Embedded Control Systems

Adaptive Cruise Control and Automated Steering

This project utilized MATLAB, Simulink and Stateflow to implement a simple vehicle with adaptive cruise control and an automatic steering controller. The vehicle was operated as stable as possible along a predetermined coordinate path via the NXP and Haptic Wheel Interface. The vehicle was implemented following a bicycle model to simplify the differential equations in the vehicle model.

• Two proportional plus derivative (PD) controllers to autonomously steer the vehicle
• CAN messaging to interact with other vehicles on the same path
• Interface with haptic wheel and other hardware which sets speed, cruise control, and throttle by developing code to handle GPIO, ADC, and PWM signals.
• Final report

Haptic Wheel

Skills and Learnings:

• Use of a NXP S32K144 microprocessor
• Sampling, Nyquist frequency,
  • Beats, aliasing
• Laplace transforms, Fourier transforms
• Pulse Width Modulation (PWM)
• Frequency response of PWM signals
  • Bode plots, RC filters
  • Transfer functions
• Second-order systems
  • Continuous vs Discrete time approximation
  • Characteristic poles
  • Numerical stability
• Interrupts, semaphores
  • RMS Schedulability
• Virtual wheel, virtual spring, virtual damping

Smart Highway

The design problem was to control the speed of a vehicle to track another vehicle from a distance that depends on speed. We wanted to string several of these vehicles, one behind the other. We first created a dynamic model using Simulink. Our final controller utilized PI control to maintain the desired distance away from the car in from of it.

Lead train with a small sinusoidal variation in speed, with 5 following.

Project Paper

Skills and Learnings:

• PID Control
• Bode plots and root locus to assess the single-car system performance
• Simulink modeling
• Nyquist plots to analyze stability
• Matlab scripting
• Constructing and analyzing filters