采用新的DNA进化算法自动设计Takagi-Sugeno模糊控制器(英文)

Automatic Design of Takagi-Sugeno Fuzzy Controllers by a New DNA-Based Evolutionary Algorithm

提出一种新颖的基于ＤＮＡ的进化算法（ＤＮＡ－ＥＡ）来自动设计一类Ｔａｋａｇｉ Ｓｕｇｅｎｏ（ＴＳ）模糊控制器．ＴＳ模糊控制器采用带有线性规则后项的ＴＳ模糊规则，连续输 入模糊集，Ｚａｄｅｈ模糊逻辑和常用的重心反模糊器．ＴＳ模糊控制器被证明是带有可变增 益的非线性ＰＩ控制器．ＤＮＡ－ＥＡ被用于自动获取ＴＳ模糊规则，并同时优化模糊规则前 项和后项中的设计参数．ＤＮＡ－ＥＡ采用由生物ＤＮＡ结构启发得到的ＤＮＡ编码方法来编 码模糊控制器的设计参数．在ＤＮＡ－ＥＡ中，引入了受微生物进化现象启发的基因转移和细 菌变异操作．另外，也引入了基于 ＤＮＡ遗传操作的框构变异操作． ＤＮＡ编码方法非常适 合于复杂知识的表达，基于基因水平的遗传操作也很容易引入到ＤＮＡ－ＥＡ中．染色体的长 度是可变的，且可插入或删除部分碱基序列。作为示例，给出了采用ＤＮＡ－ＥＡ来自动设计 ＴＳ模糊控制器用于控制一类非线性系统的方法．ＤＮＡ－ＥＡ能自动地构造模糊控制器．计 算机仿真结果表明，ＤＮＡ－ＥＡ是有效的，且优化得到的模糊控制器是满意的．

In this paper, we propose a new approach to DNA-based evolutionary algorithm (DNA-EA) to design automatically a class of Takagi-Sugeno (TS) fuzzy controllers. The fuzzy controllers employ TS fuzzy rules with linear consequent, continuous input fuzzy sets, Zadeh fuzzy logic AND operation, and the widely-used centroid defuzzier. The fuzzy controllers are proved to be nonlinear PI controllers with variable gains. The fuzzy rules are automatically discovered, and the design parameters in the input fuzzy sets and the linear rule consequent are optimized simultaneously by the DNA-EA. The DNA-EA uses the DNA encoding method stemmed from the structure of the biological DNA to encode the design parameters of the fuzzy controllers. The gene transfer operation and bacterial mutation operation inspired by a microbial evolution phenomenon are introduced into the DNA-EA. Moreover, frameshift mutation operations based on the DNA genetic operations are also used in the DNA-EA. Our encoding method is suitable for complex knowledge representation, and is easy for the genetic operations at gene level to be introduced into the DNA-EA. The length of the chromosome is variable and it is easy to insert and delete parts of the chromosome. As a demonstration, we show how to implement the new method to design automatically a TS fuzzy controller in the control of a nonlinear system. The fuzzy controller can be automatically constructed by the DNA-EA. Computer simulation results indicate that the new method is effective and the designed fuzzy controller is satisfactory.