# matlab模糊控制系统外文文献及翻译

matlab模糊控制系统外文文献及翻译
Fuzzy control of inverted pendulum and concept of stability using
Java application
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.
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. 原文请找腾讯752018766;六/维-论~文'网
http://www.lwfree.cn systems are multi-input–single-output mappings from a real-valued vector to a real-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.
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.
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.
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  showed a new approach to determining the structure of fuzzy controllers for inverted pendulums by fuzzy Lyapunov synthesis. Yamakawa demonstrated a high-speed fuzzy controller hardware system and used only seven fuzzy rules to control the angle of the inverted pendulum system in 1989. Although stabilization control of an inverted pendulum system should also include the position control of the cart besides the angular control of the pendulum because of the limited length of the rail, the above stated approaches only took into consideration the angular control of the pendulum. Yubazaki  built a new fuzzy controller for inverted pendulum systems. The fuzzy controller has four input items, each with a dynamic importance degree.
In this paper, a fuzzy controller for the inverted pendulum system that needs two input items, of which one is the angle between the pendulum and the vertical position, and the other is the derivation of the angle (angular velocity) of the pendulum, is presented simply for educational purposes. The fuzzy controller takes the angle and angular velocity of pendulum from the inverted pendulum system, aggregates inputs with defined IF-THEN rules and derives the obtained force as an output item by means of inference methods.
However, recently, obtaining information resources quickly has become increasingly important. So, by using internet technologies (Java Applets), designing a simulation program for a fuzzy controller of an inverted pendulum.
system is unavoidable. The reason for choosing Java Applet technology arises from supporting all features necessary for extending the Web in ways previously impossible and Java is based on object-oriented technology that has evolved in diverse segments of computer science as a means of managing the complexity inherent in many different kinds of system .1630

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