## 1 Introduction  
Delta robots are fast, accurate, and versatile, suitable for tasks like assembly, pick-and-place, sorting, and classification. The Delta robot is a parallel robot with three arms connected to an end effector mounted on its base, exhibiting an over-actuated configuration. Lagrangian dynamics, inverse kinematics, and screw theory are three common approaches to model the Delta robot [1, 2, 3]. Knowing the dynamic model is essential for motion planning and trajectory tracking. It allows the controller to calculate the required joint inputs to achieve a desired end effector position. The control design of a Delta robot is challenging due to its high level of non-linearity and complicated kinematic and dynamic model. Therefore, plenty of nonlinear and adaptive control algorithms have been implemented for trajectory tracking and disturbance rejection of the Delta robot. Sliding mode control is one of the most popular nonlinear control algorithms [4, 5]. This algorithm has been extensively applied to the trajectory tracking of the Delta robot combined with the fuzzy neural network [6], nonlinear proportional-derivative (PD) control [7], and synergetic control [8]. Additionally, Radial Basis Function Neural Networks (RBFNNs) combined with sliding mode control were adopted in [9, 8] to compensate for the unknown disturbances. The work reported in [10] proposed an online estimation approach to compensate for various uncertainties in the Delta robot and to improve the tracking performance. Anadaptive active disturbance rejection control was adopted for the output-based robust trajectory tracking of the Delta robot in [11]. Iterative learning control has also been applied to the trajectory tracking of the Delta robot with a high performance. [PERSON] and [PERSON] [12] proposed a PD-type ILC combined with PD control to improve the iteration performance. Model-free iterative learning control was further implemented on the Delta robot with non-repetitive trajectories [13]. Moreover, a model reference adaptive control has also been adopted for control of the Delta robot. Combining an identified linear model with mode reference adaptive control has shown a significant performance improvement compared to the three commonly used methods: PID, adaptive control, and sliding mode control algorithms [14]. Besides traditional control algorithms, neural networks were also adopted to improve the trajectory-tracking performance of the Delta robot. An inverse kinematic controller using neural networks was presented in [15] for trajectory control of a Delta robot in real-time by making use of data collected from simulation. [PERSON]. [16] proposed a data-driven optimal control algorithm by approximating the solution of the Hamilton-Jacobi-Bellman equation using neural networks. A neural network model was proposed in [17] to approximate the inverse kinematics of the Delta robot with randomly sampled experimental data and was implemented on the hardware in an open-loop control for trajectory tracking. Most of the advanced control algorithms mentioned above are either data-driven or model-based. Model-based control algorithms are highly dependent on the accuracy of the model. Therefore, they have high requirements for control robustness in terms of model uncertainties and disturbances. On the other hand, data-driven algorithms operate independent of model accuracy requirements. Given the paramount importance to safety considerations during normal operations, ensuring robustness and maintaining optimal performance pose significant challenges in the control design of Delta robots employing data-driven algorithms. Moreover, another challenge in Delta robot control is the singularity [18; 19], which occurs when the end effector is positioned at specific locations where the kinematic model degenerates and the joint angles become indeterminate, which can lead to erratic behavior and potential damage to the robot.