一类非线性离散系统的神经网络自适应控制

针对一类控制方向未知的单输入单输出非线性离散系统,将常规增量式数字PID控制器与自适应神经网络控制项相结合,提出了一种能够保证闭环系统稳定的自适应神经网络控制方法.常规PID控制器用来保证近似线性系统的稳定,自适应神经网络项用来处理非线性项对闭环系统的影响.在神经网络权值修正律中引入离散Nussbaum增益来解决被控系统控制方向未知的问题.证明了闭环系统的所有信号有界,且跟踪误差收敛于紧集,并通过仿真验证了所提方法的有效性.

Adaptive Neural Network Control for a Class of Nonlinear Discrete-Time Systems

For a class of single-input-single-output nonlinear discrete-time systems with unknown control direction, an adaptive neural network control was developed by incorporating a conventional incremental digital PID (proportional-integral-derivative) controller in an adaptive neural network term to guarantee the stability of the closed-loop systems. The conventional PID controller was utilized to stabilize the approximate linear system, while the adaptive neural network was introduced to deal with the influence of nonlinear terms on closed-loop systems. A discrete Nussbaum gain was introduced into the adaptation law of the weights in neural network to resolve the unknown control direction problem. It was proved that all signals of the closed-loop system are bounded with the tracking error converges on a compact set. Simulation results verified the effectiveness of the proposed control method.