Optimized Smith Predictor Combined with HCOPSO Algorithm for Unman Surface Vehicle Heading Control

LI Zhiqi; LIU Lanjun; CHEN Jialin

doi:10.11993/j.issn.2096-3920.2025-0104

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > Accepted Manuscript

LI Zhiqi, LIU Lanjun, CHEN Jialin. Optimized Smith Predictor Combined with HCOPSO Algorithm for Unman Surface Vehicle Heading Control[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0104

Citation:

LI Zhiqi, LIU Lanjun, CHEN Jialin. Optimized Smith Predictor Combined with HCOPSO Algorithm for Unman Surface Vehicle Heading Control[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0104

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Optimized Smith Predictor Combined with HCOPSO Algorithm for Unman Surface Vehicle Heading Control

doi: 10.11993/j.issn.2096-3920.2025-0104

LI Zhiqi^1
,,
LIU Lanjun^{1, 2
,
,},
CHEN Jialin^{1, 2
,}

1.
College of Engineering, Ocean University of China, Qingdao 266100, China
2.
Shandong Provincial Engineering Research Center for Marine Intelligent Equipment Technology, Qingdao 266100, China

Received Date: 2025-08-12
Accepted Date: 2025-10-21
Rev Recd Date: 2025-10-13

Available Online: 2025-11-05

Abstract

Abstract

In the heading control of high-speed unmanned surface vessel(USV), the presence of time delay elements in both the forward channel and feedback loop significantly degrades the system's overall performance. Moreover, a larger delay to dynamic time ratio further exacerbates the control difficulty. Conventional Smith predictors can only effectively compensate for time delays in the forward channel and are ineffective against time delays in the feedback loop. In this paper, the time delay in the feedback loop is incorporated into the design of the Smith predictor, constructing a predictive model that accounts for time delays in both directions. This approach allows for simultaneous compensation of time delays in both the forward and feedback paths, thereby significantly reducing the erosion of the system's phase margin caused by bidirectional time delays. Furthermore, a hybrid mean center opposition based learning particle swarm optimization (HCOPSO) algorithm is introduced for the parameter tuning of the PID controller. This algorithm employs a mean center opposition - based learning strategy in the early stages of iteration to expand the search range and utilizes an adaptive compression factor in the later stages for fine-tuning. Thus, it combines the advantages of both global exploration and local exploitation. Simulation results based on a USV heading model demonstrate that the improved Smith predictor PID controller shows significant improvements in system overshoot and settling time compared to conventional PID controllers and traditional Smith predictor PID controllers, with a steady-state error of less than 0.1°. When the compensation model of the optimized Smith predictor contains parameter deviations, the system can still maintain good dynamic stability and steady-state accuracy. Additionally, when comparing the HCOPSO algorithm with other algorithms such as PSO, GA, and WOA for parameter optimization of the improved Smith predictor PID controller, the HCOPSO algorithm achieves an ITAE index that is respectively 55.38%, 22.47%, and 24.63% lower than those obtained by PSO, GA, and WOA, and it exhibits stronger disturbance suppression capability and faster heading recovery performance under different disturbance scenarios, which further verifies the effectiveness of the proposed method.
- unmanned surface vessel,
- heading control,
- optimized Smith predictor,
- PID Control,
- time-delay system,
- HCOPSO algorithm

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References(27)

References

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