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Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization by Cheng, Zilong; Li, Yulin; Chen, Kai; Ma, Jun; Lee, Tong Heng is a scholarly article available to read on EtoBox.

What is Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization about?

Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an accurate system model to update the optimal control actions and the trajectory determined with forward integration, thus becoming vulnerable to inevitable model inaccuracies. Recently, substantial research efforts in learning-based methods for optimal control problems have been progressing significantly in addressing unknown system models, particularly when the system has complex interactions with the environment. Yet a deep neural network is normally required to fit substantial scale of sampling data. In this work, we present Neural-iLQR, a learning-aided shooting method over the unconstrained control space, in which a neural network with a simple structure is used to represent the local system model. In this framework, the trajectory optimization task is achieved with simultaneous refinement of the optimal policy and the neural network iteratively, without relying on the prior knowledge of the system model. Through comprehensive evaluations on two illustrative control

Author

Cheng, Zilong; Li, Yulin; Chen, Kai; Ma, Jun; Lee, Tong Heng

Published

2020

Language

EN