## 4 Discussion
### Limitations and Further Research
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Title** & **Aim** & **Actions** & **Expected Results** \\ \hline Optimization of model parameters and increased error tolerance [74]. & Develop methods for identifying parameters with increased accuracy and error tolerance. & Study more accurate methods for estimating the controlled object parameters using machine learning and adaptive filtering methods. & Reduce the model’s sensitivity to errors in parameters, increasing the modelling and control accuracy. \\ \hline Controller adaptation and tuning simplification [75; 76]. & Simplify the process of tuning controller adaptation parameters and optimization coefficients for practical applications. & Investigate new approaches for the automatic tuning of controller parameters (e.g., using optimization methods such as genetic algorithms or gradient descent). & Simplify the tuning process and increase the method’s flexibility when applied to various control objects. \\ \hline Processing complex nonlinearities [77]. & Develop methods for more efficient processing of complex nonlinearities in object dynamics. & Study more complex neural network architectures (e.g., deep neural networks considering multilevel nonlinear dependencies). & Increase the accuracy and universality of the model for systems with different nonlinear dynamics. \\ \hline Robustness to noise and data quality [78]. & Improve the method’s robustness to noise and poor data quality. & Develop data preprocessing methods, including improving noise filtering and eliminating sensor errors. & Improve the accuracy of the method by improving the input data quality. \\ \hline Reducing computational complexity [79]. & Optimizing computational processes and reducing the need for computational resources. & Developing more efficient algorithms for adapting the training rate and regularization to speed up computations. Investigating the use of hardware accelerators (e.g., neural processors or graphics processors) to optimize computational processes. & Reduce computational costs while maintaining the high accuracy of the method. \\ \hline Increasing the method generalization ability [80]. & Develop methods for adaptation and training transfer to apply the technique to the systems and engines’ new types. & Study methods for training transfer that allow the model to be adapted to different systems and conditions. & Increase the generalization ability of the method and its successful application to architectural types of engines and systems. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The proposed roadmap for future research. The roadmap's expected results include improvements in the method's accuracy and reliability, which will allow for its applications to be expanded in various fields [84, 85, 86], as well as increased computational efficiency and reduced dependence on hardware resources. In addition, we plan to expand the method's scope, including its adaptation for working with complex dynamic systems with long delays [87, 88, 89], which will increase the universality and practical value of the approach.