Globally Linearizing Control (GLC) is a control algorithm capable of using nonlinear process model directly. In GLC, mostly, first-principles models derived from dynamic mass, energy and momentum balances are used. When the process is not perfectly known, the unknown parts of the first principles model should be represented by black-box models, e.g. by neural networks. This paper is devoted to the identification and application of such hybrid models for GLC. It is shown that the first principles part of the model determines the dominant structure of the controller, while the black-box elements of the hybrid model are used as state and/or disturbance estimators. For the identification of the neural network elements of the hybrid model a sens...
In this work advanced nonlinear neural networks based control system design algorithms are adopted t...
In this paper, a new methodology for feed forward-feedback control system design is proposed. Initia...
The emergence of Artificial Neural Networks (ANNs) has rekindled interest in nonlinear control theor...
this paper aims at combining powerful nonlinear modeling techniques with existing linear control tec...
The difficulties associated with the control of nonlinear systems are especially profound when it in...
Reactor temperature control is very important as it affects chemical process operations and the prod...
This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linea...
Nonlinear process control is a challenging research topic at present. In recent years, neural networ...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
Black-box modeling techniques based on artificial neural networks are opening new horizons for model...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
In this study, it is assumed that a plant consists of input or output static nonlinearities and a li...
In this work advanced nonlinear neural networks based control system design algorithms are adopted t...
In this paper, a new methodology for feed forward-feedback control system design is proposed. Initia...
The emergence of Artificial Neural Networks (ANNs) has rekindled interest in nonlinear control theor...
this paper aims at combining powerful nonlinear modeling techniques with existing linear control tec...
The difficulties associated with the control of nonlinear systems are especially profound when it in...
Reactor temperature control is very important as it affects chemical process operations and the prod...
This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linea...
Nonlinear process control is a challenging research topic at present. In recent years, neural networ...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
Black-box modeling techniques based on artificial neural networks are opening new horizons for model...
Black-box modeling techniques based on artificial neural networks are opening new horizons for the m...
In this study, it is assumed that a plant consists of input or output static nonlinearities and a li...
In this work advanced nonlinear neural networks based control system design algorithms are adopted t...
In this paper, a new methodology for feed forward-feedback control system design is proposed. Initia...
The emergence of Artificial Neural Networks (ANNs) has rekindled interest in nonlinear control theor...