Header menu link for other important links
X
Neural network based hybrid adaptive controller for an autonomously driving car using thin plate spline radial basis activation function
Published in Trans Tech Publications Ltd
2014
Volume: 592-594
   
Pages: 2184 - 2188
Abstract
This paper presents a hybrid lateral and longitudinal controller for a self-driving passenger car. The controller comprises a Proportional Derivative (PD) controller as a closed loop controller and Neural Network (NN) based adaptive compensator as a feed forward controller. The activation function of the NN adaptive stage is based on a poly-harmonic Thin Plate Spline (TPS) Radial Basis Function (RBF), which promises better accuracy, smoother interpolation and closed form solutions. The controller development and testing has been performed using a non-linear vehicle dynamics model, which has been developed using the Matlab/Simulink tool. The Controller performance in terms of vehicle lane following (lateral deviation control) and safe cruising control (longitudinal spacing error control) have been verified through simulations. Reductions of lateral deviation error by 15% and longitudinal spacing error by 7% have been achieved. © (2014) Trans Tech Publications, Switzerland.
About the journal
JournalApplied Mechanics and Materials
PublisherTrans Tech Publications Ltd
ISSN16609336
Open AccessNo
Concepts (15)
  •  related image
    Chemical activation
  •  related image
    Controllers
  •  related image
    Industrial research
  •  related image
    Interpolation
  •  related image
    Longitudinal control
  •  related image
    Neural networks
  •  related image
    Radial basis function networks
  •  related image
    Splines
  •  related image
    Adaptive control
  •  related image
    LANE FOLLOWING
  •  related image
    LATERAL DEVIATION
  •  related image
    NONLINEAR VEHICLE DYNAMICS MODEL
  •  related image
    RADIAL BASIS FUNCTION(RBF)
  •  related image
    SELF DRIVINGS
  •  related image
    Adaptive control systems