A Study of Knowledge Representation of Fuzzy Control for Inverted Double Pendulum System by Applying Adaptive Control with Range Scaling Method

1996 ◽  
Vol 8 (3) ◽  
pp. 576-585 ◽  
Author(s):  
Romzi MUCHAMMAD ◽  
Tetsuyuki TAKAHAMA ◽  
Tomohiro ODAKA ◽  
Hisakazu OGURA
Keyword(s):  
Adaptive Control ◽  
Knowledge Representation ◽  
Fuzzy Control ◽  
Double Pendulum ◽  
Scaling Method ◽  
Pendulum System

scholarly journals A Fuzzy Control Heuristic Applied to Non-linear Dynamic System Using a Fuzzy Knowledge Representation

2010 ◽  
Vol 5 (5) ◽  
pp. 664 ◽  
Author(s):  
Felisa M. Cordova ◽  
Guillermo Leyton
Keyword(s):  
Knowledge Representation ◽  
Fuzzy Control ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Systems ◽  
System State ◽  
Specific Level ◽  
Base Matrix ◽  
Different Levels ◽  
Fuzzy Knowledge

This paper presents the design of a fuzzy control heuristic that can be applied for modeling nonlinear dynamic systems using a fuzzy knowledge representation. Nonlinear dynamic systems have been modeled traditionally on the basis of connections between the subsystems that compose it. Nevertheless, this model design does not consider some of the following problems: existing dynamics between the subsystems; order and priority of the connection between subsystems; degrees of influence or causality between subsystems; particular state of each subsystem and state of the system on the basis of the combination of the diverse states of the subsystems; positive or negative influences between subsystems. In this context, the main objective of this proposal is to manage the whole system state by managing the state combination of the subsystems involved. In the proposed design the diverse states of subsystems at different levels are represented by a knowledge base matrix of fuzzy intervals (KBMFI). This type of structure is a fuzzy hypercube that provides facilities operations like: insert, delete, and switching. It also allows Boolean operations between different KBMFI and inferences. Each subsystem in a specific level and its connectors are characterized by factors with fuzzy attributes represented by membership functions. Existing measures the degree of influence among the different levels are obtained (negatives, positives). In addition, the system state is determined based on the combination of the statements of the subsystems (stable, oscillatory, attractor, chaos). It allows introducing the dynamic effects in the calculation of each output level. The control and search of knowledge patterns are made by means of a fuzzy control heuristic. Finally, an application to the co-ordination of the activities among different levels of the operation of an underground mine is developed and discussed.

Adaptive Neural Network Fuzzy Control Plane Double Inverted Pendulum

2013 ◽  
Vol 765-767 ◽  
pp. 2004-2007
Author(s):  
Su Ying Zhang ◽  
Ying Wang ◽  
Jie Liu ◽  
Xiao Xue Zhao
Keyword(s):  
Neural Network ◽  
Fuzzy Control ◽  
Control Theory ◽  
Inverted Pendulum ◽  
Learning Ability ◽  
Adaptive Neural Network ◽  
Pendulum System ◽  
Inverted Pendulum System ◽  
Self Learning ◽  
Controlled Object

Double inverted pendulum system is nonlinear and unstable. Fuzzy control uses some expert's experience knowledge and learns approximate reasoning algorithm. For it does not depend on the mathematical model of controlled object, it has been widely used for years. In practical engineering applications, most systems are nonlinear time-varying parameter systems. As the fuzzy control theory lacks of on-line self-learning and adaptive ability, it can not control the controlled object effectively. In order to compensate for these defects, it introduced adaptive, self-organizing, self-learning functions of neural network algorithm. We called it adaptive neural network fuzzy inference system (ANFIS). ANFIS not only takes advantage of the fuzzy control theory of abstract ability, the nonlinear processing ability, but also makes use of the autonomous learning ability of neural network, the arbitrary function approximation ability. The controller was applied to double inverted pendulum system and the simulation results showed that this method can effectively control the double inverted pendulum system.

Advanced modeling and optimal control of a cart-double pendulum system

2019 ◽  
Author(s):  
◽  
Cecil Jr. Shy
Keyword(s):  
Optimal Control ◽  
Lagrange Equation ◽  
Control Technique ◽  
Tuning Parameter ◽  
Microgravity Environment ◽  
Double Pendulum ◽  
Overhead Crane ◽  
Material Transport ◽  
Pendulum System ◽  
Industry Standards

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The Overhead Crane has evolved in scope since its inception in the late 1800's. Its early use as a hoist for material transport is now proceeded by new found applications, such as in the Active Response Gravity Offload System (ARGOS) at the NASA Johnson Space Center. ARGOS is an astronaut training facility designed to simulate reduced gravity environments such as Lunar, Martian, or microgravity. By industry standards, it is essentially a repurposed Overhead Crane; in academia it can be conceptualized as a cart-double pendulum system. Anti-sway control of cart-pendulum systems has been heavily researched; however, these methods are not typically designed for space simulation. The goal of this research is to design a controller that provides both energy and error minimization for the cart-pendulum, so that its payload moves as if it were floating freely in a microgravity environment (according to Newton's 1st law). The Euler-Lagrange equation is used to model the system and an optimal control technique called the [alpha]-shift is used to control the system. Most treatments on optimal linear control do not include the [alpha]-shift, but its addition allows one to stabilize the system faster and provides an extra tuning parameter while maintaining the simplicity of the solution. Numerical experiments show that the [alpha]-shift method significantly improves the cart-pendulum's ability to control its payload; especially for payloads in the cart-double-pendulum case.

Indirect Adaptive Fuzzy Control for a Class of Uncertain Nonlinear Systems with Unknown Control Direction

2013 ◽  
pp. 139-154
Author(s):  
Salim Labiod ◽  
Hamid Boubertakh ◽  
Thierry Marie Guerra
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Fuzzy Control ◽  
Fuzzy Systems ◽  
Control Algorithm ◽  
Adaptive Fuzzy Control ◽  
Control Schemes ◽  
Unknown Control ◽  
Unknown Control Direction ◽  
Control Direction

In this paper, the authors propose two indirect adaptive fuzzy control schemes for a class of uncertain continuous-time single-input single-output (SISO) nonlinear dynamic systems with known and unknown control direction. Within these schemes, fuzzy systems are used to approximate unknown nonlinear functions and the Nussbaum gain technique is used to deal with the unknown control direction. This paper first presents a singularity-free indirect adaptive control algorithm for nonlinear systems with known control direction, and then this control algorithm is generalized for the case of unknown control direction. The proposed adaptive controllers are free from singularity, allow initialization to zero of all adjustable parameters of the used fuzzy systems, and guarantee asymptotic convergence of the tracking error to zero. Simulations performed on a nonlinear system are given to show the feasibility of the proposed adaptive control schemes.

scholarly journals Design of simultaneous input-shaping-based SIRMs fuzzy control for double-pendulum-type overhead cranes

2015 ◽  
Vol 63 (4) ◽  
pp. 887-896 ◽  
Author(s):  
D. Qian ◽  
S. Tong ◽  
B. Yang ◽  
S. Lee
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Input Shaping ◽  
Double Pendulum ◽  
Overhead Cranes ◽  
Control Command ◽  
Single Input ◽  
System Variables ◽  
Simultaneous Input

Abstract Overhead cranes are extensively employed but their performance suffers from the natural sway of payloads. Sometime, the sway exhibits double-pendulum motions. To suppress the motions, this paper investigates the design of simultaneous input-shaping-based fuzzy control for double-pendulum-type overhead cranes. The fuzzy control method is based on the single input-rule modules (SIRMs). Provided the all the system variables are measurable, the SIRMs fuzzy controller is designed at first. To improve the performance of the fuzzy controller, the simultaneous input shaper is adopted to shape the control command generated by the fuzzy controller. Compared with other two control methods, i.e., the SIRMs fuzzy control and the convolved input-shaping-based SIRMs fuzzy control, simulation results illustrate the feasibility, validity and robustness of the presented control method for the anti-swing control problem of double-pendulum-type overhead cranes.

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