This paper presents a robust proportional derivative adaptive nonsingular finite-time synergetic tracking control (PDAFS) for a parallel Delta robot system. First, a finite-time synergetic controller combined with a proportional derivative (PD) control is constructed based on an object-oriented model to fulfill the robust tracking control of the robot. Then, an adaptive radial basis function approximation neural network (RBF) is designed to compensate for the effects of uncertainty parameters and external disturbances. Second, a second-order sliding mode (SOSM) differentiator is implemented to reduce the chattering noises due to the low-resolution encoders. Third, the stability theorems of the proposed control scheme are provided, where the Lyapunov stability theory is used to prove the theorems. Then, simulations of the helix trajectory tracking and the pick-and-place task are demonstrated on the Delta robot to validate the advantages of the proposed control scheme. Based on the advances, an implementing control system of the proposed controller is performed to improve the Delta robot’s performance in the experiments.

This paper introduces a Hexa parallel robot and obstacle collision detection method based on dynamic modeling and a computer vision system. The processes to deal with the collision issues refer to collision detection, collision isolation, and collision identification applied to the Hexa robot, respectively, in this paper. Initially, the configuration, kinematic and dynamic characteristics during movement trajectories of the Hexa parallel robot are analyzed to perform the knowledge extraction for the method. Next, a virtual force sensor is presented to estimate the collision detection signal created as a combination of the solution to the inverse dynamics and a low-pass filter. Then, a vision system consisting of dual-depth cameras is designed for obstacle isolation and determining the contact point location at the end-effector, an arm, and a rod of the Hexa robot. Finally, a recursive Newton-Euler algorithm is applied to compute contact forces caused by collision cases with the real-Hexa robot. Based on the experimental results, the force identification is compared to sensor forces for the performance evaluation of the proposed collision detection method.

This paper proposes a novel robust proportional derivative adaptive non-singular synergetic control (PDATS) for the delta robot system. A proposal radial basis function approximation neural networks (RBF) compensates for external disturbances and uncertainty parameters. To counteract the chattering noise of the low-resolution encoder, a second-order sliding mode (SOSM) observer in the feedback loop showed the ability to obtain the angular velocity estimations. The stability of the PDATS approach is proven using the Lyapunov stability theory. Both the simulation and experiment result effectiveness and performances of the PDATS controller in trajectory; pick and place operations of a parallel delta robot. The characteristics of the controller demonstrate that the proposed method can effectively reduce external disturbance and uncertainty parameters of the robot by a convergent finite-time, and provide higher accuracy in comparison with finite-time synergetic control and PD control.