Title: Virtual reality data visualisation design based on model predictive control in metaverse

Authors: Tiankuo Yu; Lei Ding; Xiaocheng Zhou; Gaofeng Han

Addresses: School of Digital Creativity, Guangdong Nanhua Vocational College of Industry and Commerce, Guangzhou, Guangdong, China ' School of Computer Engineering, Anhui Wenda University of Information Engineering, Hefei, Anhui, China ' School of Computer Engineering, Anhui Wenda University of Information Engineering, Hefei, Anhui, China ' School of Computer Engineering, Anhui Wenda University of Information Engineering, Hefei, Anhui, China

Abstract: In response to the problem of slow data updates caused by a large amount of static data display and neglect of real-time dynamic interaction in visual design, this study developed a framework based on Model Predictive Control (MPC) to address the limitations of static display and promote real-time interaction. In the article, a data acquisition and processing module is constructed, combined with linear regression and Long-Short-Term Memory (LSTM) models, optimised and integrated into a Virtual Reality (VR) system. Multiple interaction methods are designed, and reinforcement learning is introduced to improve prediction performance, data display effectiveness and multi-user synchronisation accuracy. The results showed that the average accuracy of the method reached 93.17%, with response delay, frame rate and update frequency of 6.97 milliseconds, 101 frames per second and 67 hertz, respectively. These results demonstrate the effectiveness of the framework in VR applications.

Keywords: data visualisation design; model predictive control; virtual reality; art design; system architecture design.

DOI: 10.1504/IJCAT.2026.151373

International Journal of Computer Applications in Technology, 2026 Vol.78 No.1, pp.25 - 38

Received: 08 Jan 2025
Accepted: 08 Apr 2025
Published online: 26 Jan 2026 *

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