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Jianxin Zhang

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Jul 2026

Integrated-criterion-based PI parameter tuning for a permanent magnet synchronous motor servo system using an improved multi-verse optimizer

To improve the tracking performance and dynamic response of a permanent magnet synchronous motor servo system, this study investigates a performance-oriented parameter-tuning framework for the controllers in the servo loops. Rather than proposing a new control structure, the work focuses on the formulation and solution of the tuning problem under stringent requirements on both transient and steady-state performance. A comprehensive optimization criterion is established by jointly considering overshoot, settling time, and steady-state error from a global performance perspective. Based on this criterion, an improved multi-verse optimizer with nonlinear parameter scheduling and an inertia-weight mechanism is employed to search for controller parameters with a better balance between exploration and exploitation. The proposed tuning framework is evaluated on a MATLAB/Simulink co-simulation platform under tracking and load-disturbance conditions. Simulation results indicate that, compared with the baseline tuning method, the optimized controller parameters can improve dynamic response characteristics and reduce overshoot and steady-state error. Nevertheless, the present validation is limited to co-simulation, and hardware-level experimental verification will be pursued in future work to further assess practical applicability.

Jun Wu, Jianxin Zhang, Zhen Zhang et al. · 0 citations
Open access Jul 2026

Robust Real-Time DOA Estimation for Outdoor Vehicle Acoustic Sources Using Dynamic-Pruning GCC-PHAT and Adaptive Forgetting Factor OPAST-MUSIC

In outdoor road environments, vehicle acoustic source direction-of-arrival (DOA) estimation is challenged by a low signal-to-noise ratio (SNR), dynamic-noise interference, and stringent real-time requirements. Under such conditions, conventional methods often struggle to achieve an effective balance among estimation accuracy, computational efficiency, and robustness against noise. To address this issue, this paper proposes a DOA estimation method that integrates a dynamic-pruning strategy with an adaptive subspace tracking mechanism. The proposed approach reduces computational complexity while enhancing algorithmic stability in complex and time-varying noise environments. Extensive experiments conducted on simulated data, the LOCATA dataset, and real-world outdoor road measurements demonstrate that the proposed method achieves comparable or superior DOA accuracy relative to conventional approaches, while significantly reducing computational cost. Furthermore, it exhibits stronger stability and robustness in real-world static and dynamic vehicle localization scenarios. Our method achieves a more favorable trade-off among multiple performance metrics. The results show that this method has good engineering application potential in complex outdoor environments, and can provide a practical solution for real-world vehicle monitoring.

Xueheng Hu, Jianxin Zhang, Hong Ma et al. · 0 citations