Intelligent vehicles and road transportation systems (ITS) Week 7 : - - PowerPoint PPT Presentation

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Intelligent vehicles and road transportation systems (ITS)

Week 7 : Vehicle control and ADAS systems

ME470

Denis Gingras January 2015

D Gingras – ME470 IV course CalPoly Week 7

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Course outline

 Week 1 : Introduction to intelligent vehicles, context, applications and

motivations

 Week 2 : Vehicle dynamics and vehicle modelling  Week 3: Positioning and navigation systems and sensors  Week4: Vehicular perception and map building  Week 5 : Multi-sensor data fusion techniques  Week 6 : Object detection, recognition and tracking  Week 7: ADAS systems and vehicular control  Week 8 : VANETS and connected vehicles  Week 9 : Multi-vehicular scenarios and collaborative architectures  Week 10 : The future: toward autonomous vehicles and automated driving

(Final exam)

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 Brainstorming and introduction  Context and ADAS for improved safety  Background on system and control  PID  Fuzzy logic controllers  Controlling the vehicle dynamics  Longitudinal control  Lateral control  Electronic stability control systems  ABS  ADAS: some examples for IVs  High speed ACC  Low speed ACC  Lane keeping  Anti collision braking systems (ACBS)  Parking assist

Week 7 outline

D Gingras – ME470 IV course CalPoly Week 7

4

What is vehicle control?

Brainstorming

Open questions and introductory discussion

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5

What is driver assistance ?

Brainstorming

Open questions and introductory discussion

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6

Enumerate a few driver assistance systems.

Brainstorming

Open questions and introductory discussion

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How would you describe the “driver – vehicle – environment” as a closed control loop system?

Brainstorming

Open questions and introductory discussion

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8

With respect to motion, what are three main types of vehicular control?

Brainstorming

Open questions and introductory discussion

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9

Brainstorming

Open questions and introductory discussion

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Enumerate a few key features of longitudinal collision avoidance systems.

10

Brainstorming

Open questions and introductory discussion

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Enumerate a few key features of lateral collision avoidance systems.

11

What is a PID controller?

Brainstorming

Open questions and introductory discussion

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12

What is actuation? Name a few actuators in cars.

Brainstorming

Open questions and introductory discussion

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13

What is a driver model?

Brainstorming

Open questions and introductory discussion

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14

What would be the differences between a centralized vs a decentralized vehicular control design approach?

Brainstorming

Open questions and introductory discussion

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Source: Yong-Shik Kim et al., « An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment”, Int. Jrnal of Control, Automation, and Systems, vol. 2, no. 3, pp. 310-318, Sept. 2004.

Advanced driver assistance systems (ADAS) aim to increase vehicle safety and alleviate driver workload. ADAS include adaptive cruise control (ACC), lane-keeping support, collision warning and collision avoidance, and assisted lane changes. The effectiveness of these driver assistant systems depends on the interpretation of the information arriving from sensors, which provide details of the surrounding vehicle environment and of the driver-assisted vehicle itself. These systems rely on the detection and subsequent tracking of objects around the vehicle. Such detection information is provided by radar, lidar, and vision sensor. Each ADAS has certain objectives that its controller try to meet. Before a controller can make a decision that enables the driver to feel natural, the motion of the surrounding object must be properly interpreted from the available sensor information.

Introduction

Introduction 19-janv.-15 15 D Gingras – ME470 IV course CalPoly Week 7

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Introduction

Introduction

Total number of road accidents and fatalities per total distance travelled, normalised

• n 1965 data for Europe. Graphic shows when passive and active safety systems

have been introduced, as well as the expected safety potential of ADAS.

Source: O. Gietelink et al., Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations, Tech report 05-009, Delft Center for Systems and Control, Delft Uni of Technology, 2006

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Pre-safe brake from Mercedez

D Gingras - UdeS – IV course CalPoly Week 7

Introduction

Introduction

Some ADAS Requirements

 reliability: must be high for warning systems, extremely high for

automated guidance

 availability: must be available nearly 100% for automated guidance;

lower availability acceptable for warning systems provided a warning is given

 robustness: should operate in most weather conditions, warn and

disable if not operating

 accuracy: absolute accuracy of better than 30 cm needed; no high-

frequency jitter allowed for control applications

 range: rear-end warning requires knowing lane position of leading

vehicle, to approx. 100m

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Introduction

Introduction

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Source: Panagiotis Lytrivis et al., Sensor Data Fusion in Automotive Applications, InTech Open Science Europe, 2009

Introduction

Introduction

Driver assistance features to mitigate collision

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Source: J Becker, Mech Eng. Stanford University

Introduction

Introduction

Driver assistance architecture

Collision Warning Vehicle Mechanization

Source: ACAS Program, final report, executive summary, 1998.

Introduction

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Collision Warning Systems

Introduction

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Trends in control

 Current IV applications are focused on driver assistance rather than

vehicle control; nevertheless, partial and full automation will eventually be important.

 A wide variety of standard and advanced controls techniques are

being applied to road vehicles

 Vehicles to date have been designed for human control, not

automated control. For example, current steering system geometry is designed for “good handling”, i.e. predictable response for humans. The underlying hardware may need to be modified for optimal automatic control.

Introduction

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 Emergency maneuvers: Control systems optimized for smooth performance at

cruise will not work for abrupt maneuvers in emergency situations.

 Equipment failure: Special controllers need to be designed to cope with tire

blowout or loss of power brakes or power steering.

 Heavy vehicles: The load, and the distribution of the load, vary much more

for a heavy truck than for a passenger car. Truck controllers need to be much more adaptable than light vehicle controllers.

 Low speeds: Engine and transmission dynamics are hardest to model at slow

• speeds. Applications such as automated snow plows or semi-automated

busses require careful throttle control design.

 Low-friction surfaces: It is difficult task to predict the effective coefficient of

friction on a particular road surface. This affects not only braking performance but also the design of throttle and steering controllers.

Introduction

Introduction

Challenges for automated control

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Vehicle stability systems

 Introduction

• Braking
• Forces
• Friction

 Electronic Vehicle Stability Systems

• Antilock Braking System (ABS)
• Traction Control System (TCS)
• Electronic Stability Control (ESC, ESP, DSC, VSC, …)

Introduction

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A basic “classical” cruise control system.

Source: Deka J. et al., Study of Effect of P, PI Controllers on Car Cruise Control System and Security, Int. Jrnal of Adv. Res. in EE and Instr. Eng., 2006

Adaptive cruise control

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The basic functioning of an adaptive cruise control system

Source: R Rajamani, “Vehicle Dynamics and Control”, Springer, 2nd Ed, 2012

Adaptive cruise control

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Measuring inter-vehicle distances

Adaptive cruise control

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1 2 1 2 1 2

2 2

( ) ( )

veh veh veh veh veh veh

D x x y y    

1st order linear approximation

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Adaptive Cruise Control flow chart.

Adaptive cruise control

ACC

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Adaptive cruise control

ACC

1 2 2 1 2 2 1 1 2 1 2 1 2 2 1 1 2 1 2

1 1

( ) ( )* ( ) ( ) ( ) ( ) ( )cos tan ( ) ( ) ( ) ( ) ( ) ( ) ( )sin tan ( ) ( )

VV V d Safety veh veh refV veh VV veh veh veh veh refV veh VV veh veh

D t V t t D y t y t x t x t D t x t x t y t y t y t y t D t x t x t

 

                                                      

Safety

D

d

t

2 ( )

V

V t

1 2 ( )

VV

D t

2( )

veh

x t

2 ( )

veh

y t

driver's reaction time, 2nd vehicle current speed inter-vehicular distance safe distance 2nd vehicle x position 2nd vehicle y position

                 

ref

x t Ax t Bu t y t Cx t z t e t y t y t                               

n p n q ref q q q p q p q q

x t x t A B u t y t C I z t z t x t y t C z t

     

                                                        

Equations for PI controller of the Adaptive Cruise Control.

       

x z

x t u t K K z t          

Augmented linear system

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Closed loop scheme with PI and state feedback controllers for Adaptive Cruise Control.

Adaptive cruise control

ACC

tracking error integral

Source: Yong-Shik Kim et al., « An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment”, Int. Jrnal of Control, Automation, and Systems, vol. 2, no. 3, pp. 310-318, Sept. 2004.

Adaptive cruise control

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Tracking maneuvers using an IMM (interactive multiple models) in ACCs.

Low level longitudinal control

Adaptive cruise control

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Driving patterns of vehicles for ACCs

Straight line and curve: In this situation, the ACC vehicle tracks a preceding vehicle that follows straight lines and curves on a curved road. Cut-in and cut-out: The cut-in/cut-out indicates the situation in which a maneuvering vehicle cuts in (or out) to (or from) the lane while the ACC vehicle is tracking other vehicle. In this situation, the tracking of up to three surrounding vehicles is assumed: one in front, one to the left, and one to the right. In this case, the target vehicle changes its motion from a rectilinear motion to a curvilinear motion and then back to a rectilinear motion. U-turn: This situation occurs when the target vehicle changes its driving direction by 180°. The U-turn consists of three routes as follows: The target vehicle moves rectilinearly, undergoes a uniform circular turning of up to 180° with a constant yaw rate, and then converts to a rectilinear motion in the opposite direction. Interchange: When the ACC vehicle is passing through an interchange, the target vehicle usually undergoes a 3-dimensional motion. The target vehicle moves rectilinearly, undergoes a uniform circular turning of up to 270° with a constant yaw rate, and then converts to a rectilinear motion. Often, the model is simplified to consider only a 2-dimensional motion.

Adaptive cruise control

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Source: Yong-Shik Kim et al., « An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment”, Int. Jrnal of Control, Automation, and Systems, vol. 2, no. 3, pp. 310-318, Sept. 2004.

A typical ACC system consists of a driver interface, a radar or lidar sensor, which measures both the distance and speed of preceding vehicles, a controller which controls both throttle and brake actuators, and actuators. The ability to accurately predict the motion

• f preceding vehicles in the ACC environment can improve the

controller’s ability to adapt smoothly to the behavior of those vehicles preceding it. This ability to predict motions is dependent on how well the radar or lidar of an ACC vehicle can track other

• vehicles. In order to track other vehicles using the object

information obtained from multiple sensors, tracking techniques based on the Bayesian approach are usually used.

Adaptive cruise control

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 Predictive Emergency Braking  – Pre-Fill  – Collision Warning  Audible  Brake Jerk  – Brake force increase  – Automatic Emergency Braking  Partial  Automatic Full Braking  reduce consequences of unavoidable collision

Adaptive cruise control

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Lane Keeping Assistance System

 Knowing lane position is necessary for automated guidance and for lane departure warning systems. It is also important for rear-end collision warning, to know which lane your vehicle is in as well as which lane preceeding vehicles are in.  The system controls a steering with sensing lines on a road by a camera, and it assists a driver keeping a vehicle within a lane.  Accidents such as departurefrom a road side are reduced by the system because of reduction of driver’s workload.

Lateral control

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Sensing Lane Position Not always trivial….

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Lateral control

Lateral control

Lane Change Maneuver

30 m 25 m 25 m 30 m 15 m A B C

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Lateral control

Lateral control

Parking Assistance System

 The system controls a steering to assists a driver parking a vehicle into a backward parking space.  Two types of parking are considered (parallel and perpendicular)  Accidents during backward parking are reduced by the system because

• f reduction of driver’s workload.

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Lateral control

Lateral control

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The parallel parking problem: is a motion planning problem in control theory and vehicle dynamics to determine the path a car must take in order to parallel park into a parking space. The front wheels of a car are permitted to turn, but the rear wheels must stay fixed. When a car is initially adjacent to a parking space, to move into the space it would need to move in a direction perpendicular to the allowed path of motion of the rear wheels. The admissible motions of the car in its configuration space are an example of a non- holonomic system.

Adapted from Wikipedia Source: D Gruyer et al., LIVIC, IFSTTAR

D Gingras – ME470 IV course CalPoly Week 7

Parallel parking Assistance System

Lateral control

Lateral control

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Parallel parking Assistance System

Lateral control

1) Sensors detect free spot 2) Sensor measure spot dimensions 3) ADAS or driver bring car forward and compute trajectory 4) ADAS proceeds to maneuver

Lateral control

 Full or partial driving automation will require actuators, i.e.

computer-controlled motors that can move the throttle, brake, and steering.

 The state of the art is rapidly improving: vehicles are available

• n the market with electronic fuel injection, electronic power

steering, and electronic power brakes, all driven by performance and weight improvements for manually-driven cars. This makes it much easier to add computer control.

Actuators

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Actuators

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Stepper motors: digital actuators, moves in fixed increments in both directions. Typically 120 steps. (e.g. Idle speed controller). Solenoids: Digital actuators. Use to extend a plunger or armature to control functions such as vacuum flow in fuel injection or emission-related

• systems. They are controlled 2 ways: pulse width or

duty cycle. Piezo: Piezoelectric actuators convert an electrical signal into a precisely controlled physical displacement (stroke). Automotive is second-largest market for piezoelectric products (e.g. piezo fuel injectors).

Actuators

Vehicle Stability Function – functional overview

 Directional Control  Roll-over Control an electronic control system for a vehicle which improves the dynamic stability of the vehicle.  Vehicle Stability System a function within a vehicle stability system that assists the driver, in the event of under steer and over steer conditions, within the physical limits of the vehicle in maintaining the direction intended by the driver. a function within a vehicle stability system that reacts to an impending roll-over in order to stabilise the vehicle within the physical limits of the vehicle.

Stability control

Stability control

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Stability control

Stability control

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

Electronic stability control (ESC) is a system designed to help drivers to maintain control of their vehicles in situations where the vehicle is beginning to lose control.



Keeping the vehicle on the road prevents run-off-road crashes, which are the conditions that lead to most singlevehicle accidents and rollovers.



The ESC is defined as a system that has the following features:



Helps vehicle directional stability by applying and adjusting individual wheel brakes to help bring the vehicle back to the intended direction.



Uses sensors to determine when the vehicle is not under control.



Uses a steering wheel position sensor to determine the intended direction of the driver.



Operates at all vehicle speeds, except at low speeds where loss

• f control is unlikely.

Stability control

Stability control

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The electronic stability control (ESC) system applies individual wheel brakes to keep the vehicle under control of the driver.

Yaw stability control

Source: R Rajamani, “Vehicle Dynamics and Control”, Springer, 2nd Ed, 2012

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Stability control

Stability control

Stability control

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Wheel speed sensor information is used to monitor if a drive wheel is starting to spin.

Directional stability control process

Vehicle dynamics Steering Drive train/ Brakes Driver Direction control function Sensors Environment

control strategy reference model steering angle controller control error vehicle braking pressures and engine control YRS measured yaw rate vehicle speed band of desired yaw rate

min max

[ , ]

zref zref

 

h



v

z



Control loop Block diagram

 Control algorithm adapted to actual vehicle configuration and wheelbase through end-of-line programming - intervention threshold adapted  Driver selected direction of travel – steering wheel angle sensor  Actual direction of travel – yaw rate sensor  Vehicle speed – ABS wheel speed sensors  Appropriate application of brakes and engine torque reduction when actual yaw rate crosses the intervention threshold

Stability control

Stability control

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Directional control process

45 46 47 48 49 50 51 52 53 54 55

• 0.4
• 0.2

0.2 0.4

Time [s]

Yaw Rate [rad/s] Intervention Threshold Measured Yaw Rate Start of Intervention Stop of Intervention

Stability control

Stability control

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Roll-over control stabilizing interventions

 Rolling-over  The force resulting from the vehicle weight and its centrifugal force at the centre of gravity, acting outside the effective track causes the vehicle to roll-over  Roll-over control intervention  Once the lateral acceleration of the vehicle exceeds the intervention threshold, the vehicle speed is reduced by automatically applying the brakes and reducing the engine power  If wheel ‘lift off’ is detected maximum vehicle speed reduction is requested

Effective track

Stability control

Stability control

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Roll-over control process

 Control algorithm based on lateral acceleration with intervention threshold derived from vehicle type, loading condition, fast/slow steering, travelling uphill/downhill, demanded engine torque by driver, taking into account e.g. frame stiffness, suspension, tires and driver acceptance considerations  Control algorithm adapted to actual vehicle configuration through end-of-line programming - intervention threshold adapted  Control algorithm adapts intervention threshold to actual loading condition of vehicle (centre of gravity height) – vehicle mass estimated from engine torque (engine management system information modified to account for drive-line losses) and vehicle acceleration (driver demand and wheel speed information  Appropriate application of brakes and engine torque reduction when actual lateral acceleration exceeds the intervention threshold

Stability control

Stability control

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54

Roll-over control process

Measured Lateral Acceleration Start of Intervention Stop of Intervention Roll-over Threshold Intervention Threshold 90 95 100 105 110 115

• 7
• 6
• 5
• 4
• 3
• 2
• 1

1 Time [s] Lateral Acceleration [m/s2]

Stability control

Stability control

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Typical hardware – electronic braking system (EBS) (2 axle motor vehicle)

CAN J1939 CAN Brake CAN Sensor Steering angle sensor Yaw rate and lateral acceleration sensor 1 channel pressure control modules 2 channel pressure control module Trailer control module Engine ECU Foot brake module Retarder ECU EBS ECU Wheel speed sensors Stability control

Stability control

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Conclusion

 ADAS allow to avoid collisions and optimize driving.  The integration of the longitudinal control and the lateral control system is becoming mainstream.  Low level control can be implemented using PIDs, Kalman filters and its variants, fuzzy logic or neural network (adaptive control).  In driving automatization, driver models are required for the high level control.  ACCs are the most common longitudinal control system and are merging with forward looking anti-collision systems.  Designing vehicle lateral controllers is very challenging, especially at extreme speeds or trajectories (high steering angle and rates) during evasive maneuvers at which the tire and vehicle dynamics become highly nonlinear.  Drive-by-wire technology replaces the traditional mechanical and hydraulic control systems with electronic control systems. This has greatly facilitated the development of ADAS.

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References

Cho D. et al, Automotive powertrain modelling for control, J. Dynamic Syst., Measur. and Contr.,

• vol. 111, 1989.

Das S D. et al., Design and Implementation of Intelligent Vehicle Control System Based on Camera Sensor, Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA-13), 2013. Deka J. et al., Study of Effect of P, PI Controllers on Car Cruise Control System and Security, Int. Jrnal of Adv. Res. in EE and Instr. Eng., 2006 Emirler M T et al., Robust PID Steering Control in Parameter Space for Highly Automated Driving, International Journal of Vehicular Technology, 2014. Eskandarian Azim (Ed.),Handbook of Intelligent Vehicles, Springer, 2012. Kesting A. et al., Adaptive cruise control design for active congestion avoidance, Elsevier Transportation Research Part C 16, 2008. Kyongsu Y. et al., A Throttle/Brake Control Law for Vehicle Intelligent Cruise Control Seoul 2000 FISITA World Automotive Congress, 2000, Seoul, Korea. Li Li et al., (Ed.), Advanced Motion Control and Sensing for Intelligent Vehicles, Springer 2007.

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References

Siciliano B. et al. (Ed.), Handbook of robotics, Chapter 51, “Intelligent vehicles”, Springer, pp. 1175-1198, 2008 Sivaraj D. et al., Implementation of AVCS using Kalman Filter and PID Controller in Autonomous Self Guided Vehicle, International Journal of Computer Applications Vol. 27– No.2, 2011 Univ of Michigan, Control tutorials for Matlab: Example: Modeling a cruise control system. http://www.engin.umich.edu/group/ctm/examples/cruise/cc.html.

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QUESTIONS?

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