Optimal Controller Design For A Railway Vehicle Suspension System Using Particle Swarm Optimization

2012 ◽  
Author(s):  
Arfah Syahida Mohd Nor ◽  
Hazlina Selamat ◽  
Ahmad Jais Alimin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Linear Quadratic Regulator ◽  
Active Suspension ◽  
Suspension System ◽  
Railway Vehicle ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Suspension Control ◽  
Trial And Error Method

This paper presents the design of an active suspension control of a two–axle railway vehicle using an optimized linear quadratic regulator. The control objective is to minimize the lateral displacement and yaw angle of the wheelsets when the vehicle travels on straight and curved tracks with lateral irregularities. In choosing the optimum weighting matrices for the LQR, the Particle Swarm Optimization (PSO) method has been applied and the results of the controller performance with weighting matrices chosen using this method is compared with the commonly used, trial and error method. The performance of the passive and active suspension has also been compared. The results show that the active suspension system performs better than the passive suspension system. For the active suspension, the LQR employing the PSO method in choosing the weighting matrices provides a better control performance and a more systematic approach compared to the trial and error method. Key words: active suspension control, two–axle railway vehicle, linear quadratic regulator, particle swarm optimization

scholarly journals Improve Linear Quadratic Regulator by Particle Swarm Optimization Algorithms for Two Wheeled Self Balancing Mobile robot

2017 ◽  
Vol 13 (2) ◽  
pp. 173-179
Author(s):  
Ekhlas Karam ◽  
Noor Mjeed
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Method ◽  
Control Method ◽  
Particle Swarm ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Balancing Robot ◽  
The Stability ◽  
Multivariable Feedback

The aim of this paper is to suggest a methodical smooth control method for improving the stability of two wheeled self-balancing robot under effect disturbance. To promote the stability of the robot, the design of linear quadratic regulator using particle swarm optimization (PSO) method and adaptive particle swarm optimization (APSO). The computation of optimal multivariable feedback control is traditionally by LQR approach by Riccati equation. Regrettably, the method as yet has a trial and error approach when selecting parameters, particularly tuning the Q and R elements of the weight matrices. Therefore, an intelligent numerical method to solve this problem is suggested by depending PSO and APSO algorithm. To appraise the effectiveness of the suggested method, The Simulation result displays that the numerical method makes the system stable and minimizes processing time.

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 Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Abroon Jamal Qazi ◽  
Clarence W. de Silva ◽  
Afzal Khan ◽  
Muhammad Tahir Khan
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Fuzzy Logic Controller ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Active Suspension ◽  
Suspension System ◽  
Swarm Optimization ◽  
Quarter Model ◽  
Semiactive Suspension

This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control.

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