Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece.
Int J Neural Syst. 2010 Aug;20(4):319-39. doi: 10.1142/S0129065710002449.
The direct adaptive regulation of unknown nonlinear dynamical systems in Brunovsky form with modeling error effects, is considered in this paper. Since the plant is considered unknown, we propose its approximation by a special form of a Brunovsky type neuro-fuzzy dynamical system (NFDS) assuming also the existence of disturbance expressed as modeling error terms depending on both input and system states plus a not-necessarily-known constant value. The development is combined with a sensitivity analysis of the closed loop and provides a comprehensive and rigorous analysis of the stability properties. The existence and boundness of the control signal is always assured by introducing a novel method of parameter hopping and incorporating it in weight updating laws. Simulations illustrate the potency of the method and its applicability is tested on well known benchmarks, as well as in a bioreactor application. It is shown that the proposed approach is superior to the case of simple recurrent high order neural networks (HONN's).
本文考虑了具有建模误差影响的 Brunovsky 形式的未知非线性动力系统的直接自适应调节。由于所考虑的植物是未知的，我们通过假设存在依赖于输入和系统状态的建模误差项以及一个不一定为已知的常数的特殊形式的 Brunovsky 型神经模糊动态系统 (NFDS) 来对其进行近似。该开发与闭环的灵敏度分析相结合，为稳定性特性提供了全面而严格的分析。通过引入一种新的参数跳跃方法并将其纳入权重更新定律，始终可以确保控制信号的存在和有界性。仿真结果说明了该方法的有效性，并在知名基准以及生物反应器应用中对其适用性进行了测试。结果表明，所提出的方法优于简单的递归高阶神经网络 (HONN) 的情况。
