الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Continuum robots are intricate structures that pose a significant challenge for modeling and trajectory tracking control development, as they are highly coupled, nonlinear systems with multiple degrees of freedom. Therefore, various controllers have been developed and implemented, using different control algorithms that rely on the system inverse dynamic model; proportional integral derivative (PID) controller, fuzzy logic controller (FLC), and sliding mode controller (SMC). where the system dynamic model was presented using Eular-Lagrange representation, which incorporates the effects of gravity and elasticity, founded on the piecewise constant curvature assumption (PCC), that captures the relationship between the cable lengths, joint angles, and end-effector pose, while system inverse kinematics was presented geometrically by solving trigonometric equations.
In addition, and as a tuning method, particle swarm optimization (PSO) has been developed and applied, which is evaluated using the integral time of absolute error (ITAE), seeking the optimal controller parameters K_p, K_i, and K_d, for the PID, and the FLC membership function’s ranges.
The proposed methods were validated through simulations on various two and three-dimensional trajectories, where the utilized MATLAB symbolic math toolbox in conjunction with Simulink simulation demonstrates and evaluate different close-loop dynamic control responses for the predesigned various scenarios. A distinctive animated simulation have developed a for a complete 3D continuum robot while tracking its desired trajectories using the simulation’s extensive results.
Finally, the simulation results demonstrate that our proposed methods can effectively control the Hyper-Redundant Manipulator (HRM) with high tracking performance, exhibiting enhanced responses in terms of settling time, rising time, and steady-state error. where the developed SMC outperforms both PID and FLC in terms of robustness and settling time of 51% and 10.3%, respectively. In contrast, the PSO-PID results show enhancements of 16.3%, 31.1%, and 64.9% for the rising time, overshoot percentage, and settling time, respectively, over the trial-and-error PID parameters. While the tuned membership function of the PSO-FLC shows the most enhanced control action response, demonstrated in the system response of (0.4 sec) rise time and (0.7 sec) settling time, leading to the highest level of precision in trajectory tracking. At the same time, the error measured by ITAE for the PSO-FLC shows an enhancement of 11.4% and 29.9% over the PSO-PID and FLC, respectively.