A neural network based predictive controller design algorithm is introduced for nonlinear control systems. It is shown that the use of nonlinear programming techniques can be avoided by using a set of affine nonlinear predictors to predict the output of the nonlinear process. The new predictive controller, based on this design, is both simple and easy to implement in practice. An on-line weight learning algorithm based on neural networks is introduced and convergence of both the weights and estimation errors is established. Predictive controller design, based on the new procedure, is illustrated using a growing network example
Model predictive control (MPC) is a popular and an advance control technique for linear system with ...
This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC...
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems usi...
Neural network based variable structure control is proposed for the design of nonlinear discrete sys...
Design and implementation are studied for a neural network-based predictive controller meant to gove...
An artificial feedforward neural network is used for on-line control purposes of a class of single i...
Since the last three decades predictive control has shown to be successful in control industry, but ...
Purpose - To develop a new predictive control scheme based on neural networks for linear and non-lin...
The contribution is aimed at predictive control of nonlinear processes with the help of artificial n...
This paper presents the application of predictive control techniques using Artificial Neural Nets (A...
In this paper the authors present two approaches for the control of an inverted pendulum on a cart. ...
In this paper, a neural network model-based predictive control has been developed to solve problems ...
The series Advances in Industrial Control aims to report and encourage technology transfer in contro...
In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonline...
this paper aims at combining powerful nonlinear modeling techniques with existing linear control tec...
Model predictive control (MPC) is a popular and an advance control technique for linear system with ...
This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC...
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems usi...
Neural network based variable structure control is proposed for the design of nonlinear discrete sys...
Design and implementation are studied for a neural network-based predictive controller meant to gove...
An artificial feedforward neural network is used for on-line control purposes of a class of single i...
Since the last three decades predictive control has shown to be successful in control industry, but ...
Purpose - To develop a new predictive control scheme based on neural networks for linear and non-lin...
The contribution is aimed at predictive control of nonlinear processes with the help of artificial n...
This paper presents the application of predictive control techniques using Artificial Neural Nets (A...
In this paper the authors present two approaches for the control of an inverted pendulum on a cart. ...
In this paper, a neural network model-based predictive control has been developed to solve problems ...
The series Advances in Industrial Control aims to report and encourage technology transfer in contro...
In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonline...
this paper aims at combining powerful nonlinear modeling techniques with existing linear control tec...
Model predictive control (MPC) is a popular and an advance control technique for linear system with ...
This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC...
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems usi...