A Disturbance Adaptive Control Scheme Based on Order Identification

2014 ◽  
Vol 962-965 ◽  
pp. 2894-2897
Author(s):  
Mu Dan Zhou ◽  
Xiu Bin Ye

Using a order identification method based on wavelet transform to detect the positions of step disturbances, a adaptive control scheme of disturbance based on the order identification method is designed. The simulation shows that the controller can not only track the set point accurately, but also suppress the step disturbance appeared in different position timely. This control scheme is simple, effective and useful in engineering application.

Author(s):  
Vinodhini M.

The objective of this paper is to develop a Direct Model Reference Adaptive Control (DMRAC) algorithm for a MIMO process by extending the MIT rule adopted for a SISO system. The controller thus developed is implemented on Laboratory interacting coupled tank process through simulation. This can be regarded as the relevant process control in petrol and chemical industries. These industries involve controlling the liquid level and the flow rate in the presence of nonlinearity and disturbance which justifies the use of adaptive techniques such as DMRAC control scheme. For this purpose, mathematical models are obtained for each of the input-output combinations using white box approach and the respective controllers are developed. A detailed analysis on the performance of the chosen process with these controllers is carried out. Simulation studies reveal the effectiveness of proposed controller for multivariable process that exhibits nonlinear behaviour.


Author(s):  
Fatma Ezzahra Rhili ◽  
Asma Atig ◽  
Ridha Ben Abdennour ◽  
Fabrice Druaux ◽  
Dimitri Lefebvre

In this study, an adaptive control based on fuzzy adapting rate for neural emulator of nonlinear systems having unknown dynamics is proposed. The indirect adaptive control scheme is composed by the neural emulator and the neural controller which are connected by an autonomous algorithm inspired from the real-time recurrent learning. In order to ensure stability and faster convergence, a neural controller adapting rate is established in the sense of the continuous Lyapunov stability method. Numerical simulations are included to illustrate the effectiveness of the proposed method. The performance of the proposed control strategy is also demonstrated through an experimental simulation.


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