A DEEP KOOPMAN-BASED MODEL PREDICTIVE CONTROL METHOD FOR VALVE-CONTROLLED HYDRAULIC CYLINDER SYSTEMS

被引：0

作者：

Liu, Heng ^{[1
]}

Sun, Wei ^{[1
]}

Sun, Hao ^{[1
]}

Tao, Jianfeng ^{[1
]}

Liu, Chengliang ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Shanghai, Peoples R China

来源：

PROCEEDINGS OF BATH/ASME 2022 SYMPOSIUM ON FLUID POWER AND MOTION CONTROL, FPMC2022 | 2022年

基金：

中国国家自然科学基金;

关键词：

Valve-controlled asymmetric hydraulic cylinder (VCHC); model predictive control (MPC); Koopman operator; Deep Neural Network(DNN); SPECTRAL PROPERTIES; DYNAMICAL-SYSTEMS; DECOMPOSITION; OPERATOR;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Hydraulic servo systems are widely applied in construction machinery due to their simple structure and strong bearing capacity. However, considering the nonlinearity and asymmetry in such systems, it is not easy to establish a precise discrete prediction model for the design of the MPC controller, which is a key factor affecting the precision of motion control. To address this issue, this paper proposes a deep Koopman-based model predictive control (MPC) method for valve-controlled asymmetric hydraulic cylinder (VCHC) systems. Significantly, a linear predictor is developed based on the ability of the Koopman operator to lift a nonlinear space to a linear space globally. The simulation results show that the MPC algorithm combined with the Deep Koopman operator has excellent control performance.

引用

页数：9

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