Real-Time Weight Optimization of a Nonlinear Model Predictive Controller Using a Genetic Algorithm for Ship Trajectory Tracking

This paper presents a weight optimization method for nonlinear model predictive controller (NMPC) based on the genetic algorithm (GA) ship trajectory tracking. The coefficients Q and R of objective function in NMPC are obtained via real-time instead trial error method, which improves efficiency accuracy controller. In addition, targeted improvements made to internal crossover operator, mutation rate, rate algorithm. simulation comparison tracking between with real-time-optimized one constant is performed. Finally, result shows that has better effect than coefficients.