Characteristic Model-Based Discrete-Time Adaptive Tracking Control for Multimotor Driving Systems With Inertia Variations

• Published in: IEEE transactions on transportation electrification p. 1
• Main Authors: Wang, Xiang, Wang, Baofang, Chen, Xinkai, Yu, Jinpeng
• Format: Journal Article
• Language: English
• Published: IEEE 2024
• Subjects: Adaptation models; characteristic modeling; Convergence; Digital control; discrete-time sliding-mode control; Gears; inertia variations; Load modeling; Motors; Multimotor driving system; Transportation
• ISSN: 2332-7782; 2332-7782
• DOI: 10.1109/TTE.2024.3396653

In multimotor driving systems, inertia is one of the key parameters, and its variations can severely affect the control performance. This paper proposes a novel adaptive discrete-time variable-power super-twisting control (ADVSC) to address this issue. To cater to practical digital control systems, a characteristic model is built in discrete-time domain with fewer parameters and lower order, which decreases the complicacy of common dynamics models. The model parameters are acquired through online identification utilizing input and output data, which diminishes uncertainties and unknown dynamics. The ADVSC is then designed based on the characteristic model to meet practical performance requirements. Specifically, a variable-power sliding function is constructed to suit different tracking error intervals and accelerate the convergence process, and the super-twisting algorithm is further utilized to enhance robustness and meanwhile alleviate chattering. One attractive advantage of this strategy lies in that inertia variations can be reflected by time-varying parameters in the characteristic model using online identification, which endows the designed ADVSC with good adaptability. The stability of the closed-loop system is proved via the Lyapunov theory. Experiments are carried out on a multimotor servo control platform with adjustable load, and the obtained results validate the efficacy and practicality of the proposed strategy.