Khazaee Mostafa, Markazi Amir H D, Omidi Ehsan
Digital Control Laboratory, School of Mechanical Engineering, Iran University of Science and Technology, Narmak, 16844 Tehran, Iran.
Nonlinear Intelligent Structures Laboratory, Department of Mechanical Engineering, The University of Alabama, Box 870276, Tuscaloosa, AL 35487, USA.
ISA Trans. 2015 Nov;59:314-24. doi: 10.1016/j.isatra.2015.10.010. Epub 2015 Oct 30.
In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound.
本文针对具有不确定动力学和未知输入延迟的非线性系统，提出了一种新的自适应模糊预测滑模控制（AFP-SMC）方法。控制单元由一个模糊推理系统和一个切换策略组成，模糊推理系统用于逼近理想线性化控制，切换策略用于补偿估计误差。此外，还采用了自适应模糊预测器来估计系统状态的未来值，以补偿时间延迟。自适应律用于调整控制器和预测器参数，基于Lyapunov-Krasovskii泛函保证系统的稳定性。为了评估该方法的有效性，给出了在桥式起重机系统上的仿真和实验。根据所得结果，AFP-SMC能够有效地控制具有已知界输入延迟的不确定非线性系统。
