non-liner model predictive control for autonomous vehicles
DESCRIPTION
This research concentrates on online trajectory generation of an autonomous vehicle based on model predictive control. Obstacle avoidance and saturation information is directly included in cost function. Simulation results on a fully nonlinear CarSim vehicle model are presented.TRANSCRIPT
Non-linear Model Predictive Control for Autonomous Vehicles
Muhammad Awais Abbas
Supervisor: Prof. Mikael Eklund
Co-Supervisor: Prof. Ruth Milman
Presentation Outline Autonomous Vehicles: Introduction Autonomous Vehicle Framework Vehicle Model Model Validation Gradient Descent Algorithm Cost Function Formulation Simulation Results
Obstacle Avoidance Tests Real-Time Analysis Reference Trajectory Criterion
Conclusion
Autonomous Vehicles: Introduction Capable of navigating on its own.
Human not required for operation of the vehicle.
Types of autonomous vehicles: Aerial Vehicles (UAVs), Ground Vehicles (UGVs) Surface Vehicles (ASVs), Underwater Vehicles (AUV)
Technology of future
Require advanced control systems and sensing
In the U.S. State of Nevada autonomous vehicle can be legally operated on roads.
Autonomous Vehicle Framework Layered Architecture.
Trajectory may need to be replanned. Unknown obstacles, vehicles.
Difference in type of information available to Low Level and Replanning Layer.
Difference in sampling frequencies,Controller intelligence.
Offline Trajectory Generation
Layer 1
Online Trajectory Replanning
Layer 2
Low Level Control Layer 3
Vehicle and Environment
U
y
Model Predictive Control: Introduction Optimal Control
Linear Quadratic Regulator (LQR) Model Predictive Control (MPC)
Ability to look into the future
Advantages Constraints Nonlinear systems Online control solution
Limitations Computation time
PLAN
PLAN
PLAN
DO
DO
DO
t
t
t
Step 1
Step 3
Step 2
t k
2t k
3t k
t k Nk
2t k Nk
3t k Nk
N
MPC Strategy
Cost Function
where , , and are weighing matrices.
1
0
( ) , ,N
N k k kk
J L u
0
Terminal Cost
( ) TNN NQ
Running Cost
, , T T Tk k k k k k k k kQL Su Ru u
k satu u 1
j
ki
P
Vehicle Modeling
[ , , , , ]
( ) [ ]f
t X Y
u t
State vector :
Input vector :
dsfsaasf
f ,
0 0 0 1 0. where
0 0 0 0 1
[ ]
t u t
C C
X Y
ξ t
η ξ t
η
1
.f ,
k k
s k k
t t
T t u t
ξ ξ2. Euler’s Discretization
1. Dynamic Model
Model Validation
20 /xv km h 40 /xv km h
Also known as steepest descent
Well known and simplest method
Finds a local minimum
Plant input is decision variable of algorithm
Gradient Descent Algorithm
Downhill direction
Initial guess
Step Size
.( )J
x
)(xf
( )f m
m
1 ,k k k ku u J x u
Model Predictive Control Setup
MPC Controller
Plant* ( )u t
( )t
( )tOptimizer
Cost Function+
Constraints
Mathematical Model
Simulation Environment
Simulation ResultsObstacle Avoidance 20km/h
Parameter
Value
Sampling Time
Steering Constraint
0.05sT
0Q 0.1,0;0,0.1
0.05
0 5 5
Q
RS
0.1,0;0,0.1
xv 20 /km h
20
Simulation ResultsObstacle Avoidance 60km/h
Parameter
Value
Sampling Time
Steering Constraint
0.05sT
0Q 0.1,0;0,0.1
0.05
0 5 5
Q
RS
0.1,0;0,0.1
xv 60 /km h
5
Simulation ResultsTurning Move With N=20,40,60,80,100
60 left turn at 50
90 right turn at 90
X m
X m
77% decrease in simulation cost
1797% increase in computation cost
Real-Time Analysis Cold start method Warm start
method
, 0,0,0,0...0init ku *, 1init k ku u
Parameter Cold Start
Warm Start
Comparison
Number of iterations for a 20s run 6539 4410 32.5% decrease
Avg. computation time per controller step
0.0393s
0.0275s
30% decrease
Number of 0.05s limit violations 70 38 45% decrease
Reference Trajectory Criteria
`
Start Point
Obstacle
1,k
Goal Point
1,,kd
3,,kd
2,,kd
3,k2,k
`
Start Point 1,k
Goal Point
Ykd ,1,,
Xkd ,1,,
2,k
Xkd ,2,,
Ykd ,2,,
Method 1 Method 2
No goal point information
Goal point information included
,Trajectory error variable k d k k
Reference Trajectory Criteria
Method 1 Method 2
Vehicle reaches goal point
successfully
Vehicle reaches goal point
successfully
Reference Trajectory Criteria
Method 1 Method 2
Vehicle retreat from a large obstacle
Vehicle reaches goal point
successfully
Conclusion Successfully controlled the vehicle dynamics for trajectory generation.
Used fully nonlinear CarSim vehicle model for simulations.
Various types of obstacles simulated.
NMPC controller was able to steer vehicle in an unknown environment with obstacles.
Tuning of weighing matrices time consuming process.
Value of horizon length N should be selected based on the available computation power and required tracking performance.
The controller is real-time implementable at shorter horizon lengths.
Steering constraints need to be tightened with an increase in the speed.
Method 2 for trajectory tracking is found to be superior to Method 1.
Overall, the controller works well in realistic simulations and can be used for practical implementation.
THANK YOU
QUESTIONS?