A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model

2001 ◽  
Vol 5 (1) ◽  
pp. 58-70 ◽  
Author(s):  
Jianqiang Yi ◽  
◽  
Naoyoshi Yubazaki ◽  
Kaoru Hirota ◽  
◽  
...  
Keyword(s):  
Position Control ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Initial Position ◽  
Initial Angle ◽  
Inference Model ◽  
Stabilization Control ◽  
Control Situations ◽  
Single Input ◽  
Simulation Results

A fuzzy controller is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model for stabilization control of inverted pendulums. The angle and angular velocity of the pendulum and the position and velocity of the cart are selected as input items and the driving force as the output item. By using SIRMs and dynamic importance degrees, the fuzzy controller realizes angular control of the pendulum and position control of the cart in parallel with totally only 24 fuzzy rules. Switching between angular control of the pendulum and position control of the cart is smoothly performed by automatically adjusting dynamic importance degrees according to control situations. For any inverted pendulums, of which the pendulum length is among [0.5m, 2.2m], simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize the pendulum systems completely in about 6.0 seconds when the initial angle of the pendulum is among [-30.0°, +30.0°], or the initial position of the cart is among [-2.1m, +2.1m].

SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model

1997 ◽  
Vol 1 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Naoyoshi Yubazaki ◽  
◽  
Jianqiang Yi ◽  
Kaoru Hirota ◽  
Keyword(s):  
Gradient Descent ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Control Performance ◽  
Nonlinear Functions ◽  
Inference Model ◽  
Proposed Model ◽  
Single Input

A new fuzzy inference model, SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model, is proposed for plural input fuzzy control. For each input item, an importance degree is defined and single input fuzzy rule module is constructed. The importance degrees control the roles of the input items in systems. The model output is obtained by the summation of the products of the importance degree and the fuzzy inference result of each SIRM. The proposed model needs both very few rules and parameters, and the rules can be designed much easier. The new model is first applied to typical secondorder lag systems. The simulation results show that the proposed model can largely improve the control performance compared with that of the conventional fuzzy inference model. The tuning algorithm is then given based on the gradient descent method and used to adjust the parameters of the proposed model for identifying 4-input 1-output nonlinear functions. The identification results indicate that the proposed model also has the ability to identify nonlinear systems.

Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method

2018 ◽  
Vol 22 (2) ◽  
pp. 172-175
Author(s):  
Takeshi Nagata ◽  
Hirosato Seki ◽  
Hiroaki Ishii ◽  
◽  
◽  
...  
Keyword(s):  
Medical Diagnosis ◽  
Simplex Method ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Inference Model ◽  
Two Phase ◽  
Inference Models ◽  
Learning Rules ◽  
Proposed Model ◽  
Single Input

Single Input Rule Modules connected fuzzy inference model (SIRMs model, for short) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference models. However, it is difficult to understand the meaning of the weight for the SIRMs model because the value of the weight has no restriction in the learning rules. Therefore, the paper proposes a constrained SIRMs model in which the weights are in [0,1] by using two-phase simplex method. Moreover, it shows that the applicability of the proposed model by applying it to a medical diagnosis.

Integrating Servo-Pneumatic Actuator with Ball Beam System based on Intelligent Position Control

2014 ◽  
Vol 69 (3) ◽  
Author(s):  
Muhammad Asyraf Azman ◽  
Ahmad ‘Athif Mohd Faudzi ◽  
Nu’man Din Mustafa ◽  
Khairuddin Osman ◽  
Elango Natarajan
Keyword(s):  
Fuzzy Logic ◽  
Position Control ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Pneumatic Actuator ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
Beam System ◽  
Position Controller ◽  
Simulation Results

The purpose of this paper is to design a controller that can control the position of the cylinder pneumatic stroke. This work proposes two control approaches, Proportional-Integral-Derivative Fuzzy Logic (Fuzzy-PID) controller and Proportional-Derivative Fuzzy Logic (PD-Fuzzy) controller for a Servo-Pneumatic Actuator. The design steps of each controller implemented on MATLAB/Simulink are presented. A model based on position system identification is used for the controller design. Then, the simulation results are analyzed and compared to illustrate the performance of the proposed controllers. Finally, the controllers are tested with the real plant in real-time experiment to validate the results obtained by simulation. Results show that PD-Fuzzy controller offer better control compared to Fuzzy-PID. A Pneumatic Actuated Ball & Beam System (PABBS) is proposed as the application of the position controller. The mathematical model of the system is developed and tested simulation using Feedback controller (outer loop)-PD-Fuzzy controller (inner loop). Simulation result is presented to see the effectiveness of the obtained model and controller. Results show that the servo-pneumatic actuator can control the position of the Ball & Beam system using PD-Fuzzy controller.

scholarly journals A Unified Anti-Windup Technique for Fuzzy and Sliding Mode Controllers

2015 ◽  
Vol 10 (6) ◽  
pp. 83 ◽  
Author(s):  
Radu Emil Precup ◽  
Marius L. Tomescu ◽  
Emil M. Petriu
Keyword(s):  
State Feedback ◽  
Position Control ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Digital Simulation ◽  
The State ◽  
Sliding Mode Controllers ◽  
Hydraulic Servo ◽  
Back Calculation ◽  
Simulation Results

This paper proposes the unified treatment of an anti-windup technique for fuzzy and sliding mode controllers. A back-calculation and tracking anti-windup scheme is proposed in order to prevent the zero error integrator wind-up in the structures of state feedback fuzzy controllers and sliding mode controllers. The state feedback sliding mode controllers are based on the state feedback-based computation of the switching variable. An example that copes with the position control of an electro-hydraulic servo-system is presented. The conclusions are pointed out on the basis of digital simulation results for the state feedback fuzzy controller.

Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model

2000 ◽  
Vol 4 (4) ◽  
pp. 302-312 ◽  
Author(s):  
Jianqiang Yi ◽  
◽  
Naoyoshi Yubazaki ◽  
Kaoru Hirota ◽  
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Tracking Error ◽  
Control Object ◽  
Tennis Ball ◽  
Table Tennis ◽  
Inference Model ◽  
Trajectory Tracking Control

A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy inference model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy inference model is proved to be effective in tracking control of unconstrained objects.

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