Add to ORKGThis paper discusses the on-line identification of non-local static hysteresis functions, which are encountered in mechanical friction, magnetic materials, and piezoelectric actuators and cause problems by the design of controllers. In this article we want to introduce a compensation method for friction in presliding regime, based on the simplified Leuven Friction Model and on technology borrowed from neural networks. We present a solution how to identify the hysteresis caused by the friction, and how to use this identified model for the compensation of the friction effects. Results from both simulations and experiments will be shown
Abstract: The motion of hydraulic actuators are severly influenced by friction. In this paper the sl...
The present investigation aims at the definition of an efficient and robust neural network-based mod...
A computationally efficient and robust neural network-based model to reproduce the hysteresis phenom...
This paper discusses the on-line identification of non-local static hysteresis functions, which are ...
Abstract—This paper discusses the on-line identification of nonlocal static hysteresis functions, wh...
A novel dynamic friction model, which allows to capture friction hysteresis with non-local memory, ...
This paper discusses the on-line identification of nonlocal static hysteresis functions, which are e...
Compensation of nonlinear friction terms is a most challenging application of high resolution encode...
We present a compensation technique for a friction model, which captures problematic friction effect...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
Abstract: In this paper, a neural network based adaptive sliding mode control scheme for hysteretic ...
This paper proposes a feedforward and feedback combined hysteresis compensation method for a piezoel...
ABSTRACT: In order to model the hysteretic behavior of piezoceramic actuators, a hybrid model is dev...
The paper presents a velocity hysteresis friction model. This model can be used for simulation or co...
This paper presents a novel method to identify both the functional dependence of the Preisach functi...
Abstract: The motion of hydraulic actuators are severly influenced by friction. In this paper the sl...
The present investigation aims at the definition of an efficient and robust neural network-based mod...
A computationally efficient and robust neural network-based model to reproduce the hysteresis phenom...
This paper discusses the on-line identification of non-local static hysteresis functions, which are ...
Abstract—This paper discusses the on-line identification of nonlocal static hysteresis functions, wh...
A novel dynamic friction model, which allows to capture friction hysteresis with non-local memory, ...
This paper discusses the on-line identification of nonlocal static hysteresis functions, which are e...
Compensation of nonlinear friction terms is a most challenging application of high resolution encode...
We present a compensation technique for a friction model, which captures problematic friction effect...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
Abstract: In this paper, a neural network based adaptive sliding mode control scheme for hysteretic ...
This paper proposes a feedforward and feedback combined hysteresis compensation method for a piezoel...
ABSTRACT: In order to model the hysteretic behavior of piezoceramic actuators, a hybrid model is dev...
The paper presents a velocity hysteresis friction model. This model can be used for simulation or co...
This paper presents a novel method to identify both the functional dependence of the Preisach functi...
Abstract: The motion of hydraulic actuators are severly influenced by friction. In this paper the sl...
The present investigation aims at the definition of an efficient and robust neural network-based mod...
A computationally efficient and robust neural network-based model to reproduce the hysteresis phenom...