Genetic-Algorithm-Optimization-Based Infinite Horizon Linear Quadratic Control for Injection Molding Batch Processes with Uncertainty

Abstract

A new genetic-algorithm (GA)-based infinite horizon linear quadratic (IHLQ) control method is developed for injection molding batch processes under partial actuator faults and unknown disturbances. To obtain improved system performance, an extended state space model, which combines the process state and output error information, has been adopted, where extra tuning freedom is gained for the corresponding control system design by adjusting the extended weighting matrix. However, no specified rules are established for the choices of these weighting coefficients. To cope with such situations, GA is employed to optimize these weighting factors in this paper. Furthermore, the robust stability of the control system, which can be regarded as the process under uncertainty, is addressed. Finally, the validity of the proposed approach is tested on the injection velocity regulation process.

Intelligent Injection Molding on Sensing, Optimization, and Control

“…erefore, the development of injection molding process control has gradually transferred from the traditional continuous-control strategy to control strategies that focus on batch-direction characteristics. e research group of Gao F has made great advances in this field [144,[162][163][164][165][166][167][168][169][170][171][172][173][174][175]. is section will introduce the application of two typical strategies in injection molding-traditional feedback control and iterative learning control-and summarize and analyze them.…”

“…is controller has been successfully applied in the injection molding process to control the screw speed and hydraulic back pressure [178,179], coolant flow, coolant temperature [180], and cavity pressure [181,182]. e group of Zhang et al [166,167,170,173,183] designed fault-tolerant control through a predictive functional control framework for a batch process with unknown disturbances and partial actuator failures to achieve the desired closed-loop response.…”

Particle Swarm Optimization-Based H∞ Tracking Fault Tolerant Control for Batch Processes

“…It is extremely necessary to find an intelligent optimization algorithm that may avoid manual adjustment. As a classical intelligent optimization method, the genetic algorithm (GA) has been applied to the selection of batch process control parameters [26][27][28]. The particle swarm optimization algorithm (PSOA) is also an intelligent optimization method.…”