LAUSR

The inherent hysteresis property of piezoelectric actuator (PEA) brings challenges to its modeling and control. This paper proposes a model learning method that is suitable for both forward and inverse PEA models. The hysteresis property is learned based on least squares support vector machines (LS-SVMs). A larger dataset is used for training LS-SVM to guarantee a good generalization performance. Support vectors pruning is utilized to reduce the model complexity. The rate-dependent property of PEA is identified as a linear dynamic submodel. Moreover, a pointing control system with two dual-PEA-axis steering mirrors is developed, which can regulate the 4-degree-of-freedom pose of a laser beam. The coordinated control of four PEAs is realized based on the Jacobian matrix. The learned inverse PEA models are used for the feedforward compensation of each PEA’s nonlinearity. A series of experiments were conducted to evaluate the proposed method’s effectiveness.