2 research outputs found
A Data Driven Method of Feedforward Compensator Optimization for Autonomous Vehicle Control
A reliable controller is critical for execution of safe and smooth maneuvers
of an autonomous vehicle. The controller must be robust to external
disturbances, such as road surface, weather, wind conditions, and so on. It
also needs to deal with internal variations of vehicle sub-systems, including
powertrain inefficiency, measurement errors, time delay, etc. These factors
introduce issues in controller performance. In this paper, a feed-forward
compensator is designed via a data-driven method to model and optimize the
controller performance. Principal Component Analysis (PCA) is applied for
extracting influential features, after which a Time Delay Neural Network is
adopted to predict control errors over a future time horizon. Based on the
predicted error, a feedforward compensator is then designed to improve control
performance. Simulation results in different scenarios show that, with the help
of with the proposed feedforward compensator, the maximum path tracking error
and the steering wheel angle oscillation are improved by 44.4% and 26.7%,
respectively.Comment: Published at the 30th IEEE Intelligent Vehicles Symposium, 2019.
arXiv admin note: substantial text overlap with arXiv:1901.1121
A Data Driven Method of Optimizing Feedforward Compensator for Autonomous Vehicle
A reliable controller is critical and essential for the execution of safe and
smooth maneuvers of an autonomous vehicle.The controller must be robust to
external disturbances, such as road surface, weather, and wind conditions, and
so on.It also needs to deal with the internal parametric variations of vehicle
sub-systems, including power-train efficiency, measurement errors, time
delay,so on.Moreover, as in most production vehicles, the low-control commands
for the engine, brake, and steering systems are delivered through separate
electronic control units.These aforementioned factors introduce opaque and
ineffectiveness issues in controller performance.In this paper, we design a
feed-forward compensate process via a data-driven method to model and further
optimize the controller performance.We apply the principal component analysis
to the extraction of most influential features.Subsequently,we adopt a time
delay neural network and include the accuracy of the predicted error in a
future time horizon.Utilizing the predicted error,we then design a feed-forward
compensate process to improve the control performance.Finally,we demonstrate
the effectiveness of the proposed feed-forward compensate process in simulation
scenarios.Comment: This paper have been submitted to the 2019 IEEE Intelligent Vehicle
Symposiu