Adaptive Dynamic Programming-Based Optimal Heading Control for State Constrained Unmanned Sailboat

This paper proposes a new state-constrained adaptive optimal control strategy for unmanned sailboat heading angle tracking considering the motion constraints. To improve the tracking accuracy, a combination of backstepping and adaptive dynamic programming (ADP) is employed. Thus, the issue of virtual control rate derivatives in conventional backstepping control is resolved with satisfactory precision. Firstly, the motion constraints are considered by using the Barrier Lyapunov function (BLF), and the neural networks (NNs) is employed to approximate the model uncertainties and disturbances. Secondly, an adaptive backstepping feedforward controller is proposed, transforming the sailboat's affine nonlinear system tracking problem into a regulation problem. Thirdly, according to the ADP theory, critic NNs are constructed to approximate the analytical solution of the Hamilton-Jacobi-Bellman (HJB) equation, and the optimal feedback control is obtained by online learning. Finally, simulation results demonstrate the effectiveness and optimality of the proposed controller.