非线性离散系统的直接自适应神经网络控制器

对于一类非仿射离散时间系统，提出了一种新的自适应神经网络控制器。首先推导与原系统等价的仿射形式模型，由仿射模型推导控制律。控制律中采用一个神经网络，与传统的基于反馈线性化的自适应神经网络设计方法中采用两个神经网络相比，计算量大大减少且避免了控制器奇异问题。神经网络权值根据系统输入输出信号进行更新，另外σ项的引入，取消了为保证参数收敛持续激励的条件。系统的稳定性通过Lyapunov方法进行了分析，仿真实例验证了控制器的有效性。

Direct adaptive control for nonlinear discrete time systems using neural networks

A new neural network adaptive control is proposed for unknown nonaffine nonlinear discrete-time systems.An equivalent model in affine-like form is derived for the original nonaffine discrete-time systems.Then,feedback linearization adaptive control is implemented based on the affine-like equivalent model.The control input is derived by only one neural network(NN) and the computational burden is reduced significantly compared with the conventional adaptive control method,in which two NNs are required.The weights of the neural network used in adaptive control are directly updated online based on the input-output measurement.The σ-modification technique is used to remove the requirement of persistence excitation during the adaptation.With the proposed neural network adaptive control,the stability and performance of the closed-loop system is rigorously established.Two illustrated examples are provided to validate the theoretical findings.