Fixed-time lyapunov-based model predictive control for path following and obstacle avoidance in unmanned ground...

This paper investigates the path following problem for unmanned ground vehicle (UGV) formation subject to control input and obstacle constraints. A fixed-time Lyapunov-based model predictive control (FLMPC) framework is proposed for multi-vehicle formation tracking and obstacle avoidance. By integrating fixed-time stability theory into the Lyapunov-based model predictive control (LMPC) architecture, the approach incorporates a Lyapunov function derivative constraint and introduces a control input tracking term into the cost function. This design endows the closed-loop system with fixed-time stability while inheriting the rapid convergence property of fixed-time control. To address collision avoidance in mixed scenarios with sudden and static obstacles, a time-triggered avoidance function and a dynamic weighting factor are designed. The resulting scheme enables proactive obstacle avoidance and smooth formation recovery. Moreover, energy-efficient control input design is explicitly embedded in the optimization process, leading to effectively reduced overall energy consumption. Simulation results validate the superiority of the proposed method in terms of convergence speed, obstacle avoidance performance and energy efficiency.