This paper presents an intelligent adaptive proportional-integral-derivative (PID) control method using fuzzy broad learning system (FBLS) and investigates how the method can be applied to control a tool-grinding servo control (TGSC) system. Due to accuracy, quality and geometric errors which are often difficult to capture the dynamics of the controlled plants or systems, fixed-gain PID controllers without good three-term parameters cannot meet the stringent control performance specifications of nonlinear industrial systems and servomechanisms. To accomplish better control, an adaptive PID control strategy based on the FBLS, or abbreviated as FBLS-APPID, is rigorously proposed by integrating an online parameter learning FBLS identifier together with an adaptive predictive PID control law using FBLS, to eliminate tracking error and achieve fast-tracking and disturbance rejection. Numerical simulations on the two existing discrete-time nonlinear time-delay processes are performed to show the merits and superiority of the constructed FBLS-APPID by comparing to three existing adaptive PID methods. Finally, the applicability of the proposed method is well exemplified by conducting comparatively experimental results on a servo control loop of a real TGSC machine with fixed PID gains tuned by the proposed FBLS-APPID method.

本文提出了一种使用模糊广义学习系统（FBLS）的智能自适应比例积分微分（PID）控制方法，并研究了如何将该方法应用于控制刀具磨削伺服控制（TGSC）系统。由于精度，质量和几何误差（通常难以捕获受控设备或系统的动态），因此，没有良好的三项参数的固定增益PID控制器无法满足非线性工业系统和伺服机构严格的控制性能要求。为了实现更好的控制，通过将在线参数学习FBLS标识符与使用FBLS的自适应预测PID控制律集成在一起，严格提出了一种基于FBLS的自适应PID控制策略，简称FBLS-APPID。消除跟踪误差并实现快速跟踪和干扰抑制。通过与现有的三种自适应PID方法进行比较，对两个现有的离散时间非线性时滞过程进行了数值模拟，以显示所构造的FBLS-APPID的优缺点。最后，通过在实际TGSC机器的伺服控制回路上进行比较实验结果，充分证明了该方法的适用性，该TGSC机器具有通过FBLS-APPID方法调整的固定PID增益。