基于改进自适应遗传算法的舵机系统辨识方法

Steering System Identification Based on Improved Adaptive Genetic Algorithm

为了更准确地测量舵机的性能指标,利用系统识别技术来建立其数学模型具有很关键的作用。首先分析并建立电动伺服系统各部分结构模型,然后应用改进的自适应遗传算法(improved adaptive genetic algorithm, IAGA)进行系统辨识,同时将标准测试函数用于改进算法和不同智能优化算法的性能测试。最后将建立的模型作为辨识系统模型进行参数辨识,并将具有不同信噪比(signal noise ratio,SNR)的噪声信号添加到电动舵机的输出端以验证算法的稳定性。舵机实际实验结果表明,该方法参数优化精度高,抗噪声能力强且具有重要的工程使用价值。

To measure the performance index of the steering gear more accurately, establishing a mathematical model with system identification technology is very important. First, the structural model of each part of the electric servo system was analyzed. Second, an improved adaptive genetic algorithm(IAGA) was developed for the system identification, during which the standard test function was used to improve the performance of the algorithm and different intelligent optimization algorithms. Finally, the established model was applied as the identification system model for parameter recognition and the noise signal of different signal noise ratios(SNR)was added to the output of the electric steering gear to verify the stability of the algorithm. The experimental results of the actuator show that the method featured high precision of parameter optimization, strong anti-noise ability, and important engineering application value.