An Iterative LQR Controller for Off-Road and On-Road Vehicles using a Neural Network Dynamics Model

TL;DR

This paper presents an iterative LQR controller that uses a neural network-based dynamic model for trajectory tracking of both off-road and on-road wheeled robots, demonstrating its effectiveness through extensive experiments.

Contribution

It introduces a neural network-based dynamic model integrated with ILQR for trajectory control of diverse wheeled robots, combining model predictive control to handle model inaccuracies.

Findings

Effective trajectory tracking at various speeds.

Neural network models improve control accuracy.

Controller adapts to different robot types.

Abstract

In this work we evaluate Iterative Linear Quadratic Regulator(ILQR) for trajectory tracking of two different kinds of wheeled mobile robots namely Warthog (Fig. 1), an off-road holonomic robot with skid-steering and Polaris GEM e6 [1], a non-holonomic six seater vehicle (Fig. 2). We use multilayer neural network to learn the discrete dynamic model of these robots which is used in ILQR controller to compute the control law. We use model predictive control (MPC) to deal with model imperfections and perform extensive experiments to evaluate the performance of the controller on human driven reference trajectories with vehicle speeds of 3m/s- 4m/s for warthog and 7m/s-10m/s for the Polaris GEM