Abstract
Shipboard boarding systems operating under complex sea conditions are subject to vessel motion coupling, wave induced disturbances, strong nonlinearity, and engineering constraints, which make accurate end pose tracking difficult. Existing mechanism-based approaches often suffer from modeling inaccuracies and high online computational burden, whereas purely data driven methods usually provide limited interpretability for safety critical marine applications. To address these limitations, this paper proposes a data driven predictive control method for shipboard boarding systems based on a Fuzzy Neural Broad Learning System. An interpretable Linear Regression Decision Tree is first constructed to represent the plant through state space partition and local linear approximation. On this basis, a Fuzzy Neural Broad Learning predictor is developed to capture disturbance-induced uncertainty and parameter variation with fast analytical training and incremental updating capability. The predictor is then embedded into a constrained model-predictive control framework in which actuator saturation, input rate limits, and output safety constraints are handled explicitly, and closed-loop boundedness is analyzed theoretically. Simulation results on a MATLAB R2024a-based and Simulink-based coupled platform show that, for the translational outputs of the gangway end effector, the testing root mean square error ranges from 1.33 × 10−3 to 1.74 × 10−3, with corresponding coefficients of determination ranging from 0.820 to 0.912. In comparative closed-loop simulations against proportional integral derivative control, fuzzy control, and learning-based control under identical operating conditions, the proposed method achieves the lowest integral of squared error and integral of absolute error, reaching 3.40 × 10−7 and 4.28 × 10−4, respectively. Compared with the best value among the three baseline controllers, the proposed method reduces the integral of squared error by approximately 42.6% and the integral of absolute error by approximately 34.4%. Although its maximum deviation is not the smallest among all compared controllers, it remains within the same order of magnitude as the advanced baselines. In addition, the average and maximum per-step computation times are 1.61 × 10−4 s and 3.75 × 10−3 s, respectively, both of which are far below the adopted sampling period of 0.05 s. These results indicate that the proposed framework improves cumulative tracking accuracy while maintaining feasible online computational performance.