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

Author:

Wu Guoshen1,Ren Zhigang12ORCID,Li Jiajun13,Wu Zongze4

Affiliation:

1. Guangdong Key Laboratory of IoT Information Technology, School of Automation, Guangdong University of Technology, Guangzhou 510006, China

2. Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangdong University of Technology, Guangzhou 510006, China

3. Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing (GDUT), Ministry of Education, Guangzhou 510006, China

4. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China

Abstract

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.

Funder

Key-Area Research and Development Program of Guangdong Province

National Natural Science Foundation of China

Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference37 articles.

1. Zheng, R., Tanner, R.I., and Fan, X.-J. (2011). Injection Molding: Integration of Theory and Modeling Methods, Springer Science & Business Media.

2. Chaoyan, L. (2021). Progress of World Plastics Industry (I), 2020, General Plastics.

3. Modeling and optimization of the injection-molding process: A review;Fernandes;Adv. Polym. Technol.,2018

4. Intelligent methods for the process parameter determination of plastic injection molding;Gao;Front. Mech. Eng.,2018

5. Overview of injection molding technology for processing polymers and their composites;Fu;ES Mater. Manuf.,2020

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