On the Milito-Cruz adaptive control scheme for Markov chains

1993 ◽  
Vol 77 (2) ◽  
pp. 387-397 ◽  
Author(s):  
V. S. Borkar
Keyword(s):  
Adaptive Control ◽  
Markov Chains ◽  
Control Scheme ◽  
Adaptive Control Scheme

Adaptive Control Scheme for Coupled Tank Process

2018 ◽  
pp. 89-95
Author(s):  
Vinodhini M.
Keyword(s):  
Adaptive Control ◽  
Nonlinear Behaviour ◽  
Simulation Studies ◽  
Input Output ◽  
Control Scheme ◽  
Direct Model ◽  
Multivariable Process ◽  
Model Reference Adaptive ◽  
Siso System ◽  
Adaptive Control Scheme

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.

Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

2021 ◽  
pp. 095965182098818
Author(s):  
Fatma Ezzahra Rhili ◽  
Asma Atig ◽  
Ridha Ben Abdennour ◽  
Fabrice Druaux ◽  
Dimitri Lefebvre
Keyword(s):  
Adaptive Control ◽  
Control Strategy ◽  
Thermal Process ◽  
Experimental Validation ◽  
Experimental Simulation ◽  
Neural Controller ◽  
Control Scheme ◽  
Stability Method ◽  
Indirect Adaptive Control ◽  
Adaptive Control Scheme

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.

Sign in / Sign up

Export Citation Format

Share Document