Stabilization and Control of Elastic Inverted Pendulum System (EIPS) Using Adaptive Fuzzy Inference Controllers

2017 ◽  
Vol 6 (4) ◽  
pp. 21-32
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Transverse Displacement ◽  
Inference System ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Highly Nonlinear ◽  
Training Error ◽  
And Control

Elastic Inverted Pendulum system (EIP) are very popular objects of theoretical investigation and experimentation in field of control engineering. The system becomes highly nonlinear and complex due to transverse displacement of elastic pole or pendulum. This paper presents a comparison study for control of EIP using fuzzy and hybrid adaptive neuro fuzzy inference system (ANFIS) controllers. Initially a fuzzy controller was designed, which was used for training and tuning of ANFIS controller using gbell shape membership functions (MFs). The performance of complete system was evaluated through output responses of settling time, steady state error and maximum overshoot. The study also highlights effect of varying number of MFs on training error of ANFIS. The results showed better performance of ANFIS controller compared to fuzzy controller.

scholarly journals Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

2018 ◽  
Vol 8 (5) ◽  
pp. 3657 ◽  
Author(s):  
Mohammed A. A. Al-Mekhlafi ◽  
Herman Wahid ◽  
Azian Abd Aziz
Keyword(s):  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Inference System ◽  
Pendulum System ◽  
Control Approach ◽  
Inverted Pendulum System ◽  
Neuro Fuzzy

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.

Inverted Pendulum System Modeling and Fuzzy Neural Networks Control

2012 ◽  
Vol 268-270 ◽  
pp. 1371-1375
Author(s):  
Hao Yu
Keyword(s):  
Neural Network ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Fuzzy Neural Networks ◽  
Model Parameters ◽  
Adaptive Neural Network ◽  
Inference System ◽  
Pendulum System ◽  
Inverted Pendulum System

Inverted pendulum on a cart poses a challenging control problem. It seems to have been one of attractive tools for testing linear and nonlinear control laws. In this paper, we adopt PID and the adaptive neural network based fuzzy inference method to control the inverted pendulum, combined the fuzzy control into the neural control. This method can improve the capability of the fuzzy controller through learning the data of PID controller to train the fuzzy controller. When the model parameters were changed, the adaptive neural network based fuzzy inference system had good adopt ability to anti-interfere. The cart can go to the destine position exactly.

A Comparative Study for Position Regulation and Anti-Swing Control of Highly Non-Linear Double Inverted Pendulum (DIP) System Using Different Soft Com

2017 ◽  
Vol 6 (2) ◽  
pp. 59-81
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Vertical Position ◽  
Inference System ◽  
Simulink Model ◽  
Soft Computing Techniques ◽  
Non Linear ◽  
Position Regulation ◽  
Training Error ◽  
And Control

This paper presents a comparative analysis for stabilization and control of highly non-linear, complex and multi-variable Double Inverted Pendulum on cart. A Matlab-Simulink model of DIP has been built using governing mathematical equations. The objective is to control both the pendulums at vertical position while cart is free to move in horizontal direction. The control of DIP was achieved using three different soft-computing techniques namely Fuzzy logic reasoning, Neural networks (NN's) and Adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS controller is more effective as compared to other two controllers in terms of settling time (sec), maximum overshoot (degree) and steady state error. The regression (R) and mean square error (MSE) values obtained after training of Neural network were adequate and the training error obtained in ANFIS was also optimum. All the three controllers were able to stabilize the DIP system but ANFIS control provides better results as illustrated with the help of graphs and tables.

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.

scholarly journals T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Boutaina Elkinany ◽  
Mohammed Alfidi ◽  
Redouane Chaibi ◽  
Zakaria Chalh
Keyword(s):  
State Feedback ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Pendulum System ◽  
Great Ability ◽  
Control Approach ◽  
Feedback Model ◽  
Inverted Pendulum System ◽  
System Controller ◽  
Fuzzy State

This article provides a representation of the double inverted pendulum system that is shaped and regulated in response to torque application at the top rather than the bottom of the pendulum, given that most researchers have controlled the double inverted pendulum based on the lower part or the base. To achieve this objective, we designed a dynamic Lagrangian conceptualization of the double inverted pendulum and a state feedback representation based on the simple convex polytypic transformation. Finally, we used the fuzzy state feedback approach to linearize the mathematical nonlinear model and to develop a fuzzy controller H ∞ , given its great ability to simplify nonlinear systems in order to reduce the error rate and to increase precision. In our virtual conceptualization of the inverted pendulum, we used MATLAB software to simulate the movement of the system before applying a command on the upper part of the system to check its stability. Concerning the nonlinearities of the system, we have found a state feedback fuzzy control approach. Overall, the simulation results have shown that the fuzzy state feedback model is very efficient and flexible as it can be modified in different positions.

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