A Wearable Soft Fabric Sleeve for Upper Limb Augmentation

Soft actuators (SAs) have been used in many compliant robotic structure and wearable devices, due to their safe interaction with the wearers. Despite advances, the capability of current SAs is limited by scalability, high hysteresis, and slow responses. In this paper, a new class of soft, scalable, and high-aspect ratio fiber-reinforced hydraulic SAs is introduced. The new SA uses a simple fabrication process of insertion where a hollow elastic rubber tube is directly inserted into a constrained hollow coil, eliminating the need for the manual wrapping of an inextensible fiber around a long elastic structure. To provide high adaptation to the user skin for wearable applications, the new SAs are integrated into flexible fabrics to form a wearable fabric sleeve. To monitor the SA elongation, a soft liquid metal-based fabric piezoresistive sensor is also developed. To capture the nonlinear hysteresis of the SA, a novel asymmetric hysteresis model which only requires five model parameters in its structure is developed and experimentally validated. The new SAs-driven wearable robotic sleeve is scalable, highly flexible, and lightweight. It can also produce a large amount of force of around 23 N per muscle at around 30% elongation, to provide useful assistance to the human upper limbs. Experimental results show that the soft fabric sleeve can augment a user’s performance when working against a load, evidenced by a significant reduction on the muscular effort, as monitored by electromyogram (EMG) signals. The performance of the developed SAs, soft fabric sleeve, soft liquid metal fabric sensor, and nonlinear hysteresis model reveal that they can effectively modulate the level of assistance for the wearer. The new technologies obtained from this work can be potentially implemented in emerging assistive applications, such as rehabilitation, defense, and industry.

Hysteresis Compensation for a Piezoelectric Actuator of Active Helicopter Rotor Using Compound Control

Active rotor with trailing-edge flaps is a promising method to alleviate vibrations and noise level of helicopters. Hysteresis of the piezoelectric actuators used to drive the flaps can degrade the performance of an active rotor. In this study, bench-top tests are conducted to measure the nonlinear hysteresis of a double-acting piezoelectric actuator. Based on the experimental data, a rate-dependent hysteresis model is established by combining a Bouc–Wen model and a transfer function of a second order system. Good agreement is exhibited between the model outputs and the measured results for different frequencies. A compound control regime composed of a feedforward compensator and PID (Proportional–Integral–Derivative) feedback control is developed to suppress the hysteresis of this actuator. Bench-top test results demonstrate that this compound control regime is capable to suppress hysteresis at different frequencies from 10 Hz to 60 Hz, and errors between the desired actuator outputs and the measured outputs are reduced dramatically at different frequencies, revealing that this compound control regime has the potential to be implemented in an active helicopter rotor to suppress actuator hysteresis.

Hysteresis modelling and compensation for piezoelectric actuator using Jaya-BP neural network

This paper proposes a new training algorithm using a hybrid Jaya-back propagation algorithm (called H-Jaya) to optimize the neural network weights, which is applied to identify the nonlinear hysteresis Piezoelectric actuator based on the experimental input-output data. The identified H-Jaya-neural model will be used to design an advanced feed-forward (FF) controller for compensating the hysteresis nonlinearity. Furthermore as to improve the tracking performance, a feed-forward-feedback control scheme is conducted. To evaluate the effectiveness of the proposed approach, firstly, it is tested through identifying the nonlinear hysteresis of Piezoelectric (PZT) actuator and compared with other meta-heuristic techniques, including differential evolution (DE), particle swarm optimization (PSO), and Jaya. Then, the accuracy of the hysteresis model-based compensator is evaluated under various control experiments using the piezoelectric actuator. The results of experiments executed on PZT actuator configured with a PZS001 from Thorlabs prove that the proposed approach obtains an excellent performance in hysteresis modeling and compensation.

Design and development of MR damper for two wheeler application and Kwok model parameters tuning for designed damper

Magnetorheological dampers have been the interest of many researchers for a few decades for the reason of being an effective and rapidly progressing technology in the field of semi-active controlled suspension. The dynamic behaviour of these devices with nonlinear hysteresis is quite a complicated phenomenon. Hence, this paper aims at the design, modelling and simulation of a custom-made MR damper for a two-wheeler vehicle. The Kwok model has been chosen to mathematically model the MR damper. The model parameters have been optimised by minimizing the error difference between experimental and model-generated force results. A PID control is designed to control the damper effectively depending on the deflection of the damper. The two-wheeler vehicle modelled with four degrees of freedom is coupled with a mathematical model of MR damper in front and rear suspension. Further, the dynamic analysis has been performed in MATLAB/Simulink considering random road input for different velocities and current input conditions. The improved performance of MR damper was observed in suppressing road irregularities using a PID controller. As an implementation part of the work, the developed damper has been implemented in a two wheeler vehicle for performance evaluation at on-road testing conditions. The results showed significant improvement in damper performance with increment of constant current controlling MR dampers.

Electronic Surveillance and Security Applications of Magnetic Microwires

Applications in security and electronic surveillance require a combination of excellent magnetic softness with good mechanical and anticorrosive properties and low dimensionality. We overviewed the feasibility of using glass-coated microwires for electronic article surveillance and security applications, as well as different routes of tuning the magnetic properties of individual microwires or microwire arrays, making them quite attractive for electronic article surveillance and security applications. We provide the routes for tuning the hysteresis loops’ nonlinearity by the magnetostatic interaction between the microwires in the arrays of different types of amorphous microwires. The presence of neighboring microwire (either Fe- or Co-based) significantly affects the hysteresis loop of the whole microwires array. In a microwires array containing magnetically bistable microwires, we observed splitting of the initially rectangular hysteresis loop with a number of Barkhausen jumps correlated with the number of magnetically bistable microwires. Essentially, nonlinear and irregular hysteresis loops have been observed in mixed arrays containing Fe- and Co-rich microwires. The obtained nonlinearity in hysteresis loops allowed to increase the harmonics and tune their magnetic field dependencies. On the other hand, several routes allowing to tune the switching field by either postprocessing or modifying the magnetoelastic anisotropy have been reviewed. Nonlinear hysteresis loops have been also observed upon devitrification of amorphous microwires. Semihard magnetic microwires have been obtained by annealing of Fe–Pt–Si microwires. The observed unique combination of magnetic properties together with thin dimensions and excellent mechanical and anticorrosive properties provide excellent perspectives for the use of glass-coated microwires for security and electronic surveillance applications.

Review of Neural Network Algorithm and its Application in Temperature Control of Distillation Tower

Distillation process is a complex process of conduction, mass transfer and heat conduction, which is mainly manifested as follows: The mechanism is complex and changeable with uncertainty; the process is multivariate and strong coupling; the system is nonlinear, hysteresis and time-varying. Therefore, traditional control methods are difficult to accurately control, but neural networks can greatly improve this problem. This article introduces the basic concepts of distillation tower temperature control, comprehensively introduces the application of various neural network algorithms in distillation tower temperature control, and compares their advantages and disadvantages and their effect. At present, there are many researches on neural network control of distillation tower temperature. The methods are different and each has its own merits. This article has carried out a systematic review to provide reference for the development of related industries.

Compensation method for complex hysteresis characteristics on piezoelectric actuator based on Separated Level-loop Prandtl–Ishlinskii model

Piezoelectric ceramic actuators exhibit nonlinear hysteresis characteristics owing to their material properties. To modify the inverse piezoelectric effect as an ideal linear execution, the classical Prandtl–Ishlinskii (PI) model is usually used for compensation by feedforward control. The PI model compensates well on simple hysteresis characteristics. However, when the output requirements are complex, the PI model demonstrates uneven compensation accuracy on the complex hysteresis characteristics and cannot achieve an accuracy similar to that of simple hysteresis. This paper proposes a simplification of complex hysteresis: Separated Level-loop PI (SLPI) model. First, we use a loop-separation logic algorithm to simplify the complex hysteresis characteristics to obtain hysteresis in the form of single loops with loop levels and vertexes. Second, the hysteresis characteristics of each loop are independently modeled using the PI model. Finally, the inverse model is reconstructed using a rollback method to restore a positive sequence of the feedforward voltage; then, the feedforward voltage is input as a compensation value to achieve higher and more uniform accuracy. Experiments and discussions show that the SLPI model can effectively improve the compensation results of complex hysteresis characteristics; moreover, the average compensation accuracy difference between single hysteresis loops was reduced.

A general inverse control model of a magneto-rheological damper based on neural network

Because of the nonlinear hysteresis characteristics of the magneto-rheological damper, the damper’s inverse model has disadvantages of low fitting accuracy and poor practicality. Therefore, in this study, an optimized genetic algorithm has been proposed to optimize the back propagation neural network’s initial weights and threshold. Compared with other damper controllers, the proposed inverse model improves the control current’s prediction accuracy and tracks the desired damping force in real time. Moreover, the proposed inverse model and designed fuzzy controller are applied to the 1/4 vehicle suspension system simulation. The obtained results show that the optimized neural network model can be applied to a practical control. The root mean square value of body acceleration of semi-active suspension is lower than that of passive suspension under different road excitation. This method provides a foundation for the accurate modeling and semi-active control of the magneto-rheological damper.

Simulation Modeling of Unidirectional Dynamic Remagnetization of Electrical Steel

The problem of modeling unidirectional dynamic remagnetization of electrical steel in periodic modes, in-cluding sinusoidal and non-sinusoidal changes in the average cross-section of the induction sheet, is consid-ered and solved. The relevance of the problem under consideration is determined by the fact that when design-ing semiconductor pulse converters of power supply systems operating in peak load modes, it becomes neces-sary to calculate their characteristics and parameters with a more accurate setting of the magnetic characteris-tics of steel, taking into account the real processes of dynamic magnetization reversal observed in the magnetic circuits of transformers and chokes. The proposed simulation model of dynamic magnetization reversal of a ferromagnet allows us to describe with acceptable accuracy the nonlinear, hysteresis and dynamic properties and characteristics of steel, including hysteresis loops, the main magnetization curves, and losses. The performed modernization of the classical Giles – Atherton(JA) model makes it possible to take into account and approxi-mately describe the eddy currents and magnetic viscosity that appear during dynamic remagnetization. The de-veloped numerical procedure allows you to identify the parameters of the constructed model and configure it. The results of the performed validation of the model using experimental data confirm the possibility of its application to describe the characteristics of electrical steel in dynamic modes of remagnetization.

Adaptive active vibration control for piezoelectric smart structure with online hysteresis identification and compensation

Smart structure vibration reduction based on adaptive active vibration control has become a hot research spot in recent years. A filtered-U least mean square algorithm based on an infinite impulse response filter structure is used to solve the interference of controller output to reference signal. The filtered-U least mean square algorithm is very suitable for the nonlinear vibration control of the flexible structure. This study focuses on the analysis and implementation of an adaptive active vibration control system for smart structure with a surface-bonded piezoelectric actuator. The piezoelectric actuator contained in the secondary path has nonlinear hysteresis property. The nonlinear hysteresis property will cause a nonlinear relationship between the structural vibration response and the control voltage, which deteriorates the robustness and control effect of the adaptive control. This study designs an improved version of the filtered-U least mean square algorithm with online hysteresis identification and compensation (filtered-U least mean square–online hysteresis identification and compensation) based on a discrete Prandtl–Ishlinskii model. The Prandtl–Ishlinskii model parameters of the nonlinear hysteresis property are identified online based on the least mean square algorithm. Based on the identified Prandtl–Ishlinskii model parameters, an inverse hysteresis compensator is established for feedforward compensation in the secondary path. Simulation results show that the proposed method can dynamically compensate the hysteresis nonlinearity of the secondary path, linearizing the nonlinear hysteresis. The vibration reduction effect of the proposed method is obviously better than that of other competing methods. A piezoelectric smart cantilever plate with PZT (or lead zirconate titanate, Pb (Zr, Ti)) actuators and sensors is designed to demonstrate the validity and efficiency of the proposed method by experiments. Experiment results demonstrate that the adverse effect of nonlinear hysteresis is eliminated well after feedforward hysteresis compensation is introduced; the unexpected frequency vibration caused by the hysteresis property is suppressed. The proposed methodology possesses an important advantage in application of the adaptive active vibration control of the piezoelectric smart structure.