基于多模型广义预测控制器的DRTO双层结构

DRTO Two-Layer Structure Based on Multi-Model Generalized Predictive Controller

针对动态实时优化(DRTO)与模型预测控制器(MPC)结合的双层结构中由于DRTO与MPC的模型不一致影响优化效果的问题,本文将多模型广义预测控制器(GPC)引入到DRTO双层结构中,设计了基于DRTO双层结构的多模型GPC控制器。上层结构为DRTO层主要解决经济目标函数的优化问题,采用动态过程模型实时优化更新输出对象的最优设定值轨迹。下层结构采用多模型GPC控制器替代原先的单模型MPC控制器,主要是抑制过程中的快速干扰追踪DRTO层得到的最优设定值轨迹。多模型GPC控制器采用多个固定模型和自适应模型来并行辨识系统的动态特性,减小由于上下层模型不一致造成的误差的同时可以提高系统暂态性能和模型参数跳变时系统的调节能力,最后通过仿真验证了该方法的可行性和有效性。

The inconsistence of DRTO and MPC models in the two-layer structure of integrating DRTO and MPC will deteriorate the effect of optimization. Hence, this paper proposes a multi-model generalized predictive controller based on the two-layer structure of DRTO. The upper structure is DRTO, which mainly solves the optimization problem of the economic objective function. Dynamic process model is adopted to get a real-time updating optimal trajectory. In the lower one, a multi-model generalized predictive controller is adopted to replace the previous single model controller in order to attenuate the influence of disturbances. Multiple fixed models and adaptive model are used to identify system dynamic characteristics so as to decrease the errors caused by the inconsistence of two levels＇ models. Meanwhile, the transient performance of the system and the ability to regulate the jumping parameter may be improved. Finally, a case study illustrates the feasibility and efficiency of the proposed method.