![PID CONTROL FOR ACTIVE
SUSPENSION IN AUTONOMOUS VEHICLES
TEAM MEMBERS
ANUP KADAM[27]
1.
ANANT KHOT[32]
2.
RUTUJA MANE[38]
3.
NAGRAJ RAJAM[40]
4.
DIVISION:IC-A BATCH -2 GROUP - 2](https://image.slidesharecdn.com/addalittlebitofbodtext-250422085034-06c21e55/85/PID-Control-for-active-Susupension-in-Autonomous-vehicles-1-320.jpg)
![FUZZY LOGIC CONTROL [FLC]
Definition: Fuzzy logic extends classical binary logic by
allowing for more than just true or false values. It deals with
degrees of truth or membership in a set.
Membership Functions: In fuzzy logic, variables can have a
degree of membership in a set, represented by membership
functions. These functions map input values to a range
between 0 and 1, indicating how much the input belongs to a
fuzzy set.](https://image.slidesharecdn.com/addalittlebitofbodtext-250422085034-06c21e55/85/PID-Control-for-active-Susupension-in-Autonomous-vehicles-17-320.jpg)
PID Control for active Susupension in Autonomous vehicles
- 1. PID CONTROL FOR ACTIVE SUSPENSION IN AUTONOMOUS VEHICLES TEAM MEMBERS ANUP KADAM[27] 1. ANANT KHOT[32] 2. RUTUJA MANE[38] 3. NAGRAJ RAJAM[40] 4. DIVISION:IC-A BATCH -2 GROUP - 2
- 2. CONTENT Overview Active Suspension Systems Overview of PID CONTROL System modeling PID Controller Design objectives of the active suspension system Dynamic Equations Of quater car model Stability Analysis Applications FLC ChatGpt Conclusion References
- 3. An active suspension system is a type of automotive suspension system that uses actuators to control the vertical movement of the vehicle's wheels, enhancing ride comfort and handling. Traditionally automotive suspension designs have been a compromise between the three conflicting criteria of road holding, load carrying and passenger comfort. An active suspension system has the ability to store, dissipate and to introduce energy to the system. Active Suspension Systems
- 4. Overview of Active Suspension Systems Benifits Improved Ride Comfort Enhanced Handling Adaptive Response
- 5. Proportional (P): Determines the corrective action based on the current error. Integral (I): Accumulates past errors to eliminate steady-state errors. Derivative (D): Predicts future errors based on the rate of change, providing a damping effect Overview of PID Control Why PID Control simplicity 1. robustness 2. effectiveness 3. precise adjustments 4. continuously calculating an error value 5.
- 6. System Modeling Quarter-Car Model full-car model Assumptions: The vehicle is modeled as a two-mass system: one mass representing the wheel and suspension assembly, and the other representing the vehicle body (sprung mass)
- 7. Equations of Motion Let ‘ms’ be the sprung mass (vehicle body) and ‘mu’the unsprung mass (wheel and suspension). The forces acting on the system include the spring force, damper force, and the force from the road disturbance ‘zr(t)’.
- 8. The equations of motion for the sprung and unsprung masses are: where: ‘zs ’ and ‘zu’are the displacements of the sprung and unsprung masses. ‘c’ is the damping coefficient of the damper. ‘k’ is the spring constant. ‘kt’is the tire stiffness. ‘Fact’is the force applied by the actuator.
- 9. Rearrange the Laplace-transformed equations to solve for Y(s) in terms of U(s). The transfer function G(s) of the system is then given by: G(s) will depend on the parameters of the system (masses, spring constants, damping coefficients)
- 10. PID Controller Design: Model Includes:- ms : Sprung mass (mass of the vehicle body supported by the suspension). mu : Unsprung mass (mass of the wheel and axle). ks : Spring constant of the suspension. kt : Tire stiffness. cs : Damping coefficient of the suspension. u(t): Control force applied by the active suspension system. objective: The primary goal of the PID controller is to minimize the displacement of the sprung mass xs(t) in response to road disturbances r(t).
- 11. PID Controller Structure : Feedback Control Loop: eq 1
- 12. PID Control Action: Proportional Control(Kp): Integral Control(ki): Derivative Control(Kd): Eliminate Stady Error predicts future behaviour of error Provides Control Action Final Control Action signal:
- 13. How PID works in Active Suspension
- 14. Stability Analysis: ROOT LOCUS & BODE PLOT ANALYSIS Graphical method for examining how the roots of a system's characteristic equation change as a system parameter (typically the gain) varies. library of root locus : rlocus 1+ C(s).G(S)=0 Characteristic equation of closed-loop transfer function final charachteristic eq library for bode plot : bode from eq 1
- 15. Stability Analysis: Root Locus Analysis Bode plot
- 16. Applications Tesla Model S (Smart Air Suspension): Combines efficiency with luxury, offering smart location-based adjustments. Mercedes-Benz S-Class (Magic Body Control): Pioneering technology with predictive road scanning and curve tilting for a supremely smooth ride. Range Rover (Adaptive Dynamics): Emphasizes off- road capability alongside luxurious ride quality with adaptive terrain response. BMW 7 Series (Dynamic Drive): Balances dynamic performance with comfort, offering active roll stabilization for sharp handling.
- 17. FUZZY LOGIC CONTROL [FLC] Definition: Fuzzy logic extends classical binary logic by allowing for more than just true or false values. It deals with degrees of truth or membership in a set. Membership Functions: In fuzzy logic, variables can have a degree of membership in a set, represented by membership functions. These functions map input values to a range between 0 and 1, indicating how much the input belongs to a fuzzy set.
- 18. Workflow of Fuzzy Logic Control 1.Input Variables 2. Fuzzification 3. Rule Base 4. Inference Engine 5. Aggregation 6. Defuzzification 7. Output 8. System Response
- 19. CONTROL OF AUTONOMOUS VEHICLE USING FLC
- 20. COMPARISON B/W FLC AND PID
- 21. REFERENCES https://www.igi-global.com/chapter/active-suspension-control-of-full-car-model-using-bat- optimized-pid-controller/318602 https://www.researchgate.net/publication/3454431_Using_Fuzzy_Logic_in_Automated_Vehicl e_Control Shafiei, B. A Review on PID Control System Simulation of the Active Suspension System of a Quarter Car Model While Hitting Road Bumps. J. Inst. Eng. India Ser. C 103, 1001–1011 (2022). https://doi.org/10.1007/s40032-022-00821-z https://old.edouniversity.edu.ng/oerrepository/articles/modeling_design_and_simulation_of_active _suspension_system_pid_controller_using_automated_tuning_technique.pdf https://chatgpt.com/share/ee8a63a4-5b62-4887-b7bd-dafcde312839 https://cecas.clemson.edu/cvel/auto/systems/active_suspension.html https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi? article=1035&context=me_fac_articles#:~:text=Fuzzy%20logic%20controllers%20for%20autonomo us,%2C%202005)%2C%20x%20tracking%20moving https://www.researchgate.net/publication/288259404_GA- Based_PID_and_Fuzzy_logic_control_for_active_vehicle_suspension_system
- 22. THANK YOU!