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A 3-PRR (three links with each link consisting of a prismatic pair and two rotating pairs) parallel platform was designed for application in a vacuum environment. To meet the requirement of high tracking accuracy of the 3-PRR parallel platform, a full closed-loop control precision tracking system with laser displacement sensors and linear grating encoders was analysed and implemented. Equally-spaced laser displacement sensors and linear grating encoders were adopted not only for measurement but also for feedback control. A feed-forward control method was applied for comparison before conducting the closed-loop feedback control experiments. The closed-loop control experiments were conducted by adopting the PI (proportion and integration) feedback control and RBF (radial basis function) neural network control algorithms. The experimental results demonstrate that the feed-forward control, PI feedback control, and RBF neural-network control algorithms all have a better control effect than that of semi-closed-loop control, which proves the validity of the designed full closed-loop control system based on the combination of laser displacement sensors and linear grating encoders.

An adaptive feedforward and combined vibration control system with variable step size and reference filter is analyzed and implemented to suppress the vibration of a kind of thin-walled structure. The theoretical analysis and experiments of adaptive feedforward and combined vibration control algorithms are presented. The experimental setup of a flexible plate bonded PZT (Lead Zirconate Titanate) sensors/actuators is constructed. The control algorithm combines an adaptive feedforward controller and an adaptive feedback controller together, which has shown a superior control performance in the experiments. Considering the disadvantages of fixed step size used in the conventional filtered-U least mean square (FULMS) control algorithm, a variable step-size (VSS) method is applied. Also, a reference filter is used to extract the desired signal from the positive feedback and measurement noise. A proportional derivative (PD) feedback control algorithm is also applied as comparison. The experimental results demonstrate that the adaptive combined control algorithm has better control performance than the adaptive feedforward and PD feedback control algorithms.

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