基于Sugeno型模糊神经网络的空间杆系结构的压电驱动器主动控制

基于自主研发的压电主动杆件的振动控制特性，采用主动杆件两端节点的相对位移和相对速度作为输入以及控制电流作为输出，设计了空间杆系结构的Sugeno型模糊神经网络控制系统。首先通过LQR方法对结构进行控制产生训练数据样本，再利用神经网络的自适应学习功能进行模糊划分及产生模糊规则，最后利用模糊系统的推理能力对空间杆系结构模型进行基于地震响应的主动控制仿真，同时与基于经验的Mamdani型模糊推理规则进行仿真对比。仿真结果表明两种模糊推理模型对结构模型的控制都能达到良好效果，但是由于Sugeno型模糊推理的计算简单，其仿真速度比Mamdani型模糊推理快几十倍，而且省去了人为的经验总结过程，因而采用Sugeno型模糊神经网络控制器更能满足工程应用的要求。

SUGENO-TYPE FUZZY NEURAL NETWORK ACTIVE CONTROL OF SPACE FRAME STRUCTURE BASED ON PIEZOELECTRIC ACTUATOR

Based on the vibration control characteristics of a piezoelectric active-member invented independently, a Sugeno-type fuzzy neural network control system of a space frame structure is designed, in which the inputs are the relative displacement and relative speed of the two nodes at the end of the active-member and the output is the control current. First, the LQR method is used to obtain the training data samples by controlling the structure, then the adaptive learning function of neural network is used to do fuzzy partition and generate fuzzy rules, and at last a space frame structure model is actively controlled by using fuzzy reasoning capability under the action of seismic response, where the result is compared with the result produced by the simulation of Mamdani fuzzy inference rules based on experiences. The results show that both two fuzzy reasoning models achieve good control effects, but the simulation speed of the Sugeno fuzzy inference is dozens of times faster than the Mamdani fuzzy inference because of its simple calculation that disregards the human experiences, thus it can meet the application requirements better by using the Sugeno-type fuzzy neural network controller.