T-PFC: A Trajectory-Optimized Perturbation Feedback Control Approach

abstract

• 2019 IEEE. Traditional stochastic optimal control methods that attempt to obtain an optimal feedback policy for nonlinear systems are computationally intractable. In this letter, we derive a decoupling principle between the open-loop plan, and the closed-loop feedback gains, which leads to a deterministic perturbation feedback control based solution to fully observable stochastic optimal control problems, that is near-optimal. Extensive numerical simulations validate the theory, revealing a wide range of applicability, coping with medium levels of noise. The performance is compared against a set of baselines in several difficult robotic planning and control examples that show near identical performance to nonlinear model predictive control while requiring much lesser computational effort.

authors

• Chakravorty, Suman

published proceedings

• IEEE ROBOTICS AND AUTOMATION LETTERS

author list (cited authors)

• Parunandi, K. S., & Chakravorty, S.

citation count

• 3

complete list of authors

• Parunandi, Karthikeya Sharma||Chakravorty, Suman

publication date

• October 2019

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Robotics and Automation Letters  Journal

keywords

• Motion And Path Planning
• Motion Control
• Nonholonomic Motion Planning
• Optimization And Optimal Control

Digital Object Identifier (DOI)

• 10.1109/LRA.2019.2926948

start page

• 3457

end page

• 3464

volume

• 4

issue

• 4

URL

• http://dx.doi.org/10.1109/lra.2019.2926948