Identification of Fuzzy Rule on Manual Control of an Unstable System

1995 ◽  
Vol 7 (2) ◽  
pp. 100-107
Author(s):  
Shigehiro Masui ◽  
◽  
Toshiro Terano ◽  
Yoshimasa Sugaya ◽  
◽  
...  
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Human Operator ◽  
State Variables ◽  
Initial Deviation ◽  
Control Rules ◽  
Linguistic Rules ◽  
Skilled Operator ◽  
Human Operators

After some training, human operators can manually control very unstable objects when some proper information is given. But they can hardly explain how they do it, because they operate them intuitively and not logically. In this paper, we study the human behavior during the control of a double inverted pendulum and identify its control rules experimentally. The motion of a double inverted pendulum is simulated by a micro-computer and some of the state variables are indicated on a CRT, observed by a subject, and controlled on a keyboard. In order to find which information is used by a subject, his visual points are examined by an eye-camera. As a result, we see that there are three phases of operation, that is, the decrease of initial deviation, the prevention of over-shoot, and the keeping of stability. Next, the motion of a pendulum is analyzed qualitatively in each phase so as to identify the control rules of a human operator. By this analysis, we see that the intuitive manipulator of the human operator is quite reasonable from the physical viewpoint, and we represent it by some linguistic rules. From these results, we suggest a hierarchical structure of fuzzy rules as a model of a human operator which is verified through experiments on fuzzy control. It is concluded that this fuzzy controller acts as a skilled operator, but its performance is far superior to humans.

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.

Optimal Design for Fuzzy Controller by Ant Colony Algorithm

2010 ◽  
Vol 439-440 ◽  
pp. 1190-1196 ◽  
Author(s):  
Bao Jiang Zhao
Keyword(s):  
Ant Colony Algorithm ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Real Time Control ◽  
Time Control ◽  
Information Updating ◽  
Fuzzy Logical

Fuzzy logical controller is one of the most important applications of fuzzy-rule-based system that models the human decision processing with a collection of fuzzy rules. In this paper, an adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of selection of the paths and the strategy of the trail information updating. The algorithm is used to design a fuzzy logical controller automatically for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due to multivariable inputs, state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. Experimental results show that the designed controller can control actual inverted pendulum successfully.

Linguistic Fuzzy Logic Control of a Double Inverted Pendulum With Destabilizing Fractional Dampers

2016 ◽  
Author(s):  
Arman Dabiri ◽  
Morad Nazari ◽  
Eric A. Butcher
Keyword(s):  
Human Brain ◽  
Inverted Pendulum ◽  
Fuzzy Controller ◽  
Equations Of Motion ◽  
A Priori ◽  
Fuzzy Rules ◽  
Brain Functions ◽  
Logic Control ◽  
Linguistic Rules ◽  
Two Degree Of Freedom

In this paper, a fuzzy controller is designed for a mechanical system with fractional damping without a priori knowledge of the system dynamics. Because of the constitutive equation of the damping, equations of motion of the system consist of fractional order terms. In the process of developing the fuzzy controller, the fuzzy rules are selected based on the human brain functions. The controller is first implemented for the case of a single inverted pendulum with destabilizing fractional dampings mounted on a cart, i.e. a two degree of freedom (DOF) system, where the functions of human brain in balancing a stick on a fingertip are used to train the fuzzy rules. Then, by extending the linguistic rules, the controller is applied to a double inverted pendulum with destabilizing fractional dampings mounted on a cart, i.e. a three DOF system. Since the linguistic rules are based on qualitative motion of the pendulums, the controller is capable bringing the system to rest at the unstable equilibrium point despite the fractional destabilizing damping in the system. Finally, the numerical results of the both examples are discussed.

scholarly journals Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules

2020 ◽  
Vol 10 (17) ◽  
pp. 5836
Author(s):  
Jérôme Mendes ◽  
Ricardo Maia ◽  
Rui Araújo ◽  
Francisco A. A. Souza
Keyword(s):  
Fuzzy Control ◽  
Control Structure ◽  
Fuzzy Controller ◽  
Inverse Function ◽  
Time Varying ◽  
Fuzzy Logic Controllers ◽  
Control Rules ◽  
Predefined Criterion ◽  
Continuously Stirred Tank Reactor ◽  
Human Operators

The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.

scholarly journals Design and Implementation of a Fuzzy Controller for a TRIGA Mark III Reactor

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Tonatiuh Rivero-Gutiérrez ◽  
Jorge S. Benítez-Read ◽  
Armando Segovia-De-los-Ríos ◽  
Luis C. Longoria-Gándara ◽  
Javier C. Palacios-Hernández
Keyword(s):  
Nuclear Reactor ◽  
Fuzzy Controller ◽  
Power Level ◽  
Kinetic Equations ◽  
Fuzzy Rule ◽  
Control Rod ◽  
Control Rules ◽  
Wide Range ◽  
Research Nuclear Reactor ◽  
Series Of Experiments

The design and testing of a fuzzy rule based controller to regulate the power of a TRIGA Mark III research nuclear reactor are presented. The design does not require the current exact parameters of the point kinetic equations of the reactor. Instead, from a qualitative analysis of the actions taken by the operators during the reactor’s operation, a set of control rules is derived. The rules cover the operation of the reactor from low levels of about dozens of watts up to its full power level of one megawatt. The controller is able to increase power from different initial values to a wide range of desired levels, maintaining constant levels for long periods of time. The controller’s output is the external reactivity, which is further converted to a control rod incremental movement. The fuzzy controller is implemented on the reactor’s digital operating console, and the results of a series of experiments are discussed.

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.

scholarly journals Multi-route choice modelling in a metropolitan context: A comparative analysis using Multinomial Logit and Fuzzy Logic based approaches

2020 ◽  
Vol 79 (ET.2020) ◽  
pp. 1-17
Author(s):  
Sowjanya Dhulipala
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Choice Model ◽  
Route Choice ◽  
Fuzzy Rule ◽  
Multinomial Logit ◽  
Choice Modelling ◽  
Choice Behaviour ◽  
Linguistic Rules ◽  
Mnl Model

Route choice plays a vital role in the traffic assignment and network building, as it involves decision making on part of riders. The vagueness in travellers’ perceptions of attributes of the available routes between any two locations adds to the complexities in modelling the route choice behaviour. Conventional Logit models fail to address the uncertainty in travellers’ perceptions of route characteristics (especially qualitative attributes, such as environmental effects), which can be better addressed through the theory of fuzzy sets and linguistic variables. This study thus attempts to model travellers’ route choice behaviour, using a fuzzy logic approach that is based on simple and logical ‘if-then’ linguistic rules. This approach takes into consideration the uncertainty in travellers’ perceptions of route characteristics, resembling humans’ decision-making process. Three attributes – travel time, traffic congestion, and road-side environment are adopted as factors driving people’s choice of routes, and three alternative routes between two typical locations in an Indian metropolitan city, Surat, are considered in the study. The approach to deal with multiple routes is shown by analyzing two-wheeler riders’ (e.g. motorcyclists’ and scooter drivers’) route choice behaviour during the peak-traffic time. Further, a Multinomial Logit (MNL) model is estimated, to enable a comparison of the two modelling approaches. The estimated Fuzzy Rule-Based Route Choice Model outperformed the conventional MNL model, accounting for the uncertain behaviour of travellers.

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

On-Line Fuzzy Logic Temperature Control of a Concentric-Tubes Heat Exchanger Facility

2006 ◽  
Author(s):  
Claudia Ruiz-Mercado ◽  
Arturo Pacheco-Vega ◽  
Kevin Peters
Keyword(s):  
Heat Exchanger ◽  
Temperature Control ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Outlet Temperature ◽  
Single Input Single Output ◽  
Single Output ◽  
On Line ◽  
The Difference ◽  
Two Stages

We develop a fuzzy rule based controller to perform on-line temperature control of a concentric-tubes heat exchanger facility. The rules were derived from dynamical values of the mass flow rates and fluid temperatures in the heat exchanger. The controller was embedded in a closed-loop single-input single-output system to control the outlet temperature of the cold fluid. The controller was constructed in two stages, the difference between them being the amount of information provided to the controller. To validate the fuzzy controller two sets of tests were carried out for maintaining a constant value of the outlet temperature under different perturbations. Results from this analysis demonstrate that the fuzzy-based controller is able to achieve control of the system, and that the information about the system provided to it is important in terms of accuracy and efficiency.

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