Abstract:

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.