基于递归型小波神经网络的感应电动机伺服驱动系统自适应控制

RECURRENT WAVELET NEURAL NETWORKS BASED ADAPTIVE CONTROL FOR SERVO DRIVE SYSTEM OF INDUCTION MOTOR

针对感应电动机伺服驱动系统具有的多变、强耦合、慢时变等非线性特性和不确定性扰动,传统的位置速 度PID控制策略不能保证轨迹跟踪的精度和良好的动态品质的问题;保证系统对系统内部参数波动和外界不确定 性扰动具有较好的鲁棒性,在矢量控制策略的基础上,提出了基于递归型小波神经网络的自适应控制方案。神经 网络参数的在线学习机制采用delta自适应律并结合了BP算法和梯度下降法,算法简单,计算量大大减少。仿真 的结果验证了方案的有效性。

An adaptive control system using recurrent wavelet neural networks (RWNN) is presented, which is based on vector control for an induction servo drive motor characterized by non-linear, multivariable, strong coupling, slow time-varying properties, etc, and many uncertainties such as mechanical parametric variation and external disturbance. The new method is to overcome the limitation of conventional position and velocity PID that depends on the accurate model and cannot guarantee satisfactory control performance and better dynamic characteristics. A back propagation algorithm based on grads descent is developed to train the RWNN on line using delta adaptation law. Simulation results demonstrate the effectiveness of the proposed method.