基于模糊控制器的倒立摆系统英文文献及翻译
Abstract
In this paper, a fuzzy controller for an inverted pendulum system is presented in two stages. These stages are: investigation of
fuzzy control system modeling methods and solution of the “Inverted Pendulum Problem” by using Java programming with Applets
for internet based control education. In the first stage, fuzzy modeling and fuzzy control system investigation, Java programming
language, classes and multithreading were introduced. In the second stage specifically, simulation of the inverted pendulum problem
was developed with Java Applets and the simulation results were given. Also some stability concepts are introduced.
2007 Elsevier Ltd. All rights reserved.
Keywords: Fuzzy control; Java; Stability; Multithreading; e-learning
1. Introduction
As we move into the information area, human knowledge becomes increasingly important. So a theory is necessary
to formulate human knowledge and heuristics in a systematic manner and put them into engineering systems, together
with other information such as mathematical models and sensory measurements. This aspect is a justification for fuzzy
systems in the literature and characterizes the unique feature of fuzzy systems theory. For many practical systems,
important information comes from two sources: one source is human experts who describe their knowledge about the
system in natural languages; the other is mathematical models that are derived according to physical laws and sensory
measurements [2]. Therefore, we are faced with an important task of combining these two types of information into
systems design. To manage this combination, we should answer the question of how to transform human knowledge
and heuristic base into a mathematical model. Essentially, a fuzzy system performs this transformation [1,13–15].
Fuzzy systems are knowledge-based or rule-based systems that contain descriptive IF-THEN rules that are created
from human knowledge and heuristics. Also fuzzy systems are multi-input–single-output mappings from a real-valued
vector to a real-原文请找腾讯752018766辣-文^论,文.网http://www.751com.cn valued scalar, but for large scale nonlinear systems the multi-output mapping can be decomposed into
a collection of single-output mappings as shown in Fig. 1 [5].
* Corresponding author. Tel.: +90 262 3351168; fax: +90 262 3351150.
E-mail addresses: (Y. Becerikli), (B.K. Celik).
Also part time member of Halic University, Department of Computer Engineering, and Electronics and Telecommunication Engineering,
Istanbul, Turkey.
0895-7177/$ - see front matter 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.mcm.2006.12.004
An important contribution of fuzzy systems theory is that it provides a systematic procedure for transforming a
knowledge base into a nonlinear mapping. So we can use this transformation in engineering systems (control) in the
same manner as we use mathematical models and sensory measurements.
Consequently, by means of fuzzy systems, we can perform analysis and design of engineering systems in a
mathematically rigorous manner [3].
Fuzzy systems have been applied to a wide variety of fields ranging from control, signal processing,
communication, medicine, expert systems to business, etc. However, most significant applications have concentrated
on control problems. The fuzzy systems that are shown in Fig. 2 can be used either as closed-loop controllers or open-
loop controllers. As shown in Fig. 3, when the fuzzy system is used as an open-loop controller, the system usually sets
up control parameters and then the system operates according to these parameters. When it is used as a closed-loop
controller as shown in Fig. 4, the fuzzy system takes the outputs of the controlled system and applies the control
action on the controlled system continuously. In this figures, the controlled system can be considered as an application
process [3–5].
The goal of this text is to show how transformation of a knowledge base into a nonlinear mapping is done, and how
analysis and design are performed on control systems. As a nonlinear system, the inverted pendulum system is often
used as a benchmark to achieve the goal of verifying the performance and effectiveness of a control method because of its simple structure. Recently, a lot of research on control of the inverted pendulum system by using fuzzy control
systems containing fuzzy inference have been done.
Margaliot [6] showed a new approach to determining the structure of fuzzy controllers for inverted pendulums by
fuzzy Lyapunov synthesis. Yamakawa [7,6] demonstrated a high-speed fuzzy controller 1632