Intelligent Robust Control of Dynamic Systems with Partial Unstable Generalized Coordinates Based on Quantum Fuzzy Inference

2011 ◽  
pp. 24-36
Author(s):  
Andrey Mishin ◽  
Sergey Ulyanov
Keyword(s):  
Robust Control ◽  
Dynamic Systems ◽  
Fuzzy Inference ◽  
Generalized Coordinates

Robust control for nonlinear dynamic systems

2003 ◽  
Vol 18 (1-3) ◽  
pp. 93-101
Author(s):  
Kyumann Im ◽  
Cheoljoo Ham ◽  
Woonchul Ham
Keyword(s):  
Robust Control ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Systems

Artificial neural networks and adaptive neuro-fuzzy inference systems for parameter identification of dynamic systems

2020 ◽  
Vol 39 (5) ◽  
pp. 6145-6155
Author(s):  
Ramin Vatankhah ◽  
Mohammad Ghanatian
Keyword(s):  
Differential Equations ◽  
Dynamic Systems ◽  
Fuzzy Inference ◽  
Dynamic Equations ◽  
Control Analysis ◽  
Unknown Parameters ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Inference Systems

There would always be some unknown geometric, inertial or any other kinds of parameters in governing differential equations of dynamic systems. These parameters are needed to be numerically specified in order to make these dynamic equations usable for dynamic and control analysis. In this study, two powerful techniques in the field of Artificial Intelligence (AI), namely Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to explain how unknown parameters in differential equations of dynamic systems can be identified. The data required for training and testing the ANN and the ANFIS are obtained by solving the direct problem i.e. solving the dynamic equations with different known parameters and input stimulations. The governing ordinary differential equations of the system is numerically solved and the output values in different time steps are obtained. The output values of the system and their derivatives, the time and the inputs are given to the ANN and the ANFIS as their inputs and the unknown parameters in the dynamic equations are estimated as the outputs. Finally, the performances of the ANN and the ANFIS for identifying parameters of the system are compared based on the test data Percent Root Mean Square Error (% RMSE) values.

scholarly journals Concise Robust Control of Marine Engine Speed Based on Backstepping and Its Fuzzy Comprehension

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Wang ◽  
Sisi Wang
Keyword(s):  
Robust Control ◽  
Engine Speed ◽  
Closed Loop ◽  
Fuzzy Inference ◽  
Design Method ◽  
Research Literature ◽  
Parameter Determination ◽  
Engineering Practice ◽  
Ocean Engineering ◽  
Marine Engine

In this paper, a concise robust control law based on Backstepping for marine engine speed regulation is presented with the uniform asymptotic stability of the closed-loop system proved by Lyapunov synthesis, and the control parameters have obvious physical meaning. Furthermore, parameter determination method is given by virtue of closed-loop gain shaping algorithm. To overcome the perturbation due to load or interference change, variable universe fuzzy inference is introduced to optimize the control system on-line. Compared with the existing research literature, the design method and performance of the controller are more in line with the ocean engineering practice. The results of the simulations of the proposed controller are presented and compared.

Robust Control of Generalized Dynamic Systems Without the Matching Conditions

1991 ◽  
Vol 113 (4) ◽  
pp. 582-589 ◽  
Author(s):  
Zhihua Qu ◽  
John Dorsey
Keyword(s):  
Robust Control ◽  
Dynamic Systems ◽  
Control Law ◽  
General Control ◽  
Matching Conditions ◽  
Uncertain Dynamic Systems ◽  
Special Cases ◽  
The Stability ◽  
Bounded Uncertainties

A general control law and a set of conditions are proposed to guarantee the stability of dynamic systems with bounded uncertainties. The results do not require the matching conditions on the uncertainties and subsume several existing results as special cases. Moreover, it is shown that there is at least one class of uncertain dynamic systems which can always be stabilized under the proposed control law no matter what the size and the structure of the input-unrelated uncertainties.

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