中南大学学报（自然科学版）2025，Vol.56Issue(3)：911-922,12.DOI:10.11817/j.issn.1672-7207.2025.03.009
负载口独立阀控缸库普曼模型预测控制方法
Model predictive control method for independent metering valve-controlled cylinders by using koopman operators
摘要
Abstract
The independent metering valve control system(IMVCS)for hydraulic cylinders offers high control degrees of freedom and significant potential for improving system efficiency,presenting excellent application prospects in fields such as construction machinery.However,the increase of control degrees of freedom and the inherent nonlinearity of the valve metering equations pose challenges for achieving high-efficiency and high-precision control in such systems.A model predictive control(MPC)for hydraulic systems based on deep neural network Koopman operators was proposed.Firstly,through training,a high-precision linear predictive model of the controlled object was obtained and applied to the model predictive control of IMVCS.Secondly,an energy consumption term was introduced into the cost function of the controller.The flow and pressure of both chambers of the actuator were controlled to reduce energy consumption.Finally,the NSGA-II algorithm was used to optimize the controller parameters to achieve high-efficiency and high-precision control.The results show that the proposed method ensures control accuracy and achieves energy efficiency optimization.Compared with conventional PID control,the implemented strategy reduces energy consumption by at least 29%and maintains trajectory tracking errors within 0.7 mm.关键词
负载口独立控制阀控液压缸系统(IMVCS)/模型预测控制(MPC)/Koopman算子/深度神经网络(DNN)Key words
independent metering valve-controlled system(IMVCS)/model predictive control(MPC)/Koopman operator/deep neural network(DNN)分类
机械工程引用本文复制引用
刘恒,陶建峰,孙炜,孙浩,刘成良..负载口独立阀控缸库普曼模型预测控制方法[J].中南大学学报（自然科学版）,2025,56(3):911-922,12.基金项目
国家自然科学基金资助项目(52075320)(Project(52075320)supported by the National Natural Science Foundation of China) （52075320）
