Adaptive control with optimal tracking performance

ABSTRACT

This paper provides a way to optimise the steady-state tracking performance of the adaptive control system in the presence of unknown external disturbances. A-priori knowledge of the dynamic model of the reference signal to be tracked is not completely known. Especially, the updatable non-empty admissible model set, which is consistent to the a-priori knowledge of the plant parameter and the online measurements, is computed. Treating the overall system performance as the criteria, the nominal model is optimally chosen within the admissible model set. The optimal nominal model is subsequently used to synthesise the optimal closed-loop controller that minimises the steady-state absolute value of the tracking error. Combining the above two aspects, an optimal adaptive control scheme is proposed. Because of the consistency of the identification criteria and control object, the adaptive control scheme proposed in this paper can achieve the overall optimal steady-state tracking performance, and the effect of the interplay between the identification and control of the adaptive system can be handled effectively. In addition, the computable optimal performance is also provided.

KEYWORDS:

• Adaptive control
• optimal tracking
• unknown reference signal
• unknown disturbances

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China [grant number 61573233], [grant number 51175315]; the Key Project of Natural Science Foundation of Guangdong Province [grant number 2015A030311017].

Notes on contributors

Sheng-Ping Li

Shengping Lireceived the M.S. degree in control theory and control egineering from Beijing Institute of Technology in 1992, Beijing China, and Ph.D. degree in control theory and control engineering from Huazhong University of Science and Technology in 1995, Wuhan, China. He is currently a professor at Department of Mechatronics Engineering, Shantou University, and servers as Dean of the college of graduate college at Shantou university. His current reseaerch interests include adaptive control, robust control and robust design of mechanical product.