1. RC Car Thomas Chau, Ben Sack, Peter Tsonev

2. Overview Goal: to build a smart RC car that corrects itself using sensors. Objective: testing our run at high speed towards an object and halt before crashing.

3. Architecture and Design NIOS system. PWM component to interface Altera with Car Ultrasonic Sensor component with interrupts. Software component – feedback loop integrating sensor readings and outputting to PWM. Additional servo to rotate sensor 90 degrees.

4. Algorithm D = distance read from sensor (inches)‏ S = speed calculated from D and previous D (inches per second)‏ While (D is not target D)‏  Read D. Let S = (D' – D) * dt  Let new speed = function (D, S)‏  Let PWM level = normalization (speed) => [6% to 9%]  Write PWM level to register.

5. PID Equations Two equations; two degrees of freedom Gain equation tries to get car as close to target as possible. Differentiator equation opposes the first equation if the speed of approach is too high. The balance of the two equations brings the car to target.

6. Implementation Engineering a good feedback loop takes a great deal of experimentation. Precise distance measurements are tough; precise speed measurements are even harder.

7. Implementation Cont'd PID theory is for linear behavior; however, the physical system of the car and especially the throttle control is highly nonlinear. Our task is critical damping. The PID equations work best for under-damping. Solution: introduce a nonlinearity in the equations; the differentiator is also a measure of distance to target. (Smaller distance -> more reverse throttle)‏

8. Results Engine lost reverse throttle capability. The following graphs show our measurements while the engine was still performing. Graphs: Overdamping, Critical Damping, Dirty Measurements

11. Results Graph

13. New Demo instead of Throttle Demo Uses PID concept except with steering rather than braking. New challenges: sensor reads wall at a bad incident angle. Nonlinear throttle affects turning rate. Poor sensor resolution requires larger distances.

14. Difficulties The car exploded Physical difficulties: measurement; figuring out parameters for feedback equations; hacking the hardware; fundamental nonlinearities. Physical limitations; ultrasonic sensor updating every 50ms with 1” granularity. Car engine with very rough speed control. Unpredictable battery conditions.

15. Lessons Learned Be careful not to jerk the PWM levels, damaging transistors. Wiring is too low-level; it complicates debugging and increases the development time. Data filtering for dirty measurement data; unforeseen sources of interference (ethernet, battery, servos, engines, etc.)‏

16. Thanks! Peter cuts a breadboard down to size.