Improve Linear Quadratic Regulator by Particle Swarm Optimization Algorithms for Two Wheeled Self Bala ncing Mobile robot

2017 ◽  
Vol 13 (2) ◽  
pp. 173-179
Author(s):  
A Noor.M.Mjeed ◽  
A Ekhlas.H.Karam
Keyword(s):  
Particle Swarm Optimization ◽  
Mobile Robot ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
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.

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 An adaptive LQR controller based on PSO and maximum predominant frequency approach for semi-active control scheme using MR damper

2018 ◽  
Vol 19 (1) ◽  
pp. 109
Author(s):  
Gaurav Kumar ◽  
Ashok Kumar ◽  
Ravi S. Jakka
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mr Damper ◽  
Control Force ◽  
Linear Quadratic ◽  
Swarm Optimization ◽  
Predominant Period ◽  
Weighting Matrix ◽  
Lqr Problem ◽  
Lqr Controller

In the linear quadratic regulator (LQR) problem, the generation of control force depends on the components of the control weighting matrix R. The value of R is determined while designing the controller and remains the same later. Amid a seismic event, the responses of the structure may change depending the quasi-resonance occurring between the structure and the earthquake signal. In this situation, it is essential to update the value of R for conventional LQR controller to get optimum control force to mitigate the vibrations due to the earthquake. Further, the constant value of the weighting matrix R leads to the wastage of the resources using larger force unnecessarily where the structural responses are smaller. Therefore, in the quest of utilizing the resources wisely and to determine the optimized value of the control weighting matrix R for LQR controller in real time, a maximum predominant period τpmax and particle swarm optimization-based method is presented here. This method comprises of four different algorithms: particle swarm optimization (PSO), maximum predominant period approach τpmax to find the dominant frequency for each window, clipped control algorithm (CO) and LQR controller. The modified Bouc-Wen phenomenological model is taken to recognize the nonlinearities in the MR damper. The assessment of the advised method is done on a three-story structure having a MR damper at ground floor subjected to three different near fault historical earthquake time histories. The outcomes are equated with those of simple conventional LQR. The results establish that the advised methodology is more effective than conventional LQR controllers in reducing inter-story drift, relative displacement, and acceleration response.

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