## 8 Conclusions and Future Directions  
The primary objective of this research was to control the position of a knee exoskeleton robot. To achieve this, we employed an affine state-feedback controller technique to regulate the robot's movement during rehabilitation sessions, taking into account its complex nonlinear dynamics and inherent disturbances. As a result, we developed four methods for establishing LMI conditions to ensure stability in a controlled knee exoskeleton. These methods utilize an extended dynamic model to capture the differences between the linear approximation and the nonlinear model. These methods involve formulating the LMI requirements for the feedback gain in the control scheme using several lemmas. Additionally, we conducted a comparison with classical PD and PID controllers. The numerical and simulation findings effectively illustrate the stabilization of the controlled knee exoskeleton robot. This validation confirms the proposed LMI conditions and highlights the effectiveness of the affine state-feedback control law in addressing the robust control challenge under study and confirms the validity of the proposed LMI conditions. Furthermore, the results indicate that the selected affine state-feedback control approach outperforms the traditional PID and PD controllers.  
Simulations have been conducted in the present study to validate the proposed control methodology. Future work will involve experimental validation using a physical lower-limb exoskeleton robot. The planned experiments will include elderly and pediatric participants in clinical and controlled laboratory conditions to assess the actual performance of the proposed controller. Program parameters such as stability, precision, and user comfort will be evaluated to determine whether the proposed approach is suitable for real-life rehabilitation settings. To bridge the gap between theory and reality, complex modeling and simulation environments, including Gazebo, CoppeliaSim, and MuJoCo, will be employed to emulate practical scenarios. Additionally, a new LMI-based control technique will be developed. The controller will be extended to handle uncertainties and external disturbances, such as state estimation through an observer. Future work will also involve further development of the LMI framework to address more complex rehabilitation tasks, including the movement of multiple lower-limb joints such as the knee, ankle, and hip, while focusing on energy minimization. The uncertainties will be modeled in detail to assess their impact on the controller's performance. Furthermore, the performance of the developed system will be compared with modern control methods, including adaptive control and artificial intelligence-based control systems. This comparison will be extended to high-DoF rehabilitation exoskeletons to cover a wide range of clinical applications. Finally, field tests will be conducted to evaluate the efficiency of the proposed system in real rehabilitation scenarios, with particular emphasis on patient safety, comfort, and adaptability to individual needs. Efforts will also be made to explore low-cost design improvements to facilitate the adoption of the proposed method in clinical practice.  
Formal analysis, [PERSON], H.G. and J.N.; investigation, [PERSON], H.G. and J.N.; methodology, S.J. and H.G.; project administration, H.G.; software, [PERSON] and H.G.; supervision, [PERSON] and J.N.; validation, [PERSON] and J.N.; writing--original draft, S.J.; writing--review and editing, [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.