Swing up fuzzy controller for inverted pendulum based on a human control strategy

2002 ◽  
Author(s):  
S. Yasunobu ◽  
M. Mori
Keyword(s):  
Control Strategy ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Human Control

scholarly journals A Wheeled Inverted Pendulum Learning Stable and Accurate Control from Demonstrations

2019 ◽  
Vol 9 (24) ◽  
pp. 5279
Author(s):  
Shaokun Jin ◽  
Yongsheng Ou
Keyword(s):  
Control Strategy ◽  
Inverted Pendulum ◽  
Target Position ◽  
Path Following ◽  
Learning Model ◽  
Human Control ◽  
Wheeled Inverted Pendulum ◽  
Learning From Demonstrations ◽  
Trajectory Following ◽  
Robot Position

In order to enable robots to be more intelligent and flexible, one way is to let robots learn human control strategy from demonstrations. It is a useful methodology, in contrast to traditional preprograming methods, in which robots are required to show generalizing capacity in similar scenarios. In this study, we apply learning from demonstrations on a wheeled, inverted pendulum, which realizes the balance controlling and trajectory following simultaneously. The learning model is able to map the robot position and pose to the wheel speeds, such that the robot regulated by the learned model can move in a desired trajectory and finally stop at a target position. Experiments were undertaken to validate the proposed method by testing its capacity of path following and balance guaranteeing.

scholarly journals Design and simulate of LQR-Fuzzy controller for unicycle robot with double flywheels

2018 ◽  
Vol 192 ◽  
pp. 02001 ◽  
Author(s):  
Surachat Chantarachit
Keyword(s):  
Angular Momentum ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Taylor Series Expansion ◽  
Lagrange Equations ◽  
Main Structure ◽  
Structure Control ◽  
Controller Gain ◽  
Lqr Controller ◽  
Simulation Results

This research is focus on design and simulate unicycle robot with double flywheels model with LQR-Fuzzy controller. Roll balancing torque is generated by gyroscopic effect. Pitch balancing torque is applied by inverted pendulum concept. To control the heading of the robot, the angular momentum from both flywheel is applied to control this. The robot model is based on Euler-Lagrange equations. The non-linear model is linearization by Taylor series expansion. The simulation results conducted by MATLAB/Simulink. LQR-Fuzzy is combination algorithm between LQR and Fuzzy controller. The main structure control is the LQR controller and use the Fuzzy controller to adjust the close loop controller gain. The simulation results is simulated and compared with conventional LQR.

Transfer of Human Control Strategy

2005 ◽  
pp. 125-136
Author(s):  
Yangsheng Xu ◽  
Ka Keung C. Lee
Keyword(s):  
Control Strategy ◽  
Human Control

Evaluation of Human Control Strategy

2005 ◽  
pp. 89-113
Author(s):  
Yangsheng Xu ◽  
Ka Keung C. Lee
Keyword(s):  
Control Strategy ◽  
Human Control

scholarly journals Switching Stabilization for Nonlinear Networked Control Systems with Delays and Packet Losses

2020 ◽  
Vol 49 (2) ◽  
pp. 302-316
Author(s):  
Qi Zhang ◽  
Bin Liu
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Networked Control Systems ◽  
Fuzzy Model ◽  
Average Dwell Time ◽  
Switched System ◽  
Packet Dropout ◽  
Packet Losses ◽  
Nonlinear Networked Control Systems ◽  
Rotary Inverted Pendulum

This paper studies the stabilization problem for nonlinear NCSs(NNCSs) with bilateral network-induced random delay and packet dropout. T-S fuzzy model is employed to represent the nonlinear controlled plant. Based on the T-S model, a discrete-time fuzzy switched system model with uncertain parameters is established by means of the uncertain method and switching system method. Furthermore, the exponential stability condition for the state of the fuzzy switched system is obtained by using the combination of slow switching model-dependent average dwell time (MDADT) method and fast switching MDADT method. Finally, a series of rotary inverted pendulum experiments are provided to illustrates the effectiveness of the proposed method and prove that the proposed fuzzy controller based on T-S fuzzy model can balance the rotary inverted pendulum in a greater state range rather than the linear controller based on linearization

Fuzzy Control of an Inverted Pendulum

1996 ◽  
Author(s):  
Tuna Balkan ◽  
Mehmet Emin Ari
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Angular Position ◽  
Upright Position ◽  
Control Signal ◽  
Position Measurement ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
And Performance ◽  
The Stability

Abstract An inverted pendulum system has been designed and constructed as a physical model of inherently unstable mechanical systems. The vertical upright position of a pendulum is controlled by changing the horizontal position of a cart to which the pendulum is hinged. The stability of the system has been investigated when a fuzzy controller is used to produce the control signal, while making a single measurement. It has been shown that by using simple fuzzy rules to allow real time computation with a single angular position measurement, the system can not be made absolutely stable. However, the stability and performance of the system have been considerably improved by shrinking the membership functions of angular position, computed angular velocity and control signal when inverted pendulum is very close to the vertical upright position.

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

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