Add to ORKGAbstract: 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. Copyright © 2005 IFA
Abstract — In this paper, we investigate the control design for a class of strict-feedback nonlinear...
An adaptive control based on a new Multiscale Chebyshev Neural Network (MSCNN) identification is pro...
In this paper, position control of servomotors is addressed. A radial basis function neural network ...
Abstract: This paper is concerned with the adaptive sliding-mode control of nonlinear dynamic system...
This thesis deals with on-line adaptive control of a class of dynamic systems preceded by backlash-l...
The sliding-mode control problem is studied for a class of state-delayed systems with mismatched par...
This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input ...
An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor dri...
This study proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is su...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper considers the problem of achieving time-varying formation for second-order multi-agent sy...
This paper considers the problem of achieving time-varying formation for second-order multi-agent sy...
Abstract — In this paper, a neural networks (NN) based adaptive sliding mode controller (SMC) is int...
Abstract — In this paper, we investigate the control design for a class of strict-feedback nonlinear...
An adaptive control based on a new Multiscale Chebyshev Neural Network (MSCNN) identification is pro...
In this paper, position control of servomotors is addressed. A radial basis function neural network ...
Abstract: This paper is concerned with the adaptive sliding-mode control of nonlinear dynamic system...
This thesis deals with on-line adaptive control of a class of dynamic systems preceded by backlash-l...
The sliding-mode control problem is studied for a class of state-delayed systems with mismatched par...
This paper addresses the problem of adaptive neural sliding mode control for a class of multi-input ...
An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor dri...
This study proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is su...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper introduces explicit neural representations of fundamental hysteresis operators such as th...
This paper considers the problem of achieving time-varying formation for second-order multi-agent sy...
This paper considers the problem of achieving time-varying formation for second-order multi-agent sy...
Abstract — In this paper, a neural networks (NN) based adaptive sliding mode controller (SMC) is int...
Abstract — In this paper, we investigate the control design for a class of strict-feedback nonlinear...
An adaptive control based on a new Multiscale Chebyshev Neural Network (MSCNN) identification is pro...
In this paper, position control of servomotors is addressed. A radial basis function neural network ...