压电作动器非对称迟滞模型的建立和参数辨识

现有的众多基于传统Bouc-Wen改进的压电陶瓷非对称迟滞模型存在参数冗余,降低了模型参数辨识的准确性,而且常用的粒子群算法(PSO)在辨识压电陶瓷非对称迟滞模型参数方面收敛慢且容易陷入局部最优值。为此,首先提出了一种归一化的非对称迟滞模型,采用两个多项式达到非对称效果,利用归一化Bouc-Wen消除参数冗余;然后采用参数和变异策略自适应的差分进化算法进行迟滞参数辨识;建立了相应的测试系统,对压电陶瓷作动器进行了实验研究。结果表明,相比于传统的Bouc-Wen模型,所提出的模型能更精确地描述压电陶瓷实际电压位移曲线,而且消除了参数的冗余,降低了参数辨识的难度。相比于粒子群算法和传统差分进化算法,自适应差分进化算法能更快更精准地找到最优参数值。

Modeling and parameter identification of asymmetric hysteresis for piezoelectric actuator

Many asymmetric hysteresis models based on traditional Bouc Wen for the piezoelectric actuator have some redundant parameters, which reduce the accuracy of parameter identification. The most frequently used particle swarm algorithm converges slowly and is easy to fall into local optimum in terms of parameter identification of the piezoelectric actuator. Thus, a normalized asymmetric hysteresis model is proposed by introducing two polynomials to describe the asymmetric hysteresis behavior and using the normalized Bouc Wen model to eliminate the redundancy of the parameters. Tthe self adaptive differential evolution algorithm is developed for parameter identification, in which both associated control parameters and trial vector generation strategies can be self adapted with the increase of generations. An experimental system about the piezoelectric actuator is set up. The results show that the proposed model is better to represent the actual characteristic of the piezoelectric actuator and successfully eliminate the redundancy of the parameters, which decreases the difficulty of parameter identification. Compared with traditional differential evolution algorithm and particle swarm algorithm, the self adaptive differential evolution algorithm can find the optimal solution more quickly and more accurately.