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| Adaptive PI Control Strategy for Shipboard DC Converters in Dynamic Load Environments |
| Dong Xin1, Zhang Changkun1,2, Yu Wanneng1,2, Liao Weiqiang1,2, Chen Yao1,2 |
1. College of Marine Engineering Jimei University Xiamen 361021 China;
2. Fujian Provincial Research Center for Offshore Small Green Intelligent Ship System Engineering Xiamen 361021 China |
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Abstract In shipboard DC power systems, the DC-DC converter, as the core regulating unit between the source and the load, plays a critical role in maintaining bus voltage stability and ensuring robust dynamic response. Under dynamic load conditions, conventional closed-loop control of DC-DC converters often leads to increased voltage fluctuations and a sluggish dynamic response. In some cases, inferior suppression of bus voltage oscillations compared with open-loop control. This paper proposes an adaptive control strategy for shipboard DC-DC converters under dynamic load conditions.
First, a mathematical model of the DC-DC converter is established using hybrid system theory, and the system parameters are identified online using the recursive least squares (RLS) method. Based on the identified parameters, a detailed shipboard DC microgrid simulation model is developed in Matlab/Simulink to analyze the PV curve characteristics under both open-loop and closed-loop control. The analysis confirms that the closed-loop system's dynamic response is strongly load-dependent. By evaluating dynamic response indicators across the full load range, the feasible operating range for the PI controller parameters is determined. Then, a small-signal model of the DC-DC converter and its output impedance transfer function are constructed. A multi-objective optimization function is designed to incorporate both amplitude and phase errors, with weighting factors to improve fitting accuracy. Global optimization of this function is performed using a genetic algorithm to obtain the optimal PI parameter set. Finally, the accuracy of parameter identification and the performance of the proposed adaptive control strategy are validated on a hardware experimental platform.
Experimental results show that the parameter identification method based on hybrid system theory effectively estimates the DC-DC converter's internal parameters, with relative errors between identified and measured values below 4%. Under various load transients, comparison of the DC bus dynamic responses indicates that closed-loop control can achieve lower voltage fluctuations and faster steady-state recovery under certain load conditions. However, for some loads, some dynamic response indicators remain inferior to those under open-loop control. It is indicated that although closed-loop control offers advantages for some performance metrics, it cannot simultaneously optimize all indicators, leading to performance trade-offs. In contrast, the proposed adaptive control strategy achieves further performance improvement under dynamic load conditions. The average voltage fluctuation caused by load changes is reduced by approximately 30%, the time for recovering from oscillation to steady state is shortened by over 18%, and the suppression capability of bus voltage oscillations is improved by roughly 30%. These results confirm that the proposed adaptive control strategy effectively enhances the dynamic response and steady-state recovery capability of shipboard DC power systems under load transients.
The following conclusions can be drawn. (1) The DC-DC converter can be effectively modeled using hybrid system theory, and its internal parameters can be accurately identified using the RLS method, with relative errors below 4%. (2) PV curve analysis reveals that the dynamic response of closed-loop control is strongly load-dependent, and evaluation across the full load range provides a global selection range for the PI controller parameters. (3) Compared with conventional methods, the proposed adaptive control strategy reduces voltage fluctuations caused by sudden load changes by an average of more than 30%, shortens the recovery time from oscillation to steady state by over 18%, and improves the suppression capability of bus voltage oscillations by an average of 30%.
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Received: 10 June 2025
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2026, Vol. 41 
