DYNA JOURNAL ENGINEERING

Key words:

Event-triggered control, Multiple innovative parameter identification, Multiverse optimization PI, Optoelectronic tracking servo system

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

Given the coupling of speed and current and the nonlinearity of electronic devices, the photoelectric tracking servo system for milling machines make it difficult to determine the model parameters, and traditional proportional–integral (PI) control hardly meets the high-performance requirements of milling machine optoelectronic tracking servo systems, especially in the development of system networking. In this study, an event-triggered intelligent PI position control strategy based on a multi-innovation identification model was proposed to improve the low control accuracy and dynamic performance caused by the strong coupling and nonlinearity in the optoelectronic tracking servo system of computerized numerical control (CNC) milling machines. A discredite model of the system was established through a multi-innovation identification model, and the PI control parameter was quickly determined using an improved multiverse optimization (IMVO) algorithm. At the same time, an event-triggering mechanism was introduced, thus reducing the number of controller triggers and saving system resources while ensuring the dynamic performance of the system. Finally, experiment results were compared with typical second-order system engineering design PI (SSED-PI) control, pole placement PI (PP-PI) control, and multiverse optimization (MVO)-PI control. Results demonstrate that the proposed multi-innovation stochastic gradient identification model fully utilizes the historical turning angle information of the optoelectronic tracking servo system and has higher accuracy than traditional stochastic gradient identification (parameter accuracy improved by 6.9 times, quantization error reduced by 6.7 times). The proposed event-triggered IMVO-PI (ET-IMVO-PI) has a triggering frequency of 3.5% compared with time-triggered IMVO-PI, with an overshoot of less than 0.5%, which can meet the needs of most engineering practices (less than 5%). Compared with event-triggered SSED-PI, PP-PI, and IMVO-PI, ET-IMVO-PI has higher dynamic performance and fewer triggering times, which can effectively meet the requirements of high-performance network control. The proposed method serves a crucial theoretical guide and important reference for the upgrading and transformation of the photoelectric tracking servo system of CNC milling machines.

Keywords: Event-triggered control, Multiple innovative parameter identification, Multiverse optimization PI, Optoelectronic tracking servo system