Obstacle Avoiding Path Following based on Nonlinear Model Predictive Control using Artificial Variables

IGNACIO SANZEZ; ALEJANDRO HERNAN GONZALEZ; GUILHERME V. RAFFO; ANTONIO FERRAMOSCA; AGUSTINA D'JORGE

Belo Horizonte

Conferencia; 19th International Conference on Advanced Robotics (ICAR 2019); 2019

Universidade Federal de Minas Gerais

This work presents a model predictive formulation for obstacle avoiding path following control for constrainedvehicles. The obstacles are introduced as soft constraints in the value function, in order to maintain the convexity of state and output spaces. In this formulation, the path following and obstacle avoidance tasks may introduce local minima solutions -due to their competing costs- known as corner conditions. In order to address this problem, a heuristic switch in the form of additional decision variables is introduced into the cost function.The proposed solution is based on an extension of Model Predictive Control (MPC) by using Artificial Variables. Anadditional cost term is included in order to prevent early stops in the path following task. Simulations results considering an autonomous vehicle subject to input constraints are carried out to illustrate the performance of the proposed control strategy.