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The self-excited vibration of flexible planar 3-RRR parallel manipulators is converted from the residual vibration after high-speed motion and is a resonance of the strongly coupled and nonlinear electromechanical system. This makes the active vibration control quite a challenging task. In this study, we attempt to adopt the radial basis function neural network control algorithm based on acceleration feedback for suppressing the self-excited vibration and guarantee its position accuracy. The stability of the controlled system is proved by the Lyapunov concept. Self-excited vibration control experiments are conducted near the singular region. Experimental results demonstrate the effectiveness of our adopted controller.

Position sensing is essential to testify the validity of the mechanical design and verify the performance in micromanipulation. A practical system for non-contact micro-motion measurement of compliant nanopositioning stages and micromanipulators is proposed using computer micro-vision. The micro-motion measurement method integrates optical microscopy and an optical flow-based technique, in which the motions of complaint mechanisms are precisely detected and measured. Simulations are carried out to validate the robustness of the proposed method, while the micro-vision system and a laser interferometer measurement system are also built up for a series of experiments. The experimental results demonstrate that the proposed measurement system possesses high stability, extensibility, and precision with 0.06 µm absolute accuracy and 0.05 µm standard deviation.

To deal with the challenges that the classical Bouc–Wen model fails to precisely characterize amplitude-dependent hysteresis and asymmetric hysteresis, an improved Bouc–Wen model with variable parameters is presented. The proposed model introduces asymmetric terms and parameter functions related to sinusoidal excitation amplitudes into the classical Bouc–Wen model. It has a relatively simple mathematic form and can be easily identified and applied for inverse feedforward compensation in real-time applications. By comparison with the classical Bouc–Wen model and other existing hysteresis models, the superiority of the proposed model has been verified. Furthermore, inverse hysteresis control and hybrid control combining the developed inverse control and proportional-integral feedback control are proposed. Several comparative experiments are conducted on a piezo-actuated micro-scanner. Results demonstrate that inverse control and hybrid control using the improved Bouc–Wen model with variable parameters can achieve better tracking performance and are meaningful in actual trajectory-tracking applications.

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