A novel switching adaptive control for randomly switching systems with an application to suspension systems

Xuan Phu Do, Mien Van, Ngoc Phi Nguyen

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
32 Downloads (Pure)


This paper presents a new control algorithm for controlling randomly switching systems under random disturbance. The proposed controller is designed based on a dual prescribed sliding mode control and a dual modified Riccati-like equation. The sliding surface of the proposed controller includes two parts: a classical type and a prescribed performance. The combined sliding surface provides strong disturbance rejection capability for the system. The dual Riccati equation provides two functionalities: (i) a classical type associated with the system parameters, and (ii) a disturbance-type with parameters associated with the boundaries of disturbance. A fuzzy model, which functions as a filter, is applied in the proposed control system for approximating uncertainty. Its fuzzified values are also used in the adaptation laws to improve the system performance. One of the innovative features of the proposed approach is expressed by its adaptation laws, which are formed by the constraint matrices, which are related to both the nominal system and disturbance. In these laws, the parameters of the PID component are updated based on the main matrices of Riccati equations. The composite controller, which combines a sliding mode control, Riccati equations, PID control, and fuzzy model, helps to improve the performance of the classical controls when the system works under severe disturbance conditions. The proposed controller is employed for a suspension system of bus driver and compared with an existing controller under two randomly disturbance conditions. The results of a computer simulation show that the proposed controller obtains good performance in terms of vibration compression.
Original languageEnglish
Article number100635
Number of pages13
JournalEuropean Journal of Control
Early online date04 Mar 2022
Publication statusPublished - May 2022


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