Robust adaptive dynamic surface control of high-order strict feedback systems based on fully actuated system approach

Abstract

In this study, the authors propose a robust adaptive dynamic surface control method by using the fully actuated system approach for high-order strict-feedback systems (SFSs) with parameter uncertainty and disturbance. Each subsystem of the SFSs is a high-order system with a full actuation structure. In contrast to the traditional first-order state space method, the proposed control method directly treats each high-order subsystem as a whole without transforming it into a first-order system, which is a concise and efficient treatment. By introducing a series of first-order low-pass filters in each step of the design, the high-order derivatives of the virtual control law are obtained, and the complex and multiple derivation operations are transformed into simple algebraic operations. Adaptive control and robust control are combined to address parameter uncertainty and external disturbances in the system. The Lyapunov stability theory is utilised to demonstrate that all signals in the closed-loop system are uniformly ultimately bounded. The system output can effectively track the desired reference signal under specific constraints, and the tracking error can be adjusted by tweaking the parameters to converge to a sufficiently small neighbourhood around zero. Ultimately, the efficiency of the proposed control method is validated through simulations on a flexible joint manipulator system.

Keywords:

• Fully actuated system approach
• high-order strict feedback systems
• robust adaptive control
• dynamic surface control
• Lyapunov stability theory

Disclosure statement

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

Data availability statement

Data sharing is not applicable to this article as it describes entirely theoretical research.

Additional information

Funding

This work was supported in part by the Major Program of National Natural Science Foundation of China [grant numbers 61690210 and 61690212]; in part by the National Natural Science Foundation of China [grant number 61773387]; and in part by the National Natural Science Foundation of China [grant number 62188101].