Currently, about 60% of the finishing works for freeform parts are done manually, in this paper a parallel kinematic polishing machine (PKPM) with five degrees of freedom (DOFs) and with structural configurations of the type T3R2 (three translations and two rotations) is presented. The PKPM is a variation of the existing METROM Pentapod. The PKPM has a large yaw angle space and, with the aid of an NC rotation table, it can access any given point of the freeform part. A kinematic analysis is developed, an inverse dynamic model based on Kane's equation and the affine projection method is set up in detail, and a real-time and highly efficient inverse dynamic algorithm is developed; the simulation results show that the presented PKPM could achieve an acceleration which is twice that of gravity. A mixed  /  control method is presented and investigated 2∞ in order to track the error control of the inverse dynamic model; the simulation results from different conditions show that the mixed  /  control method could 2∞ achieve an optimal and robust control performance. This work shows that the presented PKPM has a higher dynamic performance than conventional machine tools

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According to the robot’s dynamics, a high performance algorithm based on dynamic surface control is introduced to track desired trajectory, and simulations are conducted on a selective compliance assembly robot arm-type manipulator to verify the algorithm. The traditional dynamic surface control is designed based on dynamic model, which requires exact model information. Due to the model uncertainty and complex environments, the tracking performance of the controller can be significantly decreased. Therefore, a model-free fuzzy adaptive dynamic surface controller is designed, by adopting a fuzzy system with Lyapunov self-adaptation law. The new controller efficiently improves the dynamic quality. The simulation results prove that the designed model-free controller ensures that all the states and signals of the closed-loop system are bounded, the system has a faster response speed and smaller steady-state error comparing with the traditional dynamic surface control using the selective compliance assembly robot arm model, and the tracking error converge to a very small scale. Besides, the proposed algorithm can track the desired trajectory with high performance without the prior knowledge of specific parameters from the experimental manipulator, which simplifies the complexity of building the control system.

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NA. Laryngoscope, 126:566-569, 2016.

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