Hysteresis Nonlinearity Identification Using New Preisach Model-Based Artificial Neural Network Approach

Preisach model is a well-known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications. Here a novel neural network approach based on the Preisach model is addressed, provides accurate hysteresis nonlinearity modeling in comparison with the classical Preisach model and can be used for many applications such as hysteresis nonlinearity control and identification in SMA and Piezo actuators and performance evaluation in some physical systems such as magnetic materials. To evaluate the proposed approach, an experimental apparatus consisting one-dimensional flexible aluminum beam actuated with an SMA wire is used. It is shown that the proposed ANN-based Preisach model can identify hysteresis nonlinearity more accurately than the classical one. It also has powerful ability to precisely predict the higher-order hysteresis minor loops behavior even though only the first-order reversal data are in use. It is also shown that to get the same precise results in the classical Preisach model, many more data should be used, and this directly increases the experimental cost

From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators

New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83º was achieved when validated against several set-points within the possible range

Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model

In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model's parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model's potential in the field of materials science

An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators

© 2017 Elsevier B.V. To overcome the low positioning accuracy of piezoelectric actuators (PZAs) caused by the hysteresis nonlinearity, this paper proposes an adaptive weighted least squares support vector regression (AWLSSVR) to model the rate-dependent hysteresis of PZA. Firstly, the AWLSSVR hyperparameters are optimized by using particle swarm optimization. Then an adaptive weighting strategy is proposed to eliminate the effects of noises in the training dataset and reduce the sample size at the same time. Finally, the proposed approach is applied to predict the hysteresis of PZA. The results show that the proposed method is more accurate than other versions of least squares support vector regression for training samples with noises, and meanwhile reduces the sample size and speeds up calculation

Hammerstein model for hysteresis characteristics of pneumatic muscle actuators

As a kind of novel compliant actuators, pneumatic muscle actuators (PMAs) have been recently used in wearable devices for rehabilitation, industrial manufacturing and other fields due to their excellent actuation characteristics such as high power/weight ratio, safety and inherent compliance. However, the strong nonlinearity and asymmetrical hysteresis cause difficulties in the accurate control of robots actuated by PMAs. In this paper, a method for hysteresis modeling of PMA based on Hammerstein model is proposed, which introduces the BP neural network into the hysteretic system. In order to overcome the limitation of BP neural network’s single-valued mapping, an extended space input method is adapted while the Modified Prandtl–Ishlinskii model is applied to characterize the hysteretic phenomenon. A conventional PID control is implemented to track the trajectory of PMA with and without the feed-forward hysteresis compensation based on Hammerstein model, and experimental results validate the effectiveness of the designed model which has the advantages of high precision and easy identification

Fuzzy PID Feedback Control of Piezoelectric Actuator with Feedforward Compensation

Piezoelectric actuator is widely used in the field of micro/nanopositioning. However, piezoelectric hysteresis introduces nonlinearity to the system, which is the major obstacle to achieve a precise positioning. In this paper, the Preisach model is employed to describe the hysteresis characteristic of piezoelectric actuator and an inverse Preisach model is developed to construct a feedforward controller. Considering that the analytical expression of inverse Preisach model is difficult to derive and not suitable for practical application, a digital inverse model is established based on the input and output data of a piezoelectric actuator. Moreover, to mitigate the compensation error of the feedforward control, a feedback control scheme is implemented using different types of control algorithms in terms of PID control, fuzzy control, and fuzzy PID control. Extensive simulation studies are carried out using the three kinds of control systems. Comparative investigation reveals that the fuzzy PID control system with feedforward compensation is capable of providing quicker response and better control accuracy than the other two ones. It provides a promising way of precision control for piezoelectric actuator

APPROXIMATION OF HYSTERESIS DENSITY FUNCTION IN STRETCH SENSOR

Stretch SensorTM developed by Images Scientific Instruments Inc, USA, is a unique polymer component that changes resistance when stretched. When sensor is stretched and released it exhibits hysteresis and large relaxation time. For identification of hysteresis and relaxation, Preisach model is a very well-known method. Experiments are carried out using tension tester and the experimental data is used for identification. Modified Preisach model is used for relaxation identification and the experimental data is discretized for analysis of relaxation. Identification is based on first reversal curve of major hysteresis loop and noise error of sensor. It has been observed that if sensor is used in pre-stretch conditions, relaxation time is reduced. Also more the iterations of stretch, hysteresis is reduced and sensor output error is also reduced. Hysteresis and relaxation time cannot be eliminated because they are inherent properties of polymer but can be compensated under specific conditions. Compensation is useful for calibration of the sensor

Shape Memory Alloy Actuators and Sensors for Applications in Minimally Invasive Interventions

Reduced access size in minimally invasive surgery and therapy (MIST) poses several restriction on the design of the dexterous robotic instruments. The instruments should be developed that are slender enough to pass through the small sized incisions and able to effectively operate in a compact workspace. Most existing robotic instruments are operated by big actuators, located outside the patient’s body, that transfer forces to the end effector via cables or magnetically controlled actuation mechanism. These instruments are certainly far from optimal in terms of their cost and the space they require in operating room. The lack of adequate sensing technologies make it very challenging to measure bending of the flexible instruments, and to measure tool-tissue contact forces of the both flexible and rigid instruments during MIST. Therefore, it requires the development of the cost effective miniature actuators and strain/force sensors. Having several unique features such as bio-compatibility, low cost, light weight, large actuation forces and electrical resistivity variations, the shape memory alloys (SMAs) show promising applications both as the actuators and strain sensors in MIST. However, highly nonlinear hysteretic behavior of the SMAs hinders their use as actuators. To overcome this problem, an adaptive artificial neural network (ANN) based Preisach model and a model predictive controller have been developed in this thesis to precisely control the output of the SMA actuators. A novel ultra thin strain sensor is also designed using a superelastic SMA wire, which can be used to measure strain and forces for many surgical and intervention instruments. A da Vinci surgical instrument is sensorized with these sensors in order to validate their force sensing capability