Almost output regulation model reference adaptive control for switched systems: combined adaptive strategy

ABSTRACT

This paper proposes the almost output regulation model reference adaptive control problem for switched systems with parametric uncertainties and multiple types of disturbances. By the almost output regulation model reference adaptive control strategy, the parametric uncertainties are suppressed and the multiple types of disturbances are rejected, simultaneously. In order to improve the transient performance of switched systems, the combined model reference adaptive control strategy is applied. A key point is to set up the almost output regulation model reference adaptive control strategy to achieve the tracking and the disturbance rejection performance. First, we design a switched identification model to estimate the plant uncertain parameters, by which more accurate plant parameters will be obtained. Secondly, based on the switched identification model, controllers with combined adaptive laws and a state-dependent switching law are designed to solve the almost output regulation model reference adaptive control problem for switched systems. Finally, a sufficient condition is given, which guarantees that the problem of the almost output regulation model reference adaptive control for switched systems is solvable in spite of the problem of almost output regulation model reference adaptive control for each subsystem is not solvable.

KEYWORDS:

• Switched systems
• model reference adaptive control
• output regulation
• combined adaptive control
• disturbance rejection

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

Jing Xie  http://orcid.org/0000-0002-1094-0528

Additional information

Funding

This work was partially supported by National Natural Science Foundation of China [grant numbers 61803225, 61803274, 61903261], partially by the Natural Science Foundation of Liaoning Province [grant number 2019-BS-179], partially by the General Project of Scientific Research of the Education Department of Liaoning Province [grant numbers LJGD2019015, LQGD2019013], partially by China Postdoctoral Science Foundation [grant numbers 2018M641710, 2019M652352], partially by Post doctoral innovation project of Shandong Province [grant number 201903046], partially by the open project of Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University) [grant number MCCSE2018A02].

Notes on contributors

Jing Xie

Jing Xie is an associate professor at the Shenyang University of Technology, Shenyang, China. Hers research interests include switched systems and adaptive control.

Hua Yan

Hua Yan is a professor at the Shenyang University of Technology, Shenyang, China. Hers research interests include signal processing and detection technology and automatic equipment.

Shujiang Li

Shujiang Li is a professor at the Shenyang University of Technology, Shenyang, China. His research interests include complex system control and intelligent control.

Dong Yang

Dong Yang is an associate professor at the Qufu Normal University, Qufu, Shandong, China. His research interests include switched LPV systems and aero-engine control.