基于宽度学习系统的气动波纹管驱动器无模型跟踪控制

针对一款具有波纹管外形的充气伸长型气动软体驱动器(简称“气动波纹管驱动器”),提出一种基于宽度学习系统的无模型跟踪控制方法,使该驱动器有效跟踪期望轨迹.首先,介绍气动波纹管驱动器结构,以及气动波纹管驱动器整体实验平台工作原理.根据驱动器实时位置信息提出一种基于宽度学习系统的跟踪控制方法,受PID跟踪控制方法中积分项作用的启发,所提出控制方法不仅采用系统跟踪误差作为宽度学习系统的输入之一,还将跟踪误差对时间的积分项作为另一输入以消除期望轨迹与实际轨迹间的恒定偏差.然后,采用宽度学习系统计算得到控制气压,同时,利用基于梯度下降法的学习律在线调整宽度学习系统权值,进而减小驱动器跟踪误差.最后,通过实验验证所提出方法的有效性.所提出方法无需建立驱动器模型,能够简化控制器设计步骤,且与深度神经网络控制方法相比,能在避免计算量过大的前提下实现较高的跟踪控制精度.

Model-free tracking control of pneumatic bellow actuator based on broad learning system

In this paper, we choose a pneumatic soft actuator with a bellow shape(pneumatic bellow actuator) as the object, and propose a model-free tracking control method based on the broad learning system to realize its trajectory tracking control. We first introduce the structure of the pneumatic bellow actuator and the working principle of the experimental platform. Based on the real-time position information of the actuator, we propose a tracking control method based on the broad learning system. Inspired by the integral term in PID tracking control methods, we not only use the system tracking error as one of the inputs of the broad learning system, but also use the integral term of the tracking error as another input to eliminate the constant deviation between the desired and the actual trajectory. Then, we utilize the broad learning system to calculate the control pressure, and we adjust the weight of the broad learning system online by the learning law based on the gradient descent method to reduce the tracking error. Experiments are designed to verify the effectiveness of the proposed method. The proposed method does not need to establish a model and simplifies the controller design steps. Compared with the control methods based on the deep neural network, the proposed method can achieve higher tracking control accuracy without excessive computation.