This paper tackles the challenges encountered in surgical continuum robotics by introducing a dynamic model tailored for a cable-driven continuum robot. The intricacies of dynamic modeling and control frequently lead to suboptimal outcomes. Prior studies have often lacked comprehensive descriptions of individual robot component movements, thereby impeding control processes, especially in the presence of external disturbances. Although machine learning-based models show promise across different domains, they face hurdles in continuum robotics due to the complexity of the systems involved. Traditional mathematical models, in contrast, offer explicit equations, providing better interpretability, unlike machine learning models that may struggle with generalization, especially in highly nonlinear systems like continuum robots. The developed model adeptly captures the kinematic and dynamic constraints of various robot segments, serving as the foundation for a robust optimized control strategy. This strategy, which integrates computed torque control and particle swarm optimization (PSO-CTC), enables real-time computation of joint torques based on feedback, ensuring precise and stable task execution even amidst external perturbations. Comparative analysis with an optimized proportional integral derivative (OPID) controller unequivocally demonstrates the superiority of the optimized computed torque controller (OCTC) in settling time, overshoot, and robustness against disturbances. This advancement represents a noteworthy contribution to robotics, with the potential to significantly enhance continuum robot performance in surgical and inspection applications, thereby fostering innovative advancements across various fields.

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