Tracking-error model-based predictive control for mobile robots in real time

By G. Klančar, Igor Skrjanc. Robotics Auton. Syst., 2007

433 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/62990d022b19aa0f05fef69f959dadb8aa23ce8b

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

1. C. Ostafew
2. Angela P. Schoellig
3. T. Barfoot
4. Melanie Nicole Zeilinger
5. Lukas Hewing
6. Marc Peter Deisenroth
7. Sanket Kamthe

In Control and Systems Engineering

1. Angela P. Schoellig
2. Chun-Yi Su
3. Renquan Lu
4. C. Ostafew
5. Zhijun Li
6. T. Barfoot
7. Melanie Nicole Zeilinger
8. Lukas Hewing

Citations in the corpus

• Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach (2016, methodology): “In [14], tracking control of a mobile robot is achieved using MPC, and in [15] tracking-error kinematic model for mobile robots is linearized as well as analyzed at the equilibrium point and transferred to an exact discrete form including the time delay.”
• Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking (2016, background): “It has been proposed for mobile robots (Howard et al., 2009; Klančar and Škrjanc, 2007; Krenn et al., 2013; Kühne et al., 2005; Peters and Iagnemma, 2008), unmanned aerial vehicles (Kumar and Michael, 2012; Mueller and D’Andrea, 2013), and humanoid robots (Erez et al., 2013; Gouaillier et al.,…”