High-order aspherics: the SMS nonimaging design method applied to imaging optics

2008 ◽  
Author(s):  
F. Muñoz ◽  
P. Benítez ◽  
J. C. Miñano
Keyword(s):  
Design Method ◽  
High Order ◽  
Imaging Optics

scholarly journals Overview of the SMS design method applied to imaging optics

2009 ◽  
Author(s):  
Juan C. Miñano ◽  
Pablo Benítez ◽  
Wang Lin ◽  
Fernando Muñoz ◽  
José Infante ◽  
...  
Keyword(s):  
Design Method ◽  
Imaging Optics

scholarly journals Design Low Order Robust Controller for the Generator’s Rotor Angle Stabilization PSS System

2021 ◽  
Vol 5 (5) ◽  
pp. 598-618
Author(s):  
Vu Ngoc Kien ◽  
Nguyen Hien Trung ◽  
Nguyen Hong Quang
Keyword(s):  
Model Order Reduction ◽  
Design Method ◽  
Order Reduction ◽  
High Order ◽  
Optimal Order ◽  
Robust Controller ◽  
Model Order ◽  
Rotor Angle ◽  
Load Angle ◽  
Low Order

The electrical system's problem stabilizes the electrical system with three primary parameters: rotor angle stability, frequency stability, and voltage stability. This paper focuses on the problem of designing a low-order stable optimal controller for the generator rotor angle (load angle) stabilization system with minor disturbances. These minor disturbances are caused by lack of damping torque, change in load, or change in a generator during operation. Using the RH∞optimal robust design method for the Power System Stabilizer (PSS) to stabilize the generator’s load angle will help the PSS system work sustainably under disturbance. However, this technique's disadvantage is that the controller often has a high order, causing many difficulties in practical application. To overcome this disadvantage, we propose to reduce the order of the higher-order optimal robust controller. There are two solutions to reduce order for high-order optimal robust controller: optimal order reduction according to the given controller structure and order reduction according to model order reduction algorithms. This study selects the order reduction of the controller according to the model order reduction algorithms. In order to choose the most suitable low-order optimal robust controller that can replace the high-order optimal robust controller, we have compared and evaluated the order-reducing controllers according to many model order reduction algorithms. Using robust low-order controllers to control the generator’s rotor angle completely meets the stabilization requirements. The research results of the paper show the correctness of the controller order reduction solution according to the model order reduction algorithms and open the possibility of application in practice. Doi: 10.28991/esj-2021-01299 Full Text: PDF

New analytical design of the Smith predictor controller for high-order systems

2005 ◽  
Vol 219 (4) ◽  
pp. 271-281 ◽  
Author(s):  
Tao Liu ◽  
Zhonghui Hu ◽  
Rupo Yin ◽  
Xiaoming Xu
Keyword(s):  
Controller Design ◽  
Design Method ◽  
High Order ◽  
Smith Predictor ◽  
Proportional Integral Derivative ◽  
Actual System ◽  
Performance Specification ◽  
Squared Error ◽  
On Line ◽  
System Uncertainties

In this paper, a new analytical Smith predictor (SP) controller design method is proposed for industrial and chemical high-order systems. Firstly, by using the integral-squared-error (ISE) performance specification, the ideally optimal SP controller is analytically derived according to the nominal high-order plant model, which inevitably results in the high-order controller. Then the analytical controller reduction formulae based on the mathematical Maclaurin and Padé expansions are proposed to duplicate it in the form of a low-order controller such as the proportional-integral-derivative (PID). Hence, the difficulty of controller implementation in practice is significantly relieved without pitiful system performance degradation in comparison with many existing methods. At the same time, the control system robust stability is analysed. Accordingly, the on-line tuning rule of the single adjustable parameter of the proposed controller is provided to cope with the actual system uncertainties. Finally, several illustrative simulation examples are included to demonstrate the effectiveness of the proposed method.

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