Obstacle avoiding path following based on nonlinear model predictive control using artificial variables
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Universidade Federal de Minas Gerais
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This work presents a model predictive formulation for obstacle avoiding path following control for constrained vehicles. 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. An additional 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.
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Nonlinear Model , Model Predictive Control , Obstacle Avoidance , Nonlinear Model Predictive Control , Artificial Variables , Cost Function , Autonomous Vehicles , Soft Constraints , Output Space , Input Constraints , Sampling Time , Optimization Problem , Optimal Control , Weight Matrix , Continuous-time , Control Input , Control Problem , Angular Velocity , Penalty Function , Optimal Control Problem , State Constraints , Reference Trajectory , Reference Input , Reference Output , Field Of Robotics , Nonlinear Path , Continuous-time Model , Presence Of Obstacles
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https://ieeexplore.ieee.org/document/8981571