Optimal Robust Tracking Control of Injection Velocity in an Injection Molding Machine

被引:2
|
作者
Wu, Guoshen [1 ]
Ren, Zhigang [1 ,2 ]
Li, Jiajun [1 ,3 ]
Wu, Zongze [4 ]
机构
[1] Guangdong Univ Technol, Sch Automat, Guangdong Key Lab IoT Informat Technol, Guangzhou 510006, Peoples R China
[2] Guangdong Univ Technol, Guangdong HongKong Macao Joint Lab Smart Discrete, Guangzhou 510006, Guangdong, Peoples R China
[3] Minist Educ, Key Lab Intelligent Detect & Internet Things Mfg G, Guangzhou 510006, Peoples R China
[4] Shenzhen Univ, Coll Mechatron & Control Engn, Guangdong Lab Artificial Intelligence & Digital Ec, Shenzhen 518060, Peoples R China
基金
中国国家自然科学基金;
关键词
discrete manufacturing process; injection molding; optimal control; model-based control; feedback control; MODEL-PREDICTIVE CONTROL; BATCH PROCESSES; CAVITY PRESSURE; RAM VELOCITY; DESIGN;
D O I
10.3390/math11122619
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Injection molding is a critical component of modern industrial operations, and achieving fast and stable control of injection molding machines (IMMs) is essential for producing high-quality plastic products. This paper focuses on solving an optimal tracking control problem of the injection velocity that arises in a typical nonlinear IMM. To this end, an efficient optimal robust controller is proposed and designed. The nonlinear injection velocity servo system is first approximately linearized at iteration points using the first-order Taylor expansion approach. Then, at each time node in the optimization process, the relevant algebraic Riccati equation is introduced, and the solution is used to construct an optimal robust feedback controller. Furthermore, a rigorous Lyapunov theorem analysis is employed to demonstrate the global stability properties of the proposed feedback controller. The results from numerical simulations show that the proposed optimal robust control strategy can successfully and rapidly achieve the best tracking of the intended injection velocity trajectory within a given time.
引用
收藏
页数:17
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