This paper introduces a novel control method for a three-link parallel manipulator robot using Type-2 Fuzzy Logic Control. The primary objective is to enhance the robot’s performance beyond what is achievable with a traditional Proportional Derivative (PD) controller. Specifically, the focus lies on optimizing the synchronization of the robot’s three links, which is crucial for improved coordination during circular trajectory operations. By employing Type-2 Fuzzy Logic, the proposed method effectively minimizes trajectory errors in the end-effector’s positions along the x and y axes. Furthermore, it achieves a zero percent error reduction in the joint angles (theta 1, 2, and 3), indicating significant performance improvement. Further analysis demonstrates that this approach offers superior performance of the dynamic model of the robot, leveraging the Euler-Lagrange equation. The implementation is carried out using MATLAB-Simulink, a popular platform for simulating dynamic systems. Notably, the mean square errors (MSE) for the position are significantly reduced with Type-2 Fuzzy Logic compared to the conventional PD controller. For position along the X-axis (ex), the PD controller has an MSE of 0.0035, while Type-2 Fuzzy achieves an impressive 1.2008e−11. Similarly, for position along the Y-axis (ey), the PD controller has an MSE of 0.0016, whereas Type-2 Fuzzy achieves 1.6677e−05. Moreover, the joint angle errors ( \(\theta\)  = 1, 2, and 3) are effectively eliminated with Type-2 Fuzzy Logic. The PD controller exhibits MSE values of 0.5425, 0.7837, and 2.6243 for theta 1, 2, and 3, respectively, while the Type-2 Fuzzy approach achieves zero error reduction in these angles. The gain parameters for the Fuzzy Type-2 controller are self-tuned, resulting in improved steady-state torques. Specifically, the torques (T1, T2, and T3) are significantly reduced compared to the conventional PD controller. For instance, T1 has a value of 0.07667 for the PD controller and 0.01749 for Type-2 Fuzzy. Similarly, T2 and T3 exhibit reduced values with Type-2 Fuzzy. However, this research presents a promising avenue for enhancing the performance of three-link parallel manipulator robots by combining Type-2 fuzzy logic control with synchronization optimization. Future research could explore the potential of this method in other types of robotic systems.