Slip Based Traction Control System Design and Validation Using Co-Simulation between ADAMS and MATLAB/SIMULINK

Slip Based Traction Control System Design and Validation Using Co-Simulation between ADAMS and MATLAB/SIMULINK BySUDIPTA SAHA Roll No. 191325009 Registration No. 235513015

Under the Guidance of Dr. Suman Saha CSIR-CMERI, Durgapur & H.P.Ikkurti CSIR-CMERI, Durgapur

School of Mechatronics & RoboticsIndian Institute of Engineering Science and Technology, ShibpurHowrah 711103, West Bengal

Central Mechanical Engineering Research Institute, (CMERI), Durgapur- 713 209IndiaMay 2015

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Objective The main objectives are :-To develop a new scheme to enhance vehicle longitudinal stability with a traction control system as road parameters changes during driving.To control wheel slip during road changes by varying the slip ratio as a function of the slip angle. To build a plant and a controller model using kinematic and dynamic equations in MATLAB/SIMULINK and design a robust control law using sliding mode control technique and test its performance.For validation of the controller requires a vehicle model, which is to be designed using CATIA and then model export to ADAMS.To create wheel velocity and chassis velocity as output and control torque as input in ADAMS Plant model.To perform Co-Simulation between ADAMS Plant and controller block in MATLAB/SIMULINK so that simulation results can verify that the proposed scheme is robust and superior to the open loop control.

Outline of this PresentationTraction Control System (TCS)Mathematical RepresentationController DesignVehicle Design and ADAMS InterfaceCo-SimulationResults & DiscussionsConclusionFuture Scope of workReferences

Traction Control System (TCS)Traction control system is a method of preventing wheels from spinning when traction is applied by limiting the amount of power supplied to the wheel.Demonstration of TCS:-

Mathematical Representation

Road ConditionsC1C2C3Asphalt Dry1.280123.990.52Asphalt Wet0.85733.8220.347Concrete Dry1.197325.1680.5373Cobblestone Dry1.37136.45650.6691Cobblestone Wet0.400433.7080.1204Snow0.194694.1290.0646Ice0.05306.390

Serial NoRoad and Pavement conditionCroll1Very good Asphalt0.01-0.01252Very good Concrete0.008-0.13Poor asphalt0.234Very good stone paving0.033-0.0555Poor stone paving0.0856Snow0.0257Ice0.037

Controller DesignModel Simplification:- As the motion needs to restrict forlongitudinal direction only dynamic equations reduces to

Sliding Mode controlSliding mode control, orSMC, is anonlinear controlmethod that alters the dynamicsof anonlinear systemby application of adiscontinuous control signal that forces the system to slide along the boundaries of the control structures. The motion of the system as it slides along these boundaries is called asliding mode and the geometricallocusconsisting of the boundaries is called thesliding (hyper) surface.

Reference slip Calculation:- Asphalt Wet = 0.175,Asphalt Wet =0.1326,Concrete Dry=0.1705,Cobblestone Dry =0.4002,Cobblestone Wet =0.1415,Snow =0.0595,Ice =0.0398.

Sliding Mode Control with Integral Action:-

Sliding Surface design:-

Formulation of Control Law:-

Chattering :-When the system is implemented in a digital controller, due toswitching of control law at infinite frequency, actuator cantcope up with it. So the trajectories will chatter across e = 0resulting in high control effort and oscillations in the systemresponse.

Vehicle Design and ADAMS InterfaceCAD Model Using CATIA:-Parts (Wheel, Chassis) & Assembly DesignCreating Joints Applying MaterialsRoad Design

CATIA model export to Adams:-Using Sim Designer extension in CATIA V5 Cad model is converted in Windows Command Script (.cmd) file extension file which can be opened in Adams/View.Importing in Adams (1, 2)

ADAMS Interface:-Modifying Revolute JointsDummy CreationCreating Translational Joints (1, 2)Defining torqueContact Surface design (1, 2, 3)Vehicle Model:-

Co-SimulationCo-simulation is the process of simulating a system where two or moreseparate simulation programs are simultaneously used to model variousaspects of the system and these simulation environments communicateduring run-time, to simulate the whole system, thus affecting each othersoutput. In this case the vehicle is modeled in Adams-View whereas theController is modeled in SIMULINK and a co-simulation is setup to run thevehicle model in Adams using the Controller model in SIMULINK.

Design Process using ADAMS Controls:-

Selection of Input and Output Variables:-

Variable NameDescriptionTorque_1Rear left control torqueTorque_2Rear right control torqueTorque_3Front left control torqueTorque_4Front right control torque

Variable NameDescriptionVel_1Rear left angular velocityVel_2Rear right angular velocityVel_3Front left angular velocityVel_4Front right angular velocitychesisChassis velocity

Steps of Co-Simulation:-

Load Adams/Controls (1)Selecting Input and Output VariablesSet References for Input Variables Adams Block Exporting to MATLAB/SIMULINKLinking Adams Plant model and the Controller model in SIMULINK (1, 2, 3)Starting Co-simulationChecklist before Co-Simulation.

Results & DiscussionsFour sample sets is used in simulation Asphalt Dry-Snow-Concrete DryCobblestone Dry-Ice-Cobblestone Wet Asphalt Wet-Concrete Dry-IceCobblestone Wet-Cobblestone Dry-Snow.Simulation time is set to 10 (s) in all. There are three road conditionsincluded into each of sample sets. For first duration of time is from 0 (s) to3 (s) and second duration from 3 (s) to 6 (s), third duration of time is 6 (s)to 10 (s.To prove the robustness to the parameter uncertainty, mass of the vehicleare being changed simultaneously. For each set of road condition mass ofthe vehicle varies as m=450 (kg), m=600(kg), m=800(kg), m=1050(kg).

In above of every figure name of set and mass are indicated, for exampleAsphalt Dry-Snow-Concrete Dry written as Ash D- Snow-Con D-600.

ValidationChassis Velocity with Control & without Control:-

Wheel Velocity with control & without control:-

Animation

Reference slip tracking in Co-Simulation

ConclusionAsphalt wet, Ice, Snowy roads are the most critical conditions identified and controlled at closed loop control, in which open loop control fails to show the desired result.In case of closed loop control after 4(s) Slip based traction controller follows the reference slip in a 10(s) co-simulation between ADAMS and MATLAB/SIMULINK.In the Simulation during 0(s) to 3(s) peak value of slip=1 occurs because of the high value of starting torque given by the controller and oscillations occur as the chosen values of integral gain and design parameter are not optimized.The control focuses on maximizing the driving force by setting the optimal value of slip ratio within the specified variation in mass and road conditions. Validation leads to development of four wheeler in-hub electric vehicle using CATIA and transportation to ADAMS. A road model has been designed in ADAMS constructed of three different friction coefficients and co-simulation have been achieved between ADAMS/View and MATLAB/Simulink to test the robustness of the controller.

Future scope of workThe scope of work will be to maintain the initial reference torque well within the motor maximum torque limit by controlling the starting torque to avoid initial jerk. To find a optimal value of integral gain and design parameter for avoiding oscillations.To attain a desired performance other control strategies can be applied e.g. PI, PID , Sliding mode with fuzzy logic etc. To get a complete control of electric vehicle, in the controller design yaw control for lateral stability should also be taken into account.

References[1] Y. Hori, Y. Toyoda, Tsuruoka Y., Traction control of electric vehicle based on the estimation of road surface condition-basic experimental results using the test EV UOT Electric March, Power Conversion Conference - Nagaoka 1997.[2] T.A. Johansen, I. Peterson, J. Kalkkuhl, J. Ludemann Gain-scheduled Wheel Slip Control in Automotive Brake Systems, Control Systems Technology, IEEE Transactions on, Volume: 11,Issue: 6, Nov. 2003.[3] W. E. Ting, J. S. Lin Nonlinear back stepping design of anti-lock braking systems with assistance of active suspensions, Proceedings of the 16th IFAC World Congress, 2005[4] I. Petersen, T. A. Johansen, J. Kalkkuhl, and J. Ludemann Wheel slip control using gain-scheduled lq-lpv/lmi analysis and experimental results, in Proc. European Control Conference, Cambridge, United Kingdom, 2003[5] O. T.C. Nyandoro, J. O. Pedro, B. Dwolatzky, and O. A. Dahunsi State Feedback Based Linear Slip Control Formulation for Vehicular Antilock Braking System, Proceedings of the World Congress on Engineering 2011 Vol I, WCE 2011[6] P. Ratiroch-Anant, H. Hirata, M. Anabuki, S. Ouchi Adaptive Controller Design for Anti-Slip System of EV, Conference on Robotics, Automation and Mechatronics, 2006.[7] J. Yi, L. Alvarez, X. Claeys, R. Horowitz, Emergency Braking Control with an Observer-based Dynamic Tire/Road Friction Model and Wheel Angular Velocity Measurement, Vehicle System Dynamics, Vol. 39, No. 2, 2003.[8] S.Li, T.Kawabe,Slip Suppression of Electric Vehicles Using Sliding Mode Control Method , Intelligent Control and Automation, 2013, 4, 327-334[9] Hans-Christian ,Becker Jensen Design of Slip-based Active Braking and Traction Control System for the Electric Vehicle QBEAK, 2012[10] CATIA V5 Part design, Tutorials MSC Corporation.[11] Getting Started with Adams Control,[12] S .J.Rao, Vehicle Modeling and ADAMS-Simulink Co-Simulation With Intregated Continuously Controlled Electronic Suspension (CES) and Electronic Stability Control (ESC) Models, The Ohio State University 2009

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