Nonlinear Model Predictive Path Following Controller with Obstacle Avoidance

In the control systems community, path-following refers to the problem of tracking an output reference curve. This work presents a novel model predictive path-following control formulation for nonlinear systems with constraints, extended with an obstacle avoidance strategy. The method proposed in th...

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Published inJournal of intelligent & robotic systems Vol. 102; no. 1; p. 16
Main Authors Sánchez, Ignacio, D’Jorge, Agustina, Raffo, Guilherme V., González, Alejandro H., Ferramosca, Antonio
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.05.2021
Springer Nature B.V
Subjects
Artificial Intelligence
Control
Control systems
Convexity
Electrical Engineering
Engineering
Mechanical Engineering
Mechatronics
Model testing
Nonlinear control
Nonlinear systems
Obstacle avoidance
Optimization
Predictive control
Regular Paper
Robotics
Topical collection on ICAR 2019 Special Issue
Trajectory planning
Model predictive control
Obstacle avoidance
Path-following
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ISSN0921-0296
1573-0409
DOI10.1007/s10846-021-01373-7

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Summary:In the control systems community, path-following refers to the problem of tracking an output reference curve. This work presents a novel model predictive path-following control formulation for nonlinear systems with constraints, extended with an obstacle avoidance strategy. The method proposed in this work simultaneously provides an optimizing solution for both, path-following and obstacle avoidance tasks in a single optimization problem, using Nonlinear Model Predictive Control (NMPC). The main idea consists in extending the existing NMPC controllers by the introduction of an additional auxiliary trajectory that maintains the feasibility of the successive optimization problems even when the reference curve is unfeasible, possibly discontinuous, relaxing assumptions required in previous works. The obstacle avoidance is fulfilled by introducing additional terms in the value functional, rather than imposing state space constraints, with the aim of maintaining the convexity of the state and output spaces. Simulations results considering an autonomous vehicle subject to input and state constraints are carried out to illustrate the performance of the proposed control strategy.
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ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-021-01373-7