Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking

By C. Ostafew, Angela P. Schoellig, T. Barfoot. 2016

236 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Mechanical Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

Safe Model-based Reinforcement Learning with Stability Guarantees (2017, background): “For example, [23] models uncertain environmental constraints, while [24] uses approximate uncertainty propagation of GP dynamics along trajectories.”
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control (2017, methodology): “Similarly, in [31, 32], the authors used a GP prior to model additive noise and model is improved episodically.”
Cautious Model Predictive Control Using Gaussian Process Regression (2017, methodology): “An application to high-performance driving using a sparse spectrum approximation of the GP was shown in [6] and constrained tracking MPC for robotic applications in [7] and [8], where the uncertainty of the GP prediction is taken into account using confidence bounds.”
Learning-Based Model Predictive Control for Safe Exploration (2018, background): “Safety through probabilistic chance constraints is considered in [11]–[13] based on approximate uncertainty propagation.”
A survey of deep learning techniques for autonomous driving (2019, methodology): “…learn driving models (Lefevre et al., 2015; Lefèvre et al., 2016), driving dynamics for race cars operating at their handling limits (Drews et al., 2017; Rosolia et al., 2017), as well as to improve path tracking accuracy (Brunner, Rosolia, Gonzales, & Borrelli, 2017; Ostafew et al., 2015, 2016).”
Safe Reinforcement Learning Using Robust MPC (2019, background): “Thecombination of learning and control techniques has been proposed in, e.g., [13], [14], [15], [16], [17].”
A Review of Safe Reinforcement Learning: Methods, Theory and Applications (2022, background): “control [17, 132, 139, 183, 204, 231, 258], adaptive control [89, 115, 199, 238], robust control [26, 73, 112, 282], lyapunov function [56, 57] for RL stability and safety.”
Learning-Based Model Predictive Control: Toward Safe Learning in Control (2020, methodology): “…of GP-based MPC to robotic systems have been presented, e.g., for trajectory tracking with robotic manipulators (95), path following of off-road mobile robots (87, 96), autonomous racing (86, 90, 97), process control (98), and telescope systems in the context of periodic additive disturbances (99).”
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning (2021, methodology): “Learning Nonparametric Unknown Dynamics with Machine Learning Models.”