Unscented Kalman Filter‐Based Dynamic Event‐Triggered Adaptive Control of Nonlinear Systems With Sensor Faults and Full State Constraints

This paper studies the nonlinear event‐triggered tracking control problem with sensor faults and full state constraints. First, an adaptive compensation law is constructed to handle sensor fault. Next, a first‐order filter is used to reduce the computational complexity caused by the derivation of the virtual control law. Finally, a novel dynamic event‐triggered mechanism (ETM) using an unscented Kalman filter (UKF) is designed to minimize the number of triggers. In addition, barrier Lyapunov functions (BLFs) are introduced to ensure state constraints. Simulation results confirm the effectiveness of the proposed scheme.