Adaptive neural control for a class of non-affine pure-feedback nonlinear systems

ABSTRACT

A novel adaptive neural control scheme is designed for a class of pure-feedback nonlinear systems with non-affine functions possibly being discontinuous. The non-affine function is not necessary to be continuous with respect to control variables or input, and the bounds of non-affine function are unknown functions. Some compact sets are constructively introduced to investigate the bounds of non-affine function so as to cope with the difficulty from these unknown bounds. Moreover, the dynamic surface control technique has been utilised for handling with the problem of ‘explosion of complexity’, and the minimal learning parameter technique is also employed to overcome the problem of excessive parameters. Furthermore, it is highly proved that all the variables will always stay in the introduced compact sets, and all the signals in the closed-loop control system are semi-globally uniformly ultimately bounded by choosing the appropriate design parameters. Finally, simulation examples are provided to demonstrate the effectiveness of the designed approach.

KEYWORDS:

• Discontinuous function
• pure-feedback systems
• neural networks

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

National Natural Science Foundation of China [grant number 61304120], [grant number 61473307], [grant number 61603411].