High Level Kinematic and Low Level Nonlinear Dynamic Control of Unmanned Ground Vehicles

Abstract

High level kinematic model-based control of vehicles is an often used technique in the presence of a driver. Existing robust low level linear (speed, steering, brake, suspension etc.) control components are available in cars which can be influenced using the outputs of the kinematic control as reference signals. If problems arise then the driver can modify the internal control based on the visual information of the path and the observed car motion. In case of unmanned ground vehicles (UGVs) this modification is no more evident. In the paper an approach is presented to estimate the errors in real UGV situations where the road-tire contacts generate special sliding effects in behavior of the UGV. These effects are considered as disturbances and are involved in both the kinematic and dynamic models. The novelties of the paper are the consideration of the sliding effects in the kinematic control and the application of sophisticated nonlinear methods for low level dynamic control. It is demonstrated by simulation that high level kinematic control based approaches can cause lateral errors in the order of 1m. In the experiments three types of low level dynamic controls were considered: i) a simplified one using the steering angle of kinematic control, ii) nonlinear input-output linearization (DGA method), and iii) flatness control. They can supply the sliding angle information for the kinematic control.

On Certain Noise Filtering Techniques in Fixed Point Iteration-based Adaptive Control

“…with the initial conditions qo (t 0 ) = 0, qo (t 0 ) = 0, and qo (t 0 ) = 0. This idea was borrowed from [15]. Evidently, at zero frequency it has the transfer function value 1, while for high frequencies it is ∝ s −3 for the variable of the Laplace transform, i.e., it realizes drastic suppression for the high frequency components.…”

“…Normally it is not easy to find appropriate values for Q N oise , and R N oise . The literature generally recommends the use of small (not zero) values the effects of which spread toward ( 13) and (15). For making the most possible correct comparison their values must be experimentally set achieve the best behavior of the Kalman filter.…”

Application of Heavy and Underestimated Dynamic Models in Adaptive Receding Horizon Control Without Constraints

“…Since the so optimized trajectory q o (t) can be quite noisy, according to an idea borrowed from [24], it was smoothed/filtered by a simple low pass filter by tracking it according to the equation…”