temperature hysteresis of VO_2 actuators is nonmonotonic due to competing mechanisms associated with the material’s phase transition and the different thermal expansion coefficients of the materials that form the bilayered cantilever. Motivated by the underlying physics, a novel model for the nonmonotonic hysteresis that combines a monotonic Preisach hysteresis operator and a quadratic operator is presented. A constrained least-squares scheme is proposed for model identification, and an effective inverse control scheme is presented for hysteresis compensation. For comparison purposes, a Preisach operator with a signed density function and a single-valued polynomial model are considered. Experimental results show that, for a 300-\mu \m -long actuator, the largest modeling errors with the proposed model, the signed Preisach operator, and the polynomial approximation are 46.8, 80.3, and 483 m^-1, respectively, over the actuated curvature range of [-104,  1846] m^-1. In addition, both the largest tracking error and root-mean-square error under the proposed inversion scheme are only around 10% of those under the polynomial-based inversion scheme. [ABSTRACT FROM PUBLISHER]

Smart material electrohydrostatic actuator based on giant magnetostrictive transducer can meet the requirements of smaller vehicles like the unmanned combat air vehicle as well as in rotating environments like helicopter rotors. In order to analyze and improve the performance of smart material electrohydrostatic actuator, the dynamic hysteresis nonlinear model of giant magnetostrictive transducer is developed based on the relationship between complex permeability and magnetic energy power loss in giant magnetostrictive material, and the inverse model is also derived to design the inverse compensator for improving the linearity of giant magnetostrictive transducer. The experiments show that with the help of inverse compensator, phase lag between the input control signal and the output displacement of giant magnetostrictive transducer is decreased. In addition, the simulation results show that because of the effect of inverse compensator, the flow rate is increased and the growth rate increases with the increase of excitation frequency. [ABSTRACT FROM AUTHOR]

The hysteresis nonlinearity of giant magnetostrictive actuator (GMA) is stress-dependent. In this paper, laboratory experiments were performed to characterize the stress-dependent hysteresis properties of a GMA under different compressive stresses ranging from −11.3 MPa to −24.3 MPa and harmonic current excitation with amplitudes from 0.6 A to 1.2 A. The experimental results showed that remarkable changes in peak-peak displacement responses, hysteresis loop width and hysteresis loss occurred with the increasing of compressive stress; no significant changes in coercive current were observed. Based on the analysis of experimental data and discussion of the influences of the weights of Modified Prandtl-Ishlinskii (MPI) on the characteristics of hysteresis loops, a stress-dependent Prandtl-Ishlinskii (SDPI) model was proposed by extending the MPI model to describe the effects of compressive stress, in which the weights of deadzone operators are extended as function of compressive stress. Good agreements were obtained between the model simulation results and the experimental data. Additional model validations were given by the performances of tracking control experiments with an inverse feedforward controller. The experimental results have clearly shown that the inverse SDPI is effective for compensating stress-dependent hysteresis including major and minor loops. [ABSTRACT FROM AUTHOR]

In order to solve the hysteresis nonlinearity of piezoelectric ceramic actuators, a closed- loop control strategy base on Preisach inverse compensation is proposed in this paper. Firstly, an inverse Preisach model was achieved using the sorting & taxis realization method. And the inverse model was connected in series with the hysteresis of the system to reduce the impact of the hysteresis. Then, a PI controller was designed to attenuate the non-linearity, which could not be compensated by inverse model. Using the designed PI controller, the tracking accuracy could be improved, and the average absolute error decreased to 0. 025 µm. Experiments show that the inverse Preisach model control strategy has good control performance. [ABSTRACT FROM AUTHOR]

Ionic polymer–metal composites (IPMCs) have intrinsic sensing capabilities. Like many other sensing materials, however, IPMC sensors demonstrate strong temperature-dependent behaviors. In this paper, we present the first systematic studies on temperature-dependent IPMC sensing dynamics. A cantilevered IPMC beam, soaked in a water bath with controlled temperature, is excited mechanically at its tip. The empirical frequency response of the sensor, with the tip displacement as an input and the short-circuit current as an output, shows a clear dependence on the bath temperature. The sensing dynamics is modeled with a transfer function with temperature-dependent coefficients. By fitting the values of the coefficients at a set of test temperatures, we capture the temperature dependence of the coefficients with polynomial functions, which can be used to predict the sensing dynamics at other temperatures. We also investigate the inversion of the sensing dynamics, for extracting the mechanical signal given the sensor output. A stable but noncausal inversion algorithm is applied to deal with the unstable zeros of the original sensing dynamics. Inversion with finite preview time is further explored to achieve near real-time decoding of the sensor output. Experimental results with both harmonic stimuli and free vibrations have validated the effectiveness of the proposed modeling and inversion schemes for IPMC sensors under different temperatures. [ABSTRACT FROM PUBLISHER]

Artificial muscles are inherently compliant actuators that exhibit high power density and impressive stroke and force outputs. They have found numerous applications in advanced mechatronic and robotic systems. Termed as lonely stroke , the first quasi-static input–output cycle of artificial muscles is inconsistent with subsequent cycles that are repeatable and exhibit hysteresis. The lonely stroke not only affects the behavior of artificial muscles, but also presents coupling with the subsequent repeatable hysteresis cycles. While lonely stroke has been reported in a number of artificial muscles, existing studies lack systematic experimental characterization procedure. Furthermore, no models or compensation schemes have been proposed. In this work, we propose the first study to systematically characterize, model, and compensate for the hysteresis with lonely stroke property in artificial muscles. The experimental procedure to characterize the hysteresis with lonely stroke behavior is presented. A modeling approach is developed through the expansion of the input range of the Preisach operator, a widely adopted hysteresis model, to physically infeasible region. The effects of such expansion on the model accuracy and computational cost are studied. An iterative algorithm is proposed to compensate for the hysteresis with lonely stroke by approximately inverting the proposed expanded Preisach operator. The effectiveness of the proposed scheme is validated by comprehensive simulation and experimental results. While this study primarily considers super-coiled polymer (SCP) actuators, the proposed model is also validated for several popular artificial muscles. • Artificial muscles exhibit hysteresis with inconsistent first input–output cycle. • Termed as hysteresis with 'lonely stroke', this behavior is systematically studied. • Procedures to experimentally characterize, model, and compensate are presented. • An extended Preisach operator is proposed to accurately capture this behavior. • Proposed methods are demonstrated and verified through simulation and experiments. [ABSTRACT FROM AUTHOR]

A novel dynamic model is proposed for the hysteresis in magnetostrictive actuators by coupling a Preisach operator to an ordinary differential equation, and a parameter identification method is described. An efficient inversion algorithm for a class of Preisach operators with piecewise uniform density functions is then introduced, based upon which an inverse control scheme for the dynamic hysteresis model is presented. Finally the inversion error is quantified and l1 control theory is applied to improve the robustness of inverse compensation. Simulation and experimental results based on a Terfenol-D actuator are provided. [Copyright &y& Elsevier]

In the fast tool servo (FTS) system for microstructure surface cutting, the dynamic voltage hysteresis of piezoelectric actuators (PEAs) and the cutting force produced in the manufacturing affect the driving accuracy and the cutting performance. For a multi-input-single-output (MISO) cutting system, in this paper, a dynamic hysteresis model based on a rate-dependent Prandtl–Ishlinskii model is proposed. A backpropagation neural network (BPNN) is established to describe the cross-coupling effect between the applied voltage and external load. An inverse dynamic model is developed to compensate the nonlinearity of PEAs. The accuracy of the model and its inverse is discussed and the performance of the inverse feedforward compensator is validated through experiments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]