Hysteresis Problem Research Articles

Proportional myoelectric and compensating control of a cable-conduit mechanism-driven upper limb exoskeleton

Recent studies have indicated that human motion recognition based on surface electromyography (sEMG) is a reliable and natural method for achieving motion intention. However, achieving accurate estimates of intended motion using a low computational cost is the main challenge in this scenario. In this study, a proportional myoelectric and compensating control method for estimating and assisting human motion intention with a cable-conduit mechanism-driven upper limb exoskeleton was proposed. The integral signal of sEMG and its time-delayed signals were applied as a new feature vector to represent the role of sEMG, which ensured the accuracy and real-time performance of motion estimation. An integrated circuit was used to reduce time of feature extraction. A feed-forward compensator was designed to compensate for the effect of the hysteresis problem in the exoskeleton, which is inevitable when the cable-conduit mechanism was applied to reduce the exoskeleton weight. The model-free control method based on PID method and least squares support vector machine were applied to avoid calculating the complex biomechanical model of human upper limb and the dynamic model of exoskeleton. Experimental results validated the proposed method. The average values of the root-mean-square difference (RMSD) for motion estimation were 0.0579 ± 0.0085 [motion with constant pace (CP)] and 0.0845 ± 0.0137 [motion with variable pace (VP)]. The Bland–Altman analysis results showed that the estimated angle of the proposed method was consistent with the actual angle. The performance of the control method was good, and the accuracies were 98.5608% ± 0.4485% (motion with CP) and 96.6119% ± 0.6628% (motion with VP).

Extended Fuzzy Adaptive Event-Triggered Compensation Control for Uncertain Nonlinear Systems With Input Hysteresis

This paper addresses the problem of adaptive compensation event-triggered control for uncertain nonlinear systems with input hysteresis. The communication resources cannot be saved effectively for the considered systems, as traditional event-triggered mechanisms do not consider input hysteresis with nonlinear gain. In a network control system, how to simultaneously overcome the problem of input hysteresis and save communication resources remains a challenge. In this paper, an extended fuzzy approximation method is proposed to estimate uncertain items for control design. The method considers the time-varying error of the approximation instead of viewing the error as a bounded constant. Furthermore, in combination with the extended fuzzy approximation method, an adaptive event-triggered compensation control method for uncertain nonlinear systems with input hysteresis is designed. Under the proposed event-triggered mechanism, the control method can effectively compensate for the input hysteresis and simultaneously save communication resources. Finally, this method is verified through two simulation experiments that the occupation of communication source has been reduced and the stability of the considered system can be guaranteed effectively.

Adaptive Fuzzy Control for Stochastic Pure-Feedback Nonlinear Systems with Unknown Hysteresis and External Disturbance

This paper solves the tracking control problem of a class of stochastic pure-feedback nonlinear systems with external disturbances and unknown hysteresis. By using the mean-value theorem, the problem of pure-feedback nonlinear function is solved. The direction-unknown hysteresis problem is solved with the aid of the Nussbaum function. The external disturbance problems can be solved by defining new Lyapunov functions. Using the backstepping technique, a new adaptive fuzzy control scheme is proposed. The results show that the proposed control scheme ensures that all signals of the closed-loop system are semiglobally uniformly bounded and the tracking error converges to the small neighborhood of origin in the sense of mean quartic value. Simulation results illustrate the effectiveness of the proposed control scheme.

Resolving Hysteresis in Perovskite Solar Cells with Rapid Flame‐Processed Cobalt‐Doped TiO2

AbstractTo further increase the open‐circuit voltage (V oc) of perovskite solar cells (PSCs), many efforts have been devoted to doping the TiO2 electron transport/selective layers by using metal dopants with higher electronegativity than Ti. However, those dopants can introduce undesired charge traps that hinder charge transport through TiO2, so the improvement in the V oc is often accompanied by an undesired photocurrent density–voltage (J–V) hysteresis problem. Herein, it is demonstrated that the use of a rapid flame doping process (40 s) to introduce cobalt dopant into TiO2 not only solves the J–V hysteresis problem but also increases the V oc and power conversion efficiency of both mesoscopic and planar PSCs. The reasons for the simultaneous improvements are two fold. First, the flame‐doped Co‐TiO2 film forms Co‐Ov (cobalt dopant‐oxygen vacancy) pairs and hence reduces the number density of Ti3+ trap states. Second, Co doping upshifts the band structure of TiO2, facilitating efficient charge extraction. As a result, for planar PSCs, the flame doping of Co increases the efficiency from 17.1% to 18.0% while reducing the hysteresis from 16.0% to 1.7%. Similarly, for mesoscopic PSCs, the flame doping of Co increases the efficiency from 18.5% to 20.0% while reducing the hysteresis from 7.0% to 0.1%.

Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network.

This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.

An additive dripping technique using diphenyl ether for tuning perovskite crystallization for high-efficiency solar cells

Controlling the morphology of the MAPbI3−xCl x active layer has remained a challenge towards advancing perovskite solar cells (PvSCs). Here, we demonstrate that a low temperature additive dripping (AD) treatment step, using diphenyl ether (DPE), can significantly improve the power conversion efficiency (PCE), compared to the control device using chlorobenzene (CB), by 15% up to 16.64%, with a high current density (JSC) of 22.67 mA/cm2. We chose DPE for its small and appropriate dipole moment to adjust the solubility of the MAPbI3−xCl x precursor during the formation of the intermediate phase and the MAPbI3−xCl x phase. The low DPE vapor pressure provides a longer processing window for the removal of residual dimethylformamide (DMF), during the annealing process, for improved perovskite formation. Imaging and X-ray analysis both reveal that the MAPbI3−xCl x film exhibits enlarged grains with increased crystallinity. Together, these improvements result in reduced carrier recombination and hole trap-state density in the MAPbI3−xCl x film, while minimizing the hysteresis problem typical of PvSCs. These results show thatthe AD approach is a promising technique for improving PvSCs.

Target Voltage Hysteresis Behavior and Control Point in the Preparation of Aluminum Oxide Thin Films by Medium Frequency Reactive Magnetron Sputtering

Aluminum oxide thin films were prepared by medium frequency reactive magnetron sputtering. The target voltage hysteresis behavior under different argon partial pressure and target power conditions were studied. The results indicate that the target voltage hysteresis loop of aluminum oxide thin film preparation has typical behavior of that for reactive sputtering deposition of compound films. The target voltage feedback control approach was applied to circumvent the hysteresis problem. The microstructure and chemical composition of the aluminum oxide thin films prepared at different target voltage control points were investigated by X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy and Auger electron spectroscopy. The results indicated that the prepared aluminum oxide thin films, which are compact and mostly amorphous, can be obtained with target voltage control point in the range of 25~35%.

Adaptive Neural Tracking Control of Switched Stochastic Pure-Feedback Nonlinear Systems With Unknown Bouc-Wen Hysteresis Input.

This paper aims to analyze the problem of adaptive neural network (NN) tracking control for a class of switched stochastic nonlinear pure-feedback systems with unknown direction hysteresis. In the light of recent studies on the hysteresis phenomenon in the field of nonlinear switched systems, this paper focuses on Bouc-Wen hysteresis model with unknown parameters and direction conditions. To simplify the control design, the following procedure is applied. Prior to tackling the unknown direction hysteresis problem based on the Nussbaum function and the backstepping techniques, the pure-feedback structure difficulty is governed by the mean value theorem. Furthermore, an optimized adaptation method is utilized to cope with computational burden. Universal approximation capability of radial basis function NNs and Lyapunov function method is synthesized to develop an adaptive NN tracking control scheme. It is demonstrated that under arbitrary deterministic switching, the presented controller can guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability and the tracking error converges to a neighborhood of the origin. Finally, two simulation examples are given to illustrate the advantages of the proposed control design approach.

Multiple states in the flow through a sluice gate

This paper addresses the problem of hydraulic hysteresis for a supercritical open channel flow approaching a sluice gate when subcritical flow can establish downstream, against the gate. The possible flow configurations across the gate are classified on the basis of the Froude number of the incoming and downstream flows, and of the ratio of gate opening to the upstream supercritical flow depth. Within the above parameter space, two regions exist in which the problem admits a dual solution, that is, two different flow configurations can establish for the same gate opening and undisturbed flow conditions. In one of these regions, both smooth flow (i.e. the fluid flows under the gate without interacting with the gate) and free outflow conditions can establish; in the other region, both smooth flow and submerged flow can establish. For flow conditions in the above regions, the configuration that actually establishes depends on the previous history of the flow, thus implying the hysteretic character of the flow.

A Fiber Bragg Grating-Based Dynamic Tension Detection System for Overhead Transmission Line Galloping.

Galloping of overhead transmission lines (OHTLs) may induce conductor breakage and tower collapse, and there is no effective method for long distance distribution on-line galloping monitoring. To overcome the drawbacks of the conventional galloping monitoring systems, such as sensitivity to electromagnetic interference, the need for onsite power, and short lifetimes, a novel optical remote passive measuring system is proposed in the paper. Firstly, to solve the hysteresis and eccentric load problem in tension sensing, and to extent the dynamic response range, an ‘S’ type elastic element structure with flanges was proposed. Then, a tension experiment was carried out to demonstrate the dynamic response characteristics. Moreover, the designed tension sensor was stretched continuously for 30 min to observe its long time stability. Last but not the least, the sensor was mounted on a 70 m conductor model, and the conductor was oscillated at different frequencies to investigate the dynamic performance of the sensor. The experimental results demonstrate the sensor is suitable for the OHTL galloping detection. Compared with the conventional sensors for OHTL monitoring, the system has many advantages, such as easy installation, no flashover risk, distribution monitoring, better bandwidth, improved accuracy and higher reliability.

Side effects of nonlinear profit taxes in an evolutionary market entry model: Abrupt changes, coexisting attractors and hysteresis problems

In order to demonstrate that nonlinear tax systems may have surprising and potentially undesirable side effects, we develop an evolutionary market entry model in which firms decide on the basis of past profit opportunities whether or not to enter a competitive market. Our main focus is on the case of a proportional tax on positive profits. Such a piecewise-linear tax scheme induces a kink in the firms’ profit functions, and may lead to abrupt changes in a market's dynamics, coexisting attractors and hysteresis problems. Since these phenomena can also be observed in more general models, a proper understanding of their basic mechanism may be helpful to explain the intricate behavior of many economic systems.

Mastering hysteresis in magnetocaloric materials.

Hysteresis is more than just an interesting oddity that occurs in materials with a first-order transition. It is a real obstacle on the path from existing laboratory-scale prototypes of magnetic refrigerators towards commercialization of this potentially disruptive cooling technology. Indeed, the reversibility of the magnetocaloric effect, being essential for magnetic heat pumps, strongly depends on the width of the thermal hysteresis and, therefore, it is necessary to understand the mechanisms causing hysteresis and to find solutions to minimize losses associated with thermal hysteresis in order to maximize the efficiency of magnetic cooling devices. In this work, we discuss the fundamental aspects that can contribute to thermal hysteresis and the strategies that we are developing to at least partially overcome the hysteresis problem in some selected classes of magnetocaloric materials with large application potential. In doing so, we refer to the most relevant classes of magnetic refrigerants La-Fe-Si-, Heusler- and Fe2P-type compounds.This article is part of the themed issue 'Taking the temperature of phase transitions in cool materials'.

Time-Dependent Negative Capacitance Effects in Al2O3/BaTiO3 Bilayers.

The negative capacitance (NC) effects in ferroelectric materials have emerged as the possible solution to low-power transistor devices and high-charge-density capacitors. Although the steep switching characteristic (subthreshold swing < sub-60 mV/dec) has been demonstrated in various devices combining the conventional transistors with ferroelectric gates, the actual applications of the NC effects are still some way off owing to the inherent hysteresis problem. This work reinterpreted the hysteretic properties of the NC effects within the time domain and demonstrated that capacitance (charge) boosting could be achieved without the hysteresis from the Al2O3/BaTiO3 bilayer capacitors through short-pulse charging. This work revealed that the hysteresis phenomenon in NC devices originated from the dielectric leakage of the dielectric layer. The suppression of charge injection via the dielectric leakage, which usually takes time, inhibits complete ferroelectric polarization switching during a short pulse time. It was demonstrated that a nonhysteretic NC effect can be achieved only within certain limited time and voltage ranges, but that these are sufficient for critical device applications.

Adaptive neural networks output feedback dynamic surface control design for MIMO pure-feedback nonlinear systems with hysteresis

An adaptive neural networks (NNs) output feedback tracking control approach is proposed for a class of multi-input and multi-output (MIMO) pure-feedback nonlinear systems with unknown backlash-like hysteresis and immeasurable states. Radial basis function neural networks (RBF NNs) are utilized to approximate the unknown nonlinear functions of the controlled systems, and a state observer is designed to estimate the unmeasured states. The filtered signals are introduced to circumvent algebraic loop problem encountered in the implementation of the controller, and an adaptive compensation technique are used to solve the problem of unknown backlash-like hysteresis. Based on the designed state observers, and combining the backstepping and dynamic surface control (DSC) techniques, an adaptive NN output feedback tracking control approach is developed. The proposed method not only overcomes the problem of “explosion of complexity” inherent in the backstepping control design but also guarantees that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking errors converge to a small neighborhood of the origin. Two simulation examples are provided to show the effectiveness of the proposed approach.

Adaptive Fuzzy Control for a Class of Stochastic Pure-Feedback Nonlinear Systems With Unknown Hysteresis

This paper addresses the problem of adaptive fuzzy control for a class of stochastic pure-feedback nonlinear systems with unknown direction hysteresis. Compared with the existing researches on hysteresis problem, the stochastic disturbances and the unknown hysteresis are simultaneously considered in the pure-feedback systems. In addition, the hysteresis parameters as well as the direction of hysteresis are unknown. By introducing an auxiliary virtual controller and employing the new properties of Nussbaum function, the major technique difficulty arising from the unknown direction hysteresis is overcome. Based on the fuzzy logic system's online approximation capability, a novel adaptive fuzzy control scheme is presented via the backstepping technique. It is shown that the proposed control scheme guarantees that all the signals of the closed-loop system are semiglobally uniformly bounded in probability, and the tracking error converges to a neighborhood of the origin in the sense of mean quantic value. Finally, simulation results further demonstrate the effectiveness of the proposed control scheme.

A Lithium-Ion Battery Simulator Based on a Diffusion and Switching Overpotential Hybrid Model for Dynamic Discharging Behavior and Runtime Predictions

A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF) and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.

Lead Halide Perovskite Photovoltaic as a Model p-i-n Diode.

The lead halide perovskite photovoltaic cells, especially the iodide compound CH3NH3PbI3 family, exhibited enormous progress in the energy conversion efficiency in the past few years. Although the first attempt to use the perovskite was as a sensitizer in a dye-sensitized solar cell, it has been recognized at the early stage of the development that the working of the perovskite photovoltaics is akin to that of the inorganic thin film solar cells. In fact, theoretically perovskite is always treated as an ordinary direct band gap semiconductor and hence the perovskite photovoltaics as a p-i-n diode. Despite this recognition, research effort along this line of thought is still in pieces and incomplete. Different measurements have been applied to different types of devices (different not only in the materials but also in the cell structures), making it difficult to have a coherent picture. To make the situation worse, the perovskite photovoltaics have been plagued by the irreproducible optoelectronic properties, most notably the sweep direction dependent current-voltage relationship, the hysteresis problem. Under such circumstances, it is naturally very difficult to analyze the data. Therefore, we set out to make hysteresis-free samples and apply time-tested models and numerical tools developed in the field of inorganic semiconductors. A series of electrical measurements have been performed on one type of CH3NH3PbI3 photovoltaic cells, in which a special attention was paid to ensure that their electronic reproducibility was better than the fitting error in the numerical analysis. The data can be quantitatively explained in terms of the established models of inorganic semiconductors: current/voltage relationship can be very well described by a two-diode model, while impedance spectroscopy revealed the presence of a thick intrinsic layer with the help of a numerical solver, SCAPS, developed for thin film solar cell analysis. These results point to that CH3NH3PbI3 is an ideal intrinsic semiconductor, which happens to be very robust against accidental doping, and that the perovskite photovoltaic cell is in fact a model p-i-n diode. The analytical methods and diagnostic tools available in the inorganic semiconductor PV cells are useful and should be fully exploited in the effort of improving the efficiency. One outstanding question is why the perovskite stays intrinsic. Considering the defects and impurities that must abound in the perovskite layers formed by the spin-coating process, for example, there must be physicochemical mechanism keeping it from being doped. This may be related to the special band structure making up the band gap in this ionic solid. Understanding the mechanism may open a door for the wider utility of this class of solid.

An Algorithm for Network Fluctuation Hopping Signal Suppression Based on Disturbance Characteristics Decomposition and Feed-Forward Modulation

In the data communication, a time-frequency hopping resonance signal will be created, and this network fluctuation hopping signal must be suppressed in order to improve the network stability. An algorithm for network fluctuation hopping signal suppression based on disturbance characteristics decomposition and feed-forward modulation is proposed, and the disturbance characteristics decomposition for a network fluctuation hopping signal is done in a Hilbert space. A feed-forward modulation filter is created to get the feed-forward modulation suppression of the network fluctuation hopping signal. The simulation results indicate that this algorithm has good real-time data transmission, which can effectively suppress the resonance signal in the network fluctuation hopping, low loss of information, and has the obvious function to resolve the startup hysteresis, server load, tremble and other problems in the large-scale hybrid networking.

Spherical aberration correction with threefold symmetric line currents

It has been shown that N-fold symmetric line current (henceforth denoted as N-SYLC) produces 2N-pole magnetic fields. In this paper, a threefold symmetric line current (N3-SYLC in short) is proposed for correcting 3rd order spherical aberration of round lenses. N3-SYLC can be realized without using magnetic materials, which makes it free of the problems of hysteresis, inhomogeneity and saturation. We investigate theoretically the basic properties of an N3-SYLC configuration which can in principle be realized by simple wires. By optimizing the parameters of a system with beam energy of 5.5keV, the required excitation current for correcting 3rd order spherical aberration coefficient of 400mm is less than 1AT, and the residual higher order aberrations can be kept sufficiently small to obtain beam size of less than 1nm for initial slopes up to 5mrad.

Design of the [formula omitted] robust control for the piezoelectric actuator based on chaos optimization algorithm

In order to improve the performance of the piezoelectric actuator and considering the uncertain factors in the working process of the piezoelectric actuator, a novel H∞ robust controller is developed. Firstly, the hysteresis characteristic of the piezoelectric actuator is studied and the fuzzy least square support vector machine is adopted to solve the hysteresis problem, then the model is established; secondly, the method of H∞ robust theory is introduced to control the piezoelectric actuator and in the process of the design, the chaos optimization algorithm is applied to optimize the weight function of the H∞ robust controller, then the final controller is established; at last, the simulation model of the optimized H∞ robust control system and the not optimized system are built, also the intelligent PID control system is built to compare the control effect. Results show that the optimized H∞ robust control system is robust with high stability, which can eliminate the disturbance from the outside and has been proved to be an excellent controller.