scholarly journals ROBUST CONTROL DESIGN PROCEDURE BASED ON PARTICLE SWARM OPTIMIZATION AND KHARITONOV'S THEOREM WITH AN APPLICATION FOR PMSMs

2020 ◽  
Vol 25 (2) ◽  
pp. 219-229
Author(s):  
Lucas Cielo Borin ◽  
Caio Ruviaro Dantas Osório ◽  
Gustavo Guilherme Koch ◽  
Thieli Smidt Gabbi ◽  
Ricardo Coração de Leão Fontoura de Oliveira ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Particle Swarm ◽  
Design Procedure ◽  
Control Design ◽  
Swarm Optimization ◽  
Kharitonov’S Theorem ◽  
Robust Control Design ◽  
Kharitonov's Theorem

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

A NEW ROBUST CONTROL DESIGN PROCEDURE BASED ON A PE IDENTIFICATION UNCERTAINTY SET

2002 ◽  
Vol 35 (1) ◽  
pp. 145-150 ◽  
Author(s):  
X. Bombois ◽  
G. Scorletti ◽  
B.D.O. Anderson ◽  
M. Gevers ◽  
P. Van den Hof
Keyword(s):  
Robust Control ◽  
Design Procedure ◽  
Control Design ◽  
Uncertainty Set ◽  
Robust Control Design

Quantitative Feedback Theory control design using particle swarm optimization method

2011 ◽  
Vol 34 (4) ◽  
pp. 463-476 ◽  
Author(s):  
Hazem I Ali ◽  
Samsul Bahari B Mohd Noor ◽  
SM Bashi ◽  
Mohammad Hamiruce Marhaban
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Design Procedure ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Quantitative Feedback Theory ◽  
Swarm Optimization ◽  
Servo Actuator ◽  
Actuator System ◽  
And Performance

In this paper, a particle swarm optimization (PSO) method is proposed to design Quantitative Feedback Theory (QFT) control. This method minimizes a proposed cost function subject to appropriate robust stability and performance QFT constraints. The PSO algorithm is simple and easy to implement, and can be used to automate the loop shaping procedures of the standard QFT. The proposed method is applied to the high uncertainty pneumatic servo actuator system as an example to illustrate the design procedure of the proposed algorithm. The proposed method is compared with the standard QFT control. The results show that the superiority of the proposed method in that it can achieve the same robustness requirements of standard QFT control with simple structure and low order controller.

scholarly journals LQR/Sliding Mode Controller Design Using Particle Swarm Optimization for Crane System

2020 ◽  
Vol 23 (1) ◽  
pp. 45-50
Author(s):  
Hazem Ali ◽  
Azhar Jabbar Abdulridha ◽  
Rawaa Khaleel ◽  
Kareem Kareem A. Hussein
Keyword(s):  
Particle Swarm Optimization ◽  
Controller Design ◽  
Sliding Mode ◽  
External Disturbance ◽  
Particle Swarm ◽  
Design Procedure ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Highly Nonlinear

In this work, the design procedure of a hybrid robust controller for crane system is presented. The proposed hybrid controller combines the linear quadratic regulator (LQR) properties with the sliding mode control (SMC) to obtain an optimal and robust LQR/SMC controller. The crane system which is represented by pendulum and cart is used to verify the effectiveness of the proposed controller. The crane system is considered one of the highly nonlinear and uncertain systems in addition to the under-actuating properties. The parameters of the proposed LQR/SMC are selected using Particle Swarm Optimization (PSO) method. The results show that the proposed LQR/SMC controller can achieve a better performance if only SMC controller is used. The robustness of the proposed controller is examined by considering a  variation in system parameters with applying an external disturbance input. Finally, the superiority of the proposed LQR/SMC controller over the SMC controller is shown in this work.

scholarly journals Robust Control Design for an Uncertain Macroeconomic Dynamical System with Unknown Characteristics and Inequality Control Constraint

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiaorui Xie ◽  
Ye-Hwa Chen
Keyword(s):  
Dynamical System ◽  
Robust Control ◽  
Design Procedure ◽  
Control Design ◽  
Uniform Boundedness ◽  
Salient Feature ◽  
Uncertain System ◽  
Stabilization Policy ◽  
Bounded Controls ◽  
Robust Control Design

The stabilization problem of a macroeconomic dynamical system is considered in this paper. The main features of this system are that the system uncertainties may be unknown functions of state and time but with known bounds. Furthermore, the control inputs are subject to constraints, which is a salient feature in an economic control problem. To ensure that the controls are within the specified boundaries, in our control design procedure, a creative diffeomorphism, which converts bounded controls into unbounded corresponding signals by choosing an appropriate transformation function, is proposed. For the uncertain system, a deterministic robust control is designed to render the practical stability: uniform boundedness and uniform ultimate boundedness. The range of the input bounds is related to the uncertainties and can be designed according to the actual situation. Numerical simulations are performed to verify the effectiveness of the stabilization policy.

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