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

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.

Automated summary

This article addresses the issue of adaptive neural tracking control for state-constrained systems with a novel approach that ensures system states do not violate their constraints and tracking errors converge. The proposed fixed-time stability criterion and adaptive neural control algorithm contribute to this successful outcome, as supported by simulation examples.