Unknown Hysteresis Research Articles

Adaptive tracking control for a class of pure-feedback non-linear systems including actuator hysteresis and dynamic uncertainties

In this study, an adaptive neural network dynamic surface control for a class of pure-feedback non-linear systems with dynamic uncertainties and unknown hysteresis is proposed. The hysteresis is described by a saturated-type Prandtl–Ishlinskii (PI) model, which is more applicable than the traditional PI model. The main advantages of the authors scheme are that by introducing an initialising technique, the ℒ∞ performance of the tracking error can be achieved, the assumption on dynamic uncertainties is relaxed, the explosion of the complexity problem when the hysteresis is fused with back-stepping design can be eliminated, which together with the estimation of vector norm of unknown parameters makes the control law be simplified and the computational burden be greatly reduced. Simulation results are presented to demonstrate the efficiency of the proposed scheme.

Adaptive Friction Compensation for Mechanical Systems with Unknown Deadzone

Friction exists in a wide range of physical systems. On the other hand, deadzone hysteresis nonlinearities seem inevitable in practice especially in mechanical system. In this paper, a class of mechanical systems with dynamic friction and unknown deadzone hysteresis are considered . A state feedback control scheme is proposed by using backstepping techniques. In this control scheme, an approximation of deadzone model is constructed and we use a linear part and a bounded part to approximate hysteresis input. The stability of closed-loop system and output tracking performance can be ensured by this control law and update laws of unknown parameters and the upper bound of external disturbance.

Adaptive tracking of stochastic nonlinear systems with Prandtl–Ishlinskii hysteresis

SUMMARYThis paper deals with adaptive tracking problems for a class of stochastic nonlinear systems with unknown hysteresis nonlinearities. The system considered is in a strict‐feedback form driven by unknown Prandtl–Ishlinskii hysteresis and Wiener noises of unknown covariance. By employing backstepping design techniques and stochastic Lyapunov design method, parameter adaptive laws and control laws are obtained, which ensure that the tracking error can converge to a small residual set around the origin in the sense of mean quartic value. Copyright © 2011 John Wiley & Sons, Ltd.

Adaptive Control of Nonlinear Systems with Unknown Backlash-like Hysteresis

Adaptive Control of Nonlinear Systems with Unknown Backlash-like Hysteresis

Pseudo-inverse-based adaptive control for uncertain discrete time systems preceded by hysteresis

This paper discusses the adaptive control for the uncertain discrete time linear systems preceded by hysteresis nonlinearity described by the Prandtl–Ishlinskii (PI) model. The contribution of the paper is the development of an adaptive algorithm in which a pseudo-inversion is introduced to avoid difficulties of the directly inverse construction for complex hysteresis models, especially for the unknown hysteresis case. In the developed approach, only those parameters in the formulation of the sliding mode controller are adaptively estimated. The stability in the sense that all signals in the loop remain bounded is analyzed. Simulation results show the effectiveness of the proposed algorithm.

Robust adaptive control for a class of perturbed strict-feedback non-linear systems with unknown Prandtl-Ishlinskii hysteresis

This paper deals with robust adaptive control for a class of perturbed strict-feedback non-linear systems preceded by unknown hysteresis non-linearities. By using the Prandtl-Ishlinskii model with play and stop operators and the properties of this model, a robust adaptive control scheme is proposed to mitigate effects of the preceded hysteresis. The global uniform ultimate boundedness of the closed-loop system is achieved, and the effectiveness of the proposed control approach is demonstrated through simulation examples.

Neural network‐based adaptive control of piezoelectric actuators with unknown hysteresis

AbstractThis paper proposes a neural network (NN)‐based adaptive control of piezoelectric actuators with unknown hysteresis. Based on the classical Duhem model described by a differential equation, the explicit solution to the equation is explored and a new hysteresis model is constructed as a linear model in series with a piecewise continuous nonlinear function. An NN‐based dynamic pre‐inversion compensator is designed to cancel out the effect of the hysteresis. With the incorporation of the pre‐inversion compensator, an adaptive control scheme is proposed to have the position of the piezoelectric actuator track the desired trajectory. This paper has three distinct features. First, it applies the NN to online approximate complicated piecewise continuous unknown nonlinear functions in the explicit solution to Duhem model. Second, an observer is designed to estimate the output of hysteresis of piezoelectric actuator based on the system input and output. Third, the stability of the controlled piezoelectric actuator with the observer is guaranteed. Simulation results for a practical system validate the effectiveness of the proposed method in this paper. Copyright © 2008 John Wiley & Sons, Ltd.

Adaptive Neural Control of SISO Time-Delay Nonlinear Systems with Unknown Hysteresis Input

In this paper, adaptive variable structure neural control is investigated for a class of SISO nonlinear systems in a Brunovsky form with state time-varying delays and unknown hysteresis input. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The effect of the unknown hysteresis with the Prandtl-Ishlinskii model is mitigated using the proposed adaptive control. By utilizing the integral-type Lyapunov function, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. Simulation results demonstrate the effectiveness of the approach.

ROBUST ADAPTIVE CONTROL OF NONLINEAR SYSTEMS WITH UNKNOWN INPUT HYSTERESIS WITH KP PRESENTATION

Control of nonlinear systems preceded by unknown hysteresis is a challenging task in industrial applications. In the literature, many mathematical models have been proposed to describe the phenomenon of hysteresis. The challenge addressed here is how to unify those models with available robust control techniques to have the basic requirement of stability of the systems. By exploiting the Krasnosel'skii-Pokrovkii (KP) hysteresis model, this paper proposes a control law which unifies a robust adaptive control technique with the KP model without constructing a hysteresis inverse. The control law can mitigate effects of the hysteresis, ensure global stability of the adaptive system, as well as achieve tracking within a desired precision. The approach is illustrated and verified through simulations performed on a nonlinear system.

Robust adaptive control of a class of nonlinear systems including actuator hysteresis with Prandtl–Ishlinskii presentations

This paper deals with robust adaptive control of a class of nonlinear systems preceded by unknown hysteresis nonlinearities. By using a Prandtl–Ishlinskii model with play and stop operators, we attempt to fuse the model of hysteresis with the available control techniques without necessarily constructing a hysteresis inverse. A robust adaptive control scheme is therefore proposed. The global stability of the adaptive system and tracking a desired trajectory to a certain precision are achieved. Simulation results attained for a nonlinear system are presented to illustrate and further validate the effectiveness of the proposed approach.

Adaptive control of system with hysteresis using neural networks

Abstract An adaptive control scheme is developed for a class of single-input nonlinear systems preceded by unknown hysteresis, which is a non-differentiable and multi-value mapping nonlinearity. The controller based on the three-layer neural network (NN), whose weights are derived from Lyapunov stability analysis, guarantees closed-loop semiglobal stability and convergence of the tracking errors to a small residual set. An example is used to confirm the effectiveness of the proposed control scheme.

Adaptive variable structure control of a class of nonlinear systems with unknown Prandtl-Ishlinskii hysteresis

Control of nonlinear systems preceded by unknown hysteresis nonlinearities is a challenging task and has received increasing attention in recent years due to growing industrial demands involving varied applications. In the literature, many mathematical models have been proposed to describe the hysteresis nonlinearities. The challenge addressed here is how to fuse those hysteresis models with available robust control techniques to have the basic requirement of stability of the system. The purpose of the note is to show such a possibility by using the Prandtl-Ishlinskii (PI) hysteresis model. An adaptive variable structure control approach, serving as an illustration, is fused with the PI model without necessarily constructing a hysteresis inverse. The global stability of the system and tracking a desired trajectory to a certain precision are achieved. Simulation results attained for a nonlinear system are presented to illustrate and further validate the effectiveness of the proposed approach.

Adaptive Output Feedback Control of Systems Preceded by the Preisach-Type Hysteresis

An adaptive output feedback controller is presented for a class of single-input-single-output (SISO) nonlinear systems preceded by an unknown hysteresis nonlinearity represented by the Preisach model. First, a novel model is developed to represent the hysteresis characteristic in order to handle the case where the hysteresis output is not directly measured. The model is motivated by the Preisach model but implemented by the neural networks (NN). Therefore, it is easily used for controller design. Then, a radius-basis-functional-neural-networks (RBF NN) adaptive controller based on the model estimation is presented by combining the high-gain state observer. The updated laws and control laws of the controller are derived from Lyapunov stability theorem, so that the ultimate boundedness of the closed-loop system is guaranteed. At last, an example is used to verify the effectiveness of the controller.

NEURAL SLIDING MODE CONTROL FOR HYSTERESIS SYSTEMS

In this paper, a neural network based adaptive sliding mode control scheme for hysteretic systems is proposed. In this control scheme, a neural network model is utilized to describe the characteristic of hysteresis. Then, the adaptive neural sliding mode controller based on the proposed neural model is presented for a class of single-input nonlinear systems with unknown hysteresis. For the case where the output of hysteresis is unmeasurable, the neural network model is applied to estimate the effect of hysteresis. Based on the model-based estimation, the effect of hysteresis on the performance of the system is compensated.

BACKSTEPPING CONTROL OF A CLASS OF NONLINEAR SYSTEMS PRECEDED BY HYSTERESIS WITH PRANDTL-ISHLINSKII PRESENTATIONS

Control of nonlinear systems preceded by unknown hysteresis nonlinearities is a challenging task. In the literature, many mathematical models have been proposed to describe the hysteresis. The challenge addressed here is how to fuse those hysteresis models with available robust control techniques to have the basic requirement of stability of the system. The purpose of the paper is to show such a possibility by using the Prandtl-Ishlinskii (PI) hysteresis model. A backstepping based robust control approach, serving as an illustration, is fused with the PI model without necessarily constructing a hysteresis inverse. The global stability of the system and tracking a desired trajectory to a certain precision are achieved. Simulations performed on a nonlinear system illustrate and further validate the effectiveness of the proposed approach.

Neural model-based adaptive control for systems with unknown Preisach-type hysteresis

An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks ( NN) . The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semiglobal stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.

Adaptive control of plants with unknown hystereses

For a system with hysteresis, the authors present a parameterized hysteresis model and develop a hysteresis inverse. The authors then design adaptive controllers with an adaptive hysteresis inverse for plants with unknown hysteresis. A new adaptive controller structure is introduced which is capable of achieving a linear parameterization and a linear error model in the presence of a hysteresis nonlinearity. A robust adaptive law is used to update the controller parameters and hysteresis inverse parameters, which ensures the global boundedness of the closed-loop signals for a wide class of of hysteresis models. Simulations show that the use of the adaptive hysteresis inverse leads to major improvements of system performance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Magnetic Ni-Fe-Co alloys with a stepwise hysteresis loop

It is possible to create a cubic recrystallization texture in Ni-Fe-Co alloys with a close-packed hexagonal lattice, which contain ∼50% Ni and up to ∼25% Co. Magnetic properties were investigated on spiral cores of these alloys in the textured state after various heat-treatment regimes in a transverse field. In this case, the previously unknown stepwise hysteresis loop, whose appearance is explained by the interaction of the energies of crystallographic anisotropy and induced uniaxial anisotropy and the external magnetic field, were observed for alloys with a high positive constant of crystallographic anisotropy K1.