Optimal Trajectory Planning and Model Predictive Control of Underactuated Marine Surface Vessels using a Flatness-Based Approach

TL;DR

This paper presents a flatness-based approach for optimal trajectory planning and model predictive control of underactuated marine vessels, effectively handling obstacles, constraints, and disturbances in simulation.

Contribution

It introduces a novel flatness-based method to generate initial guesses and optimize trajectories for underactuated vessels, enhancing real-time control and obstacle avoidance capabilities.

Findings

Successful energy-optimal trajectory planning demonstrated in simulations.

Effective obstacle avoidance with static and dynamic obstacles.

Real-time model predictive control extended with flatness-based methods.

Abstract

This paper demonstrates a refined approach to solving dynamic optimization problems for underactuated marine surface vessels. To this end the differential flatness of a mathematical model assuming full actuation is exploited to derive an efficient representation of a finite dimensional nonlinear programming problem, which in turn is constrained to apply to the underactuated case. It is illustrated how the properties of the flat output can be employed for the generation of an initial guess to be used in the optimization algorithm in the presence of static and dynamic obstacles. As an example energy optimal point to point trajectory planning for a nonlinear 3 degrees of freedom dynamic model of an underactuated surface vessel is undertaken. Input constraints, both in rate and magnitude as well as state constraints due to convex and non-convex obstacles in the area of operation are considered and simulation results for a challenging scenario are reported. Furthermore, an extension to a trajectory tracking controller using model predictive control is made where the benefits of the flatness based direct method allow to introduce nonuniform sample times that help to realize long prediction horizons while maintaining short term accuracy and real time capability. This is also verified in simulation where additional disturbances in the form of environmental disturbances, dynamic obstacles and parameter mismatch are introduced.