Inverse Compensation Research Articles

Adaptive Neural Control for Hysteretic Nonlinear Systems With Hysteresis Neural Direct Inverse Compensator and Its Application.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

A nonlinear hysteresis compensation control of hydro-viscous drive in air transport operations

The Hydro-Viscous Drive (HVD) speed regulating system finds extensive application in air transport transmission systems to regulate the stepless speed or conduct overload protection. However, its intrinsic hysteretic behaviors, such as the asymmetric hysteretic and dead zone, could introduce inaccuracy and delay in control applications, posing challenges to system regulation. This paper investigates a Nonlinear Hysteresis Compensation Control (NHCC) that consists of two parts to control the HVD output speed by operating the valve under different engine operating conditions. In the first part, the Inverse Hysteresis Compensator (IHC) based on major loop data is introduced for the asymmetric hysteresis characterization and compensation of the HVD speed control system of the power generation and distribution, which aims to reduce the hysteresis and dead zone effect and expand the effective input range. In the second part, the Active Disturbance Rejection Controller (ADRC) is employed to mitigate the hysteresis effects of the compensated system and remove the steady-state error, which allows real-time compensation of the estimated perturbations as state feedback to achieve the required performance. An experimental laboratory station has been fabricated to evaluate the proposed method. The test results show that the NHCC method can regulate the fan speed to the desired value (|e|< 45 r/min at steady state) and broaden the effective input range to the full range under different engine conditions. Besides, the proposed control method can reduce the non-linearity of the input and output curves (from 18% to 4%) and compensate for the asymmetric hysteresis (from 38% to 5%).

Inversion-based fuzzy adaptive control with prespecifiable tracking accuracy for uncertain hysteretic systems

Inversion-based control strategies have the outstanding effectiveness in the compensation for hysteresis nonlinearities. However, when the plant is driven by smart material-based actuator with the information absence of hysteresis output, there still exist technical gaps in the construction of hysteresis inverse controller. Therefore, this study aims to address this gap by proposing a novel control scheme. Technically, a novel hysteresis inverse algorithm, significantly reducing computational burden, has been proposed based on a novel adaptive estimation method for the unknown parameter (density function) in the Preisach-typed hysteresis nonlinearity. Furthermore, the inverse compensation error, the nonlinearities and uncertainties appeared in the controlled plant are accommodated by the innovative adaptive fuzzy feedback controller. With our scheme, it can ensure that the closed-loop stability, predefined steady-stated tracking performance and the convergence of algorithms including inverse algorithm and adaptive updating laws. In addition to theoretical analysis, we have validated the effectiveness of the proposed control scheme through simulation comparisons and experimental results conducted by the real-life piezoelectric actuator.

Modeling of dynamic characteristics and μ-synthesis control of piezoelectric positioning platform

Abstract The dynamic hysteresis nonlinearity and various uncertainties of the piezoelectric positioning platform are critical factors limiting its high-precision applications. To address this, the dynamic characteristics of the piezoelectric positioning platform under various loads are identified based on the linearization of the Prandtl-Ishlinskii inverse model. The inverse compensation error and load disturbances are attributed to model uncertainties, which are modeled in the frequency domain. A μ-synthesis controller is designed to improve the tracking accuracy of the piezoelectric positioning platform under uncertainty disturbances. Experimental results indicate that with the μ-synthesis controller, the piezoelectric positioning platform achieves a relative error (RE) of less than 2.71% when unloaded. The RE is less than 6.03% for load disturbances of 100g, 200g, and 300g, respectively. These results demonstrate that the designed controller meets the high-precision positioning performance requirements under various load disturbance conditions.

Adaptive neural chattering suppression pseudo inverse active control for a class of micromilling systems with multiaxis piezoelectric actuators

For a multi-axis nonlinear milling system with piezoelectric actuators, a pseudo inverse dynamic surface active control scheme with adaptive fuzzy output-feedback is developed. The main contributions can be characterized as following: (1) to overcome the hysteresis non-linearities in the piezoelectric actuators, the new hysteresis pseudo inverse compensators are established to replace the conventional hysteresis direct-inverse model construction, which is a very challenging or even impossible work. The hysteresis pseudo inverse means that the hysteresis direct-inverse model is no longer essential any more, instead, a search algorithm for the actual control signal from the designed hysteretic temporary control laws are proposed to alleviate the hysteresis nonlinearities effectively; (2) to realized high-precision tracking control and mitigate chattering suppression in the micromilling systems, the fuzzy logic systems, the novel dynamic surface control algorithms and the modified high-gain states observer are designed to approximise the unknown continuous functions of the micromilling systems, design the high performance controller and estimate the immeasurable states in the control system, respectively; (3) the hardware-in-loop simulation platform is constructed and the experiments of tracking control and chattering suppression are implemented to validate the effectivity of the developed control scheme.

Inverse compensation mechanism-based adaptive fuzzy-neural fault-tolerant control for an uncertain quadrotor UAV

Actuator faults, system uncertainties, and external disturbances can affect tracking control performance of the quadrotor UAV, and even cause the instability of the system. To address this issue, an adaptive fuzzy-neural fault-tolerant control (AFNFTC) scheme is proposed for the quadrotor UAV integrated with inverse compensation strategy and fuzzy neural networks (FNNs) technology. Firstly, we design an inverse compensation mechanism to address actuator faults and utilize the approximation capabilities of the FNNs to compensate for system uncertainties. Then, based on the Lyapunov direct method, the AFNFTC scheme is designed to ensure the stability of the quadrotor UAV. Under the proposed scheme, the closed-loop system for the quadrotor UAV is proven to be uniformly bounded, and the tracking error of the quadrotor UAV is demonstrated to converge to a small compact set ultimately by appropriately selecting design parameters. Finally, the feasibility and effectiveness of the proposed scheme is validated through simulation results.

Linearizing AFM raster scans acquired using dual-stage lateral scanners

Dual-stage scanners can successfully increase the range of atomic force microscope (AFM) scanners in space and/or frequency by designing and constructing two nanopositioners with significant differences in static and dynamic characteristics. In such cases, the positioning signal has to be split to meet the displacement and frequency limitations of each stage. Without closed-loop displacement measurement sensors, linearizing images acquired using signals split in time and frequency can be challenging. A common method, often cumbersome and time consuming, uses inverse model-based feedforward compensation. In this work, we show that image-based linearization can be used to achieve acceptable results for raster scans acquired using dual-stage scanners. In particular, we apply the feedforward and image-based methods to our homemade dual-stage lateral scanner and high-speed AFM (HS-AFM) system. The acquired scans compare well for the two methods, indicating that image-based raster scan linearization can be used in place of inverse model-based feedforward approaches.

Application of adaptive inverse compensation feedforward—MPC feedback control to AFM piezoelectric micro-positioning platform

As the integral constituent of atomic force microscope (AFM), piezoelectric micro-positioning platform (PMP) plays an pivotal role in AFM working accuracy. However, the PMP platform has hysteretic nonlinear characteristics, which bring challenges to high-precision positioning applications, especially in large travel applications. In this paper, the nonlinear Prandtl–Ishlinskii (P–I) model and the linear auto-regressive with extra inputs dynamic model are cascaded to form the Hammerstein model to characterize the dynamic characteristics of PMP, and the mixed algorithm of the beetle antennae search-differential evolution is designed to identify the parameters of the established model. In order to eliminate the hysteresis effect, a compound controller based on adaptive inverse compensation is proposed, which is composed of feedforward controller of P–I inverse model and model prediction controller feedback controller. As the compound controller depends on modeling accuracy, the tracking error caused by model mismatch is improved by adaptive mechanism. The experimental tracking results of sinusoidal signals and triangular signals of different frequencies show that the proposed method can improve the tracking performance of PMP and verify its effectiveness.

Decentralized Implicit Inverse Control for Large-Scale Hysteretic Nonlinear Time-Delay Systems and Its Application on Triple-Axis Giant Magnetostrictive Actuators.

This article proposes fuzzy-logic systems (FLSs)-based decentralized adaptive implicit inverse control scheme for a class of large-scale nonlinear systems with time delays and multihysteretic loops. Our novel algorithms feature hysteretic implicit inverse compensators designed to effectively mitigate multihysteretic loops in large-scale systems. In this article, hysteretic implicit inverse compensators can replace the traditional hysteretic inverse models, which are exceedingly difficult to construct, and no longer necessary. The authors provide three contributions: 1) a searching mechanism to obtain the approximate value of the practical input signal from the so-called hysteretic temporary control law; 2) the arbitrarily small L∞ norm of the tracking error attained by utilizing the proposed initializing technique, which applies the combination of FLSs and a finite covering lemma to deal with time delays; and 3) the construction of a triple-axis giant magnetostrictive motion control platform, which validates the effectiveness of the proposed control scheme and algorithms.

PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

Investigation of Legendre polynomials expanded adaptive controller with inverse compensation for high frequency tracking of electro-hydraulic force systems

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.

Event‐triggered adaptive estimated inverse neural network control of uncertain nonlinear systems with unknown hysteresis effects

AbstractThis article investigates an event‐triggered adaptive estimated inverse control scheme for uncertain nonlinear systems with hysteresis effects, parametric uncertainties, and unknown disturbances. An online estimated inverse hysteresis compensation mechanism is developed, in which an adaptive technique is designed to obtain the value of unknown hysteresis parameters. Compared with common approaches, its biggest advantage lies in the fact that it eliminates the need for experimental determination of hysteresis parameters, thereby reducing time‐consuming offline identification work and enhance the compatibility of the proposed method. Additionally, an adaptive radial basis functions neural network is applied to approximate the unknown disturbances, and its weight coefficients, along with unknown system parameters, are estimated by means of the adaptive method. Furthermore, the introduction of relative threshold event‐triggered control significantly reduces communication costs resulting from the hysteresis compensation. Through Lyapunov analysis, the proposed controller guarantees all signals are bounded and the errors are convergent. Numerical simulation results demonstrate the superiority of the developed controller.

Optimizing the series cascade control structure for nonminimum phase system regulation

AbstractThis work elucidates the control of integrating a nonminimum phase system via a series cascade scheme with fractional‐order P.I. (Proportional–Integral) plus D (Derivative) controller. The traditional Internal Model Control (IMC) is adopted for inner loop controller design. The feedback D controller is synthesized with the outer loop process model, showing the proposed work's universality. The outer loop controller is suggested in the IMC framework after the accountability of fractional‐filter and inverse response compensator. This combination is revealed to enhance performance without compromising robustness. The Riemann sheet principle is explored to compute the stability of the suggested controller. The sensitivity analysis has asserted the robustness. More importantly, the optimal value of controller settings is achieved via the Teaching Learning Based Optimization (TLBO) algorithm. This TLBO algorithm uses an objective function that minimizes Integral Square Error. Two illustrative problems are utilized to examine the recommended control structure's virtue.

Qualitative and semiquantitative analyses of the chemical components of the seed coat and kernel of Ziziphi Spinosae Semen

Ziziphi Spinosae Semen refers to the dried seed of Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chou. The seed is composed of a reddish brown coat and a yellow kernel. A comparative study was conducted to investigate differences in the chemical composition and their relative contents between the seed coat and kernel of Ziziphi Spinosae Semen. First, the chemical compounds found in the seed coat and kernel were characterized and identified using ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). The analytical results tentatively identified 57 chemical compounds based on reference-compound comparison, literature retrieval, and chemical-database (e. g., MassBank) searches; these compounds included 14 triterpenes, 23 flavonoids, 7 alkaloids, 6 carboxylic acids, and 7 other types of compounds. The mass error of the identified compounds was within the mass deviation range of 5×10-6 (5 ppm). Next, two methods of multivariate statistical analysis, namely, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), were used to compare the differential compounds between the two seed parts. A total of 17 differential compounds were screened out via OPLS-DA based on a variable importance in projection (VIP) value of >5. The results revealed that betulinic acid, betulonic acid, alphitolic acid, and jujuboside Ⅰ mainly existed in the seed coat whereas the 13 other compounds, such as spinosin, jujuboside A, and 6‴-feruloylspinosin, mainly existed in the seed kernel. Therefore, these 17 differential compounds can be used to distinguish between the two seed parts. Finally, a semiquantitative method was established using UPLC and a charged aerosol detector (CAD) with inverse gradient compensation in the mobile phase. Six representative compounds with different types were selected to examine the CAD response consistency: magnoflorine (alkaloid), spinosin (flavone), 6‴-feruloylspinosin (flavone), jujuboside A (triterpenoid saponin), jujuboside B (triterpenoid saponin), and betulinic acid (triterpenoid acid). The results showed that the relative standard deviation (RSD) of the average response factors at different levels of these six compounds was 7.04% and that their response intensities were similar. Moreover, each compound in the fingerprint demonstrated good response consistency, and the peak areas obtained directly reflected the contents of each compound. Based on the semiquantitative fingerprints obtained, betulinic acid and oleic acid were considered the main components of the seed coat. The betulinic acid content in the seed coat was approximately 7 times higher than that in the seed kernel. Spinosin, jujuboside A, linoleic acid, betulinic acid, and oleic acid were the main components of the seed kernel. The spinosin content in the seed kernel was 18 times higher than that in the seed coat. In addition, the jujuboside A content in the seed kernel was 24 times higher than that in the seed coat. The proposed method can accurately determine the main components and compare the relative contents of these components in different seed parts. In summary, this study identified the differences in chemical components between the seed coat and kernel of Ziziphi Spinosae Semen and clarified the main components and their relative contents in these parts. The findings can not only provide a basis for the identification of chemical compounds and quality research on different parts of Ziziphi Spinosae Semen but also promote the development and utilization of this traditional Chinese medicine.

Implementation of real-time hybrid simulation using a large shake table with onboard actuators

In recent decades, shake table real-time hybrid simulation (RTHS) has emerged as a promising seismic testing approach for complex engineering structures. However, the current shake table RTHS systems with actuators typically install the dynamic actuators on a reaction wall outside the table, which introduce interaction issues between the dynamic actuators and the shake table. This study proposes a potential improvement by developing a shake table RTHS platform with onboard actuators. The platform incorporates a 6 m × 9 m large-scale shake table where the physical substructure, two dynamic actuators, and the reaction frame are all positioned. To validate the effectiveness of the developed platform, a two-story steel frame is tested by both full structure shake table test and substructure shake table RTHS. In the shake table RTHS, the lower part of the frame serves as the physical substructure and is simultaneously loaded by the shake table and dynamic actuators, while the upper part is numerically analyzed in OpenSees, using OpenFresco and Simulink as intermediate softwares. The test results demonstrate that the developed shake table RTHS platform provides reasonable predictions to the structural seismic responses. In addition, time delay is optimized using both inner and outer loop compensators, highlighting the effectiveness of inverse compensation and adaptive time series (ATS) compensation schemes. This paper successfully develops a shake table RTHS platform using a large shake table with onboard dynamic actuators, providing a valuable seismic testing platform for RTHS of large-scale complex engineering structures.

Optimal fractional-order series cascade controller design: A refined Bode’s ideal transfer function perspective

In process industries, cascade control is comprehensively used for disturbance attenuation. This manuscript presents the control of the non-minimum phase (NMP) system with dead time via a series cascade scheme. An improved fractional-filter-based control scheme encompassing inverse and dead time compensators with analytical tuning is proposed. The design hinges on an enhanced Bode’s ideal transfer function, which incorporates delay and NMP zero to deal with the system’s NMP and dead time. Particle swarm optimization (PSO) is utilized to obtain the optimal assessment of fractional filter-based controller settings by minimizing an objective function. Two benchmark problems are presented to test the efficacy of the recommended controller. The Riemann surface and sensitivity examination are used to realize the stability and robustness of the feedback design. Numerical simulations exhibit the superiority of the suggested controller for closed-loop results.

An improved error cancellation method for high speed FMCW radar based on FPGA platform

AbstractTarget detection of frequency‐modulated continuous wave radar has the problems of high noise and low detection accuracy. The reason is due to the influence of internal hardware and external environment. In response to this issue, this article uses the field programmable gate array platform to perform frequency modulation on the radar module, time‐frequency conversion, autoregression moving average model, inverse proportion function compensation, and frequency domain selection processing on the intermediate frequency in‐phase signals collected by the oscilloscope. The algorithm proposed in this article could effectively reduce noise and system errors. Compared with traditional methods before filtering and variational mode decomposition Hilbert–Huang Transform algorithm, the effective target detection rate of this method is improved by 50%.

Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading.

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide. Deep learning has exhibited promising performance in the grading of DR. Certain deep learning strategies have facilitated convenient regular eye check-ups, which are crucial for managing DR and preventing severe visual impairment. However, the generalization performance on cross-center, cross-vendor, and cross-user test datasets is compromised due to domain shift. Furthermore, the presence of small lesions and the imbalanced grade distribution, resulting from the characteristics of DR grading (e.g., the progressive nature of DR disease and the design of grading standards), complicates image-level domain adaptation for DR grading. The general predictions of the models trained on grade-skewed source domains will be significantly biased toward the majority grades, which further increases the adaptation difficulty. We formulate this problem as a grade-skewed domain adaptation challenge. Under the grade-skewed domain adaptation problem, we propose a novel method for image-level supervised DR grading via Asymmetric Bi-Classifier Discrepancy Minimization (ABiD). First, we propose optimizing the feature extractor by minimizing the discrepancy between the predictions of the asymmetric bi-classifier based on two classification criteria to encourage the exploration of crucial features in adjacent grades and stretch the distribution of adjacent grades in the latent space. Moreover, the classifier difference is maximized by using the forward and inverse distribution compensation mechanism to locate easily confused instances, which avoids pseudolabel bias on the target domain. The experimental results on two public DR datasets and one private DR dataset demonstrate that our method outperforms state-of-the-art methods significantly.

Modeling and Sliding Mode Control Based on Inverse Compensation of Piezo-positioning System

Modeling and Sliding Mode Control Based on Inverse Compensation of Piezo-positioning System

Adaptive Control Method for Conically Shaped Dielectric Elastomer Actuator With Different Loads

In this paper, we present an adaptive control method for a conically shaped dielectric elastomer actuator (CSDEA) with different loads to achieve its tracking control objective. Firstly, a dynamic model of the CSDEA is constructed to describe its asymmetrical, rate-dependent hysteresis behavior and creep behavior simultaneously. Then, an offline parameter identification method based on the nonlinear-least-squares algorithm is presented to obtain the nominal values of the model parameters of the CSDEA corresponding to the load of 200 (g). The obtained values are regarded as the initial values of the adaptive online parameter identification. Next, an adaptive online parameter identification method (AOPIM) based on the least-mean-square algorithm is proposed, which can dynamically calculate and update the model parameter values to cope with the load changing of the CSDEA. Lastly, based on two kinds of analytical inverses of the dynamic model and the proposed AOPIM, two adaptive inverse compensators are respectively designed to realize the tracking control of the CSDEA. The control experiments with different desired trajectories and different loads are implemented to demonstrate the effectiveness of the presented adaptive control method. Since the root-mean-square errors of the results of all control experiments are lower than 2.7%, the presented method is remarkable from the perspective of the practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The dielectric elastomer material has beneficial properties of high energy density, low mass density, large deformation, fast response and good biological compatibility. Thus, the soft actuator based on the dielectric elastomer material (SADE) has shown great potentials for being used in soft robots. Nevertheless, the SADE has the complex nonlinear behaviors, which brings a big challenge for its precision control. To deal with this issue, some approaches have been presented in previous literatures. However, the load of the SADE is usually fixed in these studies. This paper focuses on the tracking control of the SADE with different loads. To this end, a dynamic model of the CSDEA is constructed, whose parameter values are dynamically calculated and updated to cope with the load changing of the CSDEA. Through calculating the analytical inverse of the dynamic model, two adaptive inverse compensators are developed. The control experimental results demonstrate that the presented adaptive control method is effective. Considering that the load of the SADE is usually diverse in practical applications, this study pays the way for the application of the SADE.