Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm

1997 ◽  
Vol 12 (1) ◽  
pp. 100-105 ◽  
Author(s):  
Yangping Zhao ◽  
R.M. Edwards ◽  
K.Y. Lee
Keyword(s):  
Genetic Algorithm ◽  
Controller Design ◽  
Feedback Controller ◽  
Steam Generators ◽  
Wide Range

scholarly journals Autonomous Cascade Structure Feedback Controller Design With Genetic Algorithm-based Structure Optimization

2020 ◽  
Vol 53 (2) ◽  
pp. 8419-8425
Author(s):  
Yoshihiro Maeda ◽  
Shu Kunitate ◽  
Eitaro Kuroda ◽  
Makoto Iwasaki
Keyword(s):  
Genetic Algorithm ◽  
Controller Design ◽  
Structure Optimization ◽  
Feedback Controller ◽  
Cascade Structure

scholarly journals Robust state feedback controller design of STATCOM using chaotic optimization algorithm

2010 ◽  
Vol 7 (2) ◽  
pp. 253-268 ◽  
Author(s):  
Amin Safari ◽  
Hossein Shayeghi ◽  
Ali Heidar
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
State Feedback ◽  
Controller Design ◽  
Low Frequency ◽  
Operating Conditions ◽  
Feedback Controller ◽  
Static Synchronous Compensator ◽  
State Feedback Controller ◽  
Wide Range

In this paper, a new design technique for the design of robust state feedback controller for static synchronous compensator (STATCOM) using Chaotic Optimization Algorithm (COA) is presented. The design is formulated as an optimization problem which is solved by the COA. Since chaotic planning enjoys reliability, ergodicity and stochastic feature, the proposed technique presents chaos mapping using Lozi map chaotic sequences which increases its convergence rate. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results reveal that the proposed controller has an excellent capability in damping power system low frequency oscillations and enhances greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions shows that the phase based controller is superior compare to the magnitude based controller.

Application of PID Controller for Servo Motors

2014 ◽  
Vol 926-930 ◽  
pp. 1218-1221
Author(s):  
Jun Shi ◽  
Hua Jie Wu
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Parameter Optimization ◽  
Pid Controller ◽  
Controller Design ◽  
Optimization Method ◽  
Engineering Practice ◽  
Algorithm Optimization ◽  
Quantum Genetic Algorithm ◽  
Wide Range

Parameter optimization of PID controller design, parameter optimization method is proposed based on quantum genetic algorithm for PID controller tuning problem. Quantum Genetic Algorithm (QGA) DC servo motor control system PID parameter optimization control, quantum genetic algorithm to optimize the results of the genetic algorithm, the simulation results show that the QGA to optimize control get PID controller comprehensive performance is better than general genetic algorithm optimization PID controller, and the realization of the algorithm does not depend on the controlled object, so that the control system has better robustness and stability, with a wide range of practical in engineering practice play a good role in the control.

Robust H2 Output Feedback Controller Design for Damping Power System Oscillations

2017 ◽  
Vol 6 (10) ◽  
pp. 8
Author(s):  
Kho Hie Kwee ◽  
Hardiansyah .
Keyword(s):  
Power System ◽  
Output Feedback ◽  
Controller Design ◽  
Sufficient Conditions ◽  
Feedback Controller ◽  
Linear Matrix ◽  
Parameter Uncertainties ◽  
Worst Case ◽  
Output Feedback Controller ◽  
Power System Oscillations

This paper addresses the design problem of robust H2 output feedback controller design for damping power system oscillations. Sufficient conditions for the existence of output feedback controllers with norm-bounded parameter uncertainties are given in terms of linear matrix inequalities (LMIs). Furthermore, a convex optimization problem with LMI constraints is formulated to design the output feedback controller which minimizes an upper bound on the worst-case H2 norm for a range of admissible plant perturbations. The technique is illustrated with applications to the design of stabilizer for a single-machine infinite-bus (SMIB) power system. The LMI based control ensures adequate damping for widely varying system operating.

scholarly journals Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 949
Author(s):  
Keita Hara ◽  
Masaki Inoue
Keyword(s):  
Controller Design ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Data Driven ◽  
Koopman Operator ◽  
Plant System ◽  
Dimensional Approximation ◽  
Model Control ◽  
Data Driven Modeling ◽  
L2 Gain

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

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