State-Space representation
variables of a dynamic system that describe the whole system and its response to an input
Linear State-Space representation
State equation:
n first order linear differential equations
r inputs
Output equation:
m outputs
Obtaining State-Space Matrices
no two distinct states may have the same output
states are indistinguishable if they produce the same output for different inputs
Observability is a measure of how well the internal states of a system can be inferred by knowledge of outputs
Vehicle model types
Kinematic
No forces or torques
No complex parameters
large estimation error
Dynamic
better description
requires knowledge on tire-road interaction
requires knowledge of parameters
Vehicle Models
Single track
describe dynamics without vertical and roll motions
Two track
includes longitudinal, lateral and yaw motions
Quarter Car
considers sprung and unsprung mass
SDS
Tire Model
relationship between tire forces and moments with slip ratio/slip angle
Tyre Model Graph
Observer based estimation methods
Open Loop Observer
Luenberger observer
Sliding-mode observer
Kalman filter
Properties Open Loop Observer
measured data is fed into mathematical model creating estimates
problems with stability
Properties Luenberger Observer
deterministic, closed loop
uses error between predicted and measured state
simple design
sensitive to changes
Properties Sliding-Mode observer
uses sign function
more robust
chattering effect
Properties Kalman filter
not deterministic
considers noise
Sideslip angle
angle between the vehicle’s center plane and the velocity vector
Methods to improve sideslip angle estimation
R and Q tuning
More accurate models
Additional „measurements“
process of obtaining state space matrices from differential equations
DGLs -> linearization -> get matrices
standard tire models
Linear region
Transitional region
Frictional (lower forces)
Over slip, forces
Last changeda year ago