Dynamic Event-Triggered ADP Optimized Tracking Control of Marine Surface Vessels With Asymmetric Time-Varying Full-State Constraint

An optimal tracking control problem is critical for marine surface vessels (MSVs) with full state constraints, input constraints, external disturbances, and model uncertainty terms. For solving an MSV's position and velocity constraints, a barrier Lyapunov function (BLF) and control barrier function (CBF) are introduced and combined with prescribed performance functions, and then, an adaptive dynamic programming (ADP) optimized tracking control strategy is presented based on the two functions. A dynamic event-triggered mechanism is employed, which reduces the number of triggers by tracking the intermittent transmission of error signals, thus avoiding actuator wear and excessive consumption of communication resources. The experience replay method is brought in the gradient descent method for improvement of the weight learning efficiency. The Lyapunov-based stability analysis shows that the designed tracking control strategy ensures that all signals and weights in the closed-loop system are semi-globally bounded with Zeno-free behavior. Numerical comparison simulation verifies the effectiveness and superiority of the strategy.