Neural Adaptive Fixed-Time Control for Nonlinear Systems With Full-State Constraints

• Published in: IEEE transactions on cybernetics Vol. 53; no. 5; pp. 3048 - 3059
• Main Authors: Yuan, Xu, Chen, Bing, Lin, Chong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive control; Adaptive neural fixed-time control; Adaptive systems; Asymptotic stability; Backstepping; Constraints; Control systems; Control theory; Corresponding states; full-state constraints; Nonlinear control; Nonlinear systems; Stability criteria; Tracking control; Tracking errors
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2021.3125678

This article aims at this problem of adaptive neural tracking control for state-constrained systems. A general fixed-time stability criterion is first presented, by which an adaptive neural control algorithm is developed. Under the action of the proposed adaptive neural tracking controller, the tracking error converges into a small neighborhood around the origin in fixed time; meanwhile, all system states abide by the corresponding state constraints for all the time. The main difference between the present research and the previous control schemes for state-constrained systems is that this article proposes a novel and feasible approach to ensure that the constructed virtual control signals satisfy the state constraints on the corresponding states viewed as the virtual control inputs. Such an approach guarantees theoretically that all the system states cannot violate their constrained requirements at any time. Finally, two simulation examples provide support to the proposed results.