Human-in-the-Loop Adaptive-Enhanced Constraint Management for Switched Systems via Self-Adjusting Feedback Triggering Mechanism

Human-in-the-loop switched systems often suffer from unmeasurable states and strict safety constraints, which may lead to unacceptable workload or even catastrophic errors for human operators. This article proposes an adaptive-enhanced constraint-management strategy for such human-machine shared-control platforms. The main results and contributions are as follows. first, a self-adjusting event-triggered mechanism is introduced to reduce the update frequency of the autopilot, alleviating the operator's cognitive burden while ensuring communication efficiency; Second, an adaptive state observer is designed to reconstruct unmeasurable states in real time, providing accurate state information for feedback control; Third, by incorporating a tan-type barrier Lyapunov function, we theoretically guarantee that all state variables strictly remain within predefined human-safe ranges throughout system operation; fourth, fuzzy-logic systems are employed to approximate unknown nonlinearities arising from pilot-autopilot interactions, with established approximation error bounds; Finally, under the average dwell-time switching law, we rigorously prove that the closed-loop human-machine system achieves uniformly ultimately bounded stability while excluding Zeno behavior. Numerical simulation results validate the effectiveness of the proposed approach, demonstrating improved tracking accuracy and reduced control update frequency compared with traditional periodic control schemes.