Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking

Abstract

The underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the first-order Nomoto model in the Serret–Frenet coordinate system. A novel risk model has been developed to depict the association between USVs and obstacles based on SFC. Combined with an artificial potential field that accounts for environmental obstacles, model predictive control (MPC) based on state space is employed to achieve the optimal control sequence. The stability of the designed controller is demonstrated by means of the Lyapunov method and zero-pole analysis. Through simulation, it has been demonstrated that the controller is asymptotically stable concerning track error deviation, heading angle deviation, and heading angle speed, and its good stability and robustness in the presence of multiple risks are verified.

Variable Frequency Resonant Controller Based on Generalized Predictive Control for Biased-Sinusoidal Reference Tracking and Multi-Layer Perceptron

“…is the input and y k is the output. The matrices A, B and C can be obtained through the discretization process (e.g., forward Euler method or Tustin method) of the continuous-time space-state model of the plant [36][37][38]. Equation (3) defines the tracking error e k :…”