Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece.
Int J Neural Syst. 2010 Apr;20(2):129-48. doi: 10.1142/S0129065710002310.
The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks.
本文考虑了在动态和参数不确定性存在的情况下，对具有多个输入和状态（MIMS）的未知非线性动力系统进行间接自适应调节。该方法基于一种新的神经模糊动态系统描述，它使用模糊系统输出的模糊分区和与这些分区中心相关联的高阶神经网络（HONN）。每个高阶神经网络都近似于与每个中心相关联的一组模糊规则。间接调节是通过首先在当前操作点周围识别系统，然后使用其参数来设计控制律来实现的。提供了涉及的 HONN 的权重更新定律，这些定律保证在存在参数和动态不确定性的情况下，同时使识别误差和系统状态都达到零，同时保持闭环中的所有信号都有界。通过使用 Moore-Penrose 意义上的伪逆，构造控制信号以使其对平方和非平方系统都有效。通过采用一种新的参数跳跃方法而不是传统的投影方法，始终可以确保控制信号的存在。该方法的适用性通过对著名基准进行测试得到了验证。
