Fuzzy Control of Inverted Pendulum Based on Differential Evolution
Fuzzy Control of Inverted Pendulum Based on Differential Evolution
Abstract: Fuzzy control is an efficient method for the control of nonlinear, uncertain plants.
Although satisfactory performance can be achieved with the fuzzy control method, its performance can still be improved, if some optimization algorithms are used to tune some of its parameters. In this paper, we testify the performance of the fuzzy logic for the inverted pendulum system and utilize the Differential Evolution algorithm to optimize the parameters of this controller to obtain better performance.
Key words: Fuzzy Control ；Differential Evolution ；Inverted Pendulum ；Optimization 1 Introduction
In stabilization control of inverted pendulum systems, fuzzy control shows great potentials. However, it is difficult to determine the value of some parameters. Differential Evolution is a simple and efficient heuristic for global optimization over continuous spaces. This paper gives a better optimization algorithm for the fuzzy controller and it is verified. Theory developed by L. A. Zadeh, is a practical alternative for a variety of challenging control applications since it provides a simple and convenient method for constructing nonlinear controllers using heuristic information. Fuzzy control provides a user-friendly formalism for representing and implementing our ideas via the use of linguistic rules, so that we can achieve the expected performance easily. Usually, a block diagram of a fuzzy controller is shown in Fig.1 [1].
2 Fuzzy Control and Differential Evolution
2.1 Fuzzy logic systems and control Fuzzy control, based on the Fuzzy Logic
Rule-base
Reference input
Fuzzification
Inference Mechanism
defuzzification
Output
Fig.1 The block diagram of a fuzzy controller
(1) The rule-base contains a fuzzy logic quantification of the expert’s linguistic description of how to achieve the performance. (2) The inference mechanism emulates the expert’s decision making,
Abstract: Fuzzy control is an efficient method for the control of nonlinear, uncertain plants.
Although satisfactory performance can be achieved with the fuzzy control method, its performance can still be improved, if some optimization algorithms are used to tune some of its parameters. In this paper, we testify the performance of the fuzzy logic for the inverted pendulum system and utilize the Differential Evolution algorithm to optimize the parameters of this controller to obtain better performance.
Key words: Fuzzy Control ；Differential Evolution ；Inverted Pendulum ；Optimization 1 Introduction
In stabilization control of inverted pendulum systems, fuzzy control shows great potentials. However, it is difficult to determine the value of some parameters. Differential Evolution is a simple and efficient heuristic for global optimization over continuous spaces. This paper gives a better optimization algorithm for the fuzzy controller and it is verified. Theory developed by L. A. Zadeh, is a practical alternative for a variety of challenging control applications since it provides a simple and convenient method for constructing nonlinear controllers using heuristic information. Fuzzy control provides a user-friendly formalism for representing and implementing our ideas via the use of linguistic rules, so that we can achieve the expected performance easily. Usually, a block diagram of a fuzzy controller is shown in Fig.1 [1].
2 Fuzzy Control and Differential Evolution
2.1 Fuzzy logic systems and control Fuzzy control, based on the Fuzzy Logic
Rule-base
Reference input
Fuzzification
Inference Mechanism
defuzzification
Output
Fig.1 The block diagram of a fuzzy controller
(1) The rule-base contains a fuzzy logic quantification of the expert’s linguistic description of how to achieve the performance. (2) The inference mechanism emulates the expert’s decision making,
References: [1] Kevin M. Passino, Stephen Yurkovich. “Fuzzy Control”, An Imprint of Addison-Wesley Longman, Inc.1997. [2] Rainer Storn, Kenneth Price. “Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization 11: 341–359, 1997. [3] Zhou Yanping, Gu Xingsheng. “Advances in differential evolution”, Control and Instruments in Chemical Industry, 2007, 34(3) : 1—5. [4] Hong Liu, Fengyang Duan and Ying Gao. “Study on Fuzzy Control of Inverted Pendulum System in the Simulink Environment”, Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation August 5 8, 2007, Harbin, China. 5