## 5 Conclusions  
This paper introduces the iFFDL-MFA-PID algorithm to address the challenges posed by sensor measurement noise and other disturbances in electric cargo forklift systems. The algorithm is specifically designed to handle uncertain disturbed systems. The effectiveness of the controller is verified through both theoretical analysis and simulation experiments. Firstly, the gradient estimation algorithm and time difference algorithm are employed based on the nonlinear general equation of the electric cargo forklift system. These algorithms effectively estimate time-varying parameters and uncertain nonlinear terms, enabling accurate estimation of changing parameters and nonlinear terms in the system. Furthermore, the proposed control strategy leverages the iPID algorithm and utilizes additional tracking error information to compensate for the decrease in system information accuracy caused by excessive linearization in the iFFDL-MFAC approach. This ensures better system output tracking by taking advantage of the benefits offered by the iPID algorithm. Additionally, the learning update law and parameter update law of the system are restructured to enhance the controller's structure, resulting in improved overall performance and stability. The simulation results demonstrate that the proposed control strategy effectively suppresses the harmful effects of disturbances on system stability, achieving more accurate tracking of the system output. This ensures the safe operation of electric forklifts and helps prevent accidents such as rollovers and rear-end collisions during operation. In addition, in future work, we plan to extend the theory of autonomous driving for cars to forklift systems. By integrating image recognition technology, we can make the forklift autonomous driving system more intelligent, safe, and efficient, providing drivers with a safer driving experience.  
It should be noted that adaptively adjusting the controller parameters without relying on model information is crucial. For instance, PID control parameter-tuning methods, such as iterative feedback tuning and virtual reference feedback tuning, are data-driven methods. Therefore, in future research, we plan to develop parameter-tuning mechanisms that reference these methods to enhance control performance. While the adaptive adjustment  
Figure 10: Electric forklift acceleration curves.  
of controller parameters without relying on model information is crucial, we recognize that in specific engineering applications, reliance on model information remains vital. Therefore, the purpose of this study is not to completely negate the importance of model information but to emphasize the advantages and practicality of adaptively adjusting controller parameters in the absence of detailed model information.  
Conceptualization, J.X.; Validation, Z.S.; Formal analysis, F.X.; Data curation, Y.W.; Writing--original draft, J.X.; Writing--review & editing, Z.S.; Supervision, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Quzhou City Science and Technology Plan project: (2023K263, 2023K265, 2023K045) and General Research Project of Zhejiang Provincial Department of Education (2023): (Y202353440, Y202353289).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.