基于分散模糊推理的多输入多输出系统预测控制

对于动态过程具有明显迟延和惯性的MIMO系统,常规模糊控制难以建立模糊规则,控制效果不理想.针对MIMO控制对象,提出一种基于分散模糊推理的预测控制(predictive control based on decentralized fuzzy inference, DFIPC)方法.构造一组与被控输出相对应的分散模糊推理模块,每个推理模块利用一组分散的模糊推理单元,分别根据各个输出的期望值与预测值之间的偏差进行分散推理.在时间层面,根据动态响应程度对推理结果进行加权综合,获得等效控制输入;进一步,通过对等效控制输入加权综合产生系统实际控制输入增量,从而有效克服模糊推理系统处理多维输入信息时模糊规则难以建立的困难.最后,通过实验验证所提出控制方法对于迟延和惯性明显的MIMO控制对象的有效性和适应性.

Predictive control of multiple-input multiple-output systems based on decentralized fuzzy inference

For multiple-input multiple-output(MIMO) systems whose dynamic processes have conspicuous time-delay and inertia, the conventional fuzzy control method is hard to establish control rules and the control effect can not be satisfied. In this paper, a predictive control based on the decentralized fuzzy inference(DFIPC) method is proposed for MIMO control objects. A cluster of a decentralized fuzzy inference modules corresponding to the outputs of a control object are established. Each decentralized fuzzy inference module contains a set of decentralized fuzzy inference units, and decentralized fuzzy inference are carried out based on the deviation between the expectation in the future time and the predicted value of each output, respectively. In the time domain, the inference results are weighted and synthesized based on the dynamic response degree, and thus the equivalent control inputs of the system are generated. Further, the control increaments of system inputs are generated by weighted synthesis of equivalent control inputs. The difficulty of establishing the control rules of the fuzzy inference system in processing multi-input information is resolved. Simulation experiments are proceeded on MIMO objects with obvious time-delay and inertia to verify the effectiveness and adaptability of the proposed method.