• Corpus ID: 18485531

Non-linear Plant model Inversion technique using Fuzzy Logic Controller

@inproceedings{Babu2015NonlinearPM,
  title={Non-linear Plant model Inversion technique using Fuzzy Logic Controller},
  author={Hari Babu},
  year={2015}
}
This work of fiction proposes a contemporary approach for developing inverse model of a temperature process which will be based on fuzzy control logic. The proposed Fuzzy Nonlinear Internal Model Control (FNIMC) will possiblyestablish exactly the model inverse which guarantees offset free control performances. Purely efficient method was used in developing an inversion approach of the temperature process. The inverse model is conventionally having been performed using proportional-Integral… 

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References

SHOWING 1-10 OF 20 REFERENCES

Fuzzy adaptive internal model control

  • W. XieA. Rad
  • Computer Science, Engineering
    1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228)
  • 1998
TLDR
The application of fuzzy adaptive internal model controller in the laboratory scale Process Control Unit (PCU) from Bytronic shows that this kind of control scheme is appropriate for controlling the time-varying stable plant with time-delay.

Internal model control with a fuzzy model: application to an air-conditioning system

TLDR
This paper presents an identification procedure for a Takagi-Sugeno fuzzy model, which is based on product-space fuzzy clustering, and can be inverted analytically and hence can be easily included in a nonlinear IMC scheme.

Nonlinear internal model control: application of inverse model based fuzzy control

This paper investigates the possible applications of dynamical fuzzy systems to control nonlinear plants with asymptotically stable zero dynamics using a fuzzy nonlinear internal model control

Internal model control using neural networks

This paper deals with the internal model control of non linear dynamic systems based on artificial neural networks. The proposed control scheme is based on the neural network model and the inverse

Nonlinear control structures based on embedded neural system models

TLDR
A novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms.

Neural Networks for Nonlinear Internal Model Control

A novel technique, directly using artificial neural networks, is proposed for the adaptive control of nonlinear systems. The ability of neural networks to model arbitrary nonlinear functions and

Nonlinear internal model control using neural networks: application to processes with delay and design issues

TLDR
It is shown that the design of such nonadaptive indirect control systems necessitates only the training of the inverse of the model deprived from its delay, and that the presence of the delay thus does not increase the order ofThe inverse.

Comparison of Controller Performance for MIMO Process

The main objective of this paper is to simulate and control some different aspects of multivariable control system of the quadruple tank process. The ideas are illustrated on a quadruple tank process

Adaptive control of a class of nonlinear discrete-time systems using neural networks

TLDR
The result says that, for any bounded initial conditions of the plant, if the neural network model contains enough number of nonlinear hidden neurons and if the initial guess of the network weights is sufficiently close to the correct weights, then the tracking error between the plant output and the reference command will converge to a bounded ball, whose size is determined by a dead-zone nonlinearity.