Modelling and Simulation of Spherical Inverted Pendulum Based on LQR Control with SimMechanics

2013 ◽  
Vol 391 ◽  
pp. 163-167 ◽  
Author(s):  
M. Fajar ◽  
S.S. Douglas ◽  
J.B. Gomm
Keyword(s):  
Time Domain ◽  
Control Strategy ◽  
Inverted Pendulum ◽  
Multibody System ◽  
Equations Of Motion ◽  
Pendulum System ◽  
Lqr Control ◽  
Inverted Pendulum System ◽  
Lqr Controller ◽  
Feedback Method

This paper describes how to simulate the spherical inverted pendulum, a dynamics of multibody system, with SimMechanics. The control strategy used is based on the LQR feedback method for the stabilisation of the spherical inverted pendulum system. Simulation study has been done in Simulink environment shows that LQR controller is capable to control multi input and multi output of spherical inverted pendulum system successfully. The result shows that LQR control strategy gives satisfactory response that is presented in time domain with the details analysis. The use of SimMechanics for simulation of spherical inverted pendulum has some advantages i.e. not need to derive equations of motion, available visualisation tools, fast and easy design

scholarly journals CONTROL OF THE TWO DoF INVERTED PENDULUM

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
M. Fajar
Keyword(s):  
Control Strategy ◽  
Inverted Pendulum ◽  
Multibody System ◽  
Pole Placement ◽  
Pendulum System ◽  
System A ◽  
Inverted Pendulum System ◽  
Feedback Method ◽  
And Control ◽  
Pole Placement Control

This paper describes how to simulate and control the two DoF inverted pendulum system, a dynamics of multibody system. The control strategy used is based on the conventional feedback method for the stabilisation of the two DoF inverted pendulum system. Simulation study has been done shows that conventional method i.e. pole placement control strategy is capable to control multi input and multi output of the two DoF inverted pendulum system successfully. The result shows that pole placement control strategy gives satisfactory response that is presented in time domain.

Robust Stabilizing Controller Design for Inverted Pendulum System

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Hazem I. Ali
Keyword(s):  
Steady State ◽  
Time Domain ◽  
State Feedback ◽  
Inverted Pendulum ◽  
Controller Design ◽  
H Infinity ◽  
Pendulum System ◽  
Stabilizing Controller ◽  
Inverted Pendulum System ◽  
System Uncertainties

In this paper the design of robust stabilizing state feedback controller for inverted pendulum system is presented. The Ant Colony Optimization (ACO) method is used to tune the state feedback gains subject to different proposed cost functions comprise of H-infinity constraints and time domain specifications. The steady state and dynamic characteristics of the proposed controller are investigated by simulations and experiments. The results show the effectiveness of the proposed controller which offers a satisfactory robustness and a desirable time response specifications. Finally, the robustness of the controller is tested in the presence of system uncertainties and disturbance.

scholarly journals Multibody Modeling and Balance Control of a Reaction Wheel Inverted Pendulum Using LQR Controller

2021 ◽  
Vol 1 (1) ◽  
pp. 84-89
Author(s):  
Ümit Önen ◽  
Abdullah Çakan
Keyword(s):  
State Space ◽  
Inverted Pendulum ◽  
Balance Control ◽  
Space Representation ◽  
Reaction Wheel ◽  
Linear Quadratic ◽  
Pendulum System ◽  
State Space Representation ◽  
Inverted Pendulum System ◽  
Lqr Controller

In this study, modeling and LQR control of a reaction wheel inverted pendulum system is described. The reaction wheel inverted pendulum model is created by using a 3D CAD platform and exported to Simscape Multibody. The multibody model is linearized to derive a state-space representation. A LQR (Linear-quadratic regulator) controller is designed and applied for balance control of the pendulum. The results show that deriving a state-space representation from multibody is an easy and effective way to model dynamic systems and balance control of the reaction wheel inverted pendulum is successfully achieved by LQR controller. Results are given in the form of graphics.

scholarly journals USING GENETIC ALGORITHM IN OPTIMIZING THE MATRIX OF LQR CONTROLLER FOR NONLINEAR INVERTED PENDULUM SYSTEM

2019 ◽  
Vol 1 (28) ◽  
pp. 50-55
Author(s):  
Tan Thanh Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Inverted Pendulum ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Pendulum Model ◽  
The Matrix ◽  
Lqr Controller ◽  
Genetic Algorithm Method

In this article, the author used the matlab software to simulate and then compared the results between the classical LQR (Linear Quadratic Regulator) controller and another method to adjust the matrix parameters toward optimization of the LQR controller. It is the GA (Genetic Algorithm) method to optimize the matrix of the LQR controller, and the results have  been verified on the nonlinear pendulum model. The Genetic Algorithm is a modern control algorithm, which is widely applied in research and practice. The main objective of this article is to use the GA algorithm in order to optimize the matrix parameters of LQR controller, whichcontrolled the position and angle of the nonlinear inverted pendulum at the stable balance point. The matlab-based simulating results showed that  the system has operated properly to the requirements and the output response has reached an equilibrium position of about 2.5 seconds.

PID and LQR Control for Two-Wheeled Vehicles with Hand Sensors

2014 ◽  
Vol 665 ◽  
pp. 619-622
Author(s):  
Dian Rong Li ◽  
Yih Guang Leu ◽  
Yan Hou Wen
Keyword(s):  
Inverted Pendulum ◽  
Pendulum System ◽  
Lqr Control ◽  
Inverted Pendulum System ◽  
Wheeled Vehicle ◽  
Control Signals ◽  
Left And Right ◽  
The Difference ◽  
Unstable Plant ◽  
Wheeled Vehicles

This paper studies the control of a two-wheeled vehicle which is similar to an inverted pendulum system and has hand sensors to make right and left turns. Because the two-wheeled vehicle is unstable and its load is uncertain, PID and LQR controllers are used to stabilize the uncertain and unstable plant. Moreover, the two controllers can make it move forwards and backwards, and turn left and right. The two controllers are implemented into a microcontroller, and the microcontroller outputs appropriate control signals to drive the two-wheeled vehicle according to the three-axis accelerometer and gyroscope sensors. Finally, we verify the controllers' efficiency and compare the difference between above two controllers.

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