4.6 Article

Can Deep Models Help a Robot to Tune Its Controller? A Step Closer to Self-Tuning Model Predictive Controllers

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

Taewon Ahn et al.

Summary: This paper describes an integrated autonomous driving control system for a vehicle with four independent in-wheel motors, which improves vehicle stability and path tracking performance through longitudinal/lateral path tracking and chassis control. Validation through simulation shows significant improvements in path tracking capability and vehicle stability compared to other control systems.

ELECTRONICS (2021)

Article Computer Science, Information Systems

Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking

Alvaro Ortiz et al.

Summary: This study compares two different control strategies and a dynamic trajectory-planning algorithm for achieving optimal outcomes in military missions.

ELECTRONICS (2021)

Article Computer Science, Artificial Intelligence

A constrained instantaneous learning approach for aerial package delivery robots: onboard implementation and experimental results

Mohit Mehndiratta et al.

AUTONOMOUS ROBOTS (2019)

Review Computer Science, Information Systems

Review and Comparison of Path Tracking Based on Model Predictive Control

Guoxing Bai et al.

ELECTRONICS (2019)

Proceedings Paper Automation & Control Systems

Reinforcement Learning and Deep Neural Networks for PI Controller Tuning

William J. Shipman et al.

IFAC PAPERSONLINE (2019)

Article Engineering, Aerospace

Receding horizon control of a 3 DOF helicopter using online estimation of aerodynamic parameters

Mohit Mehndiratta et al.

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING (2018)

Article Computer Science, Artificial Intelligence

Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control

Panagiotis Kofinas et al.

ALGORITHMS (2018)

Review Engineering, Aerospace

Model Predictive Control in Aerospace Systems: Current State and Opportunities

Utku Eren et al.

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS (2017)

Article Automation & Control Systems

Development of a Genetic-Algorithm-Based Nonlinear Model Predictive Control Scheme on Velocity and Steering of Autonomous Vehicles

Xinxin Du et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)

Article Automation & Control Systems

Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System

Erkan Kayacan et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2015)

Article Agriculture, Multidisciplinary

Moving horizon estimation and nonlinear model predictive control for autonomous agricultural vehicles

T. Kraus et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2013)

Article Automation & Control Systems

Model Predictive Control Tuning by Controller Matching

Stefano Di Cairano et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2010)

Article Automation & Control Systems

Heuristic on-line tuning for nonlinear model predictive controllers using fuzzy logic

E Ali

JOURNAL OF PROCESS CONTROL (2003)

Article Automation & Control Systems

On-line tuning strategy for model predictive controllers

A Al-Ghazzawi et al.

JOURNAL OF PROCESS CONTROL (2001)

Article Automation & Control Systems

On-line PID tuning for engine idle-speed control using continuous action reinforcement learning automata

MN Howell et al.

CONTROL ENGINEERING PRACTICE (2000)