Applied Control Systems 1: autonomous cars: Math + PID + MPC

Module 1: Dynamic System Modelling

⏳ 45 minutes

• Deriving transfer functions from first- and second-order physical systems

• Building state-space representations and converting between forms

Module 2: Time- and Frequency-Domain Analysis

⏳ 1 hour

• Step, impulse, and bode plot analysis for system characterization

• Poles, zeros, and stability criteria (Routh, Nyquist, and root locus)

Module 3: PID Control Fundamentals

⏳ 1 hour

• Proportional, integral, and derivative actions—effects on rise time, overshoot, and steady-state error

• Closed-loop tuning methods: Ziegler–Nichols, Cohen–Coon, and manual tuning

Module 4: Advanced PID Implementation

⏳ 45 minutes

• Anti-windup strategies, filter design, and implementation in discrete time

• Handling noise, saturation, and non-ideal actuator dynamics

Module 5: Introduction to Model Predictive Control (MPC)

⏳ 1 hour

• MPC theory: prediction horizon, control horizon, and cost function formulation

• Constraint handling on inputs, states, and outputs

Module 6: MPC Design & Simulation

⏳ 1 hour

• Setting up MPC controllers in MATLAB/Simulink with built-in toolboxes

• Case studies: multivariable process, temperature control, and constrained tracking

Module 7: Robustness & Performance Evaluation

⏳ 45 minutes

• Sensitivity functions, gain and phase margins, and worst-case disturbance rejection

• Comparative analysis: PID vs. MPC in practical scenarios

Module 8: Real-World Applications & Code Deployment

⏳ 45 minutes

• Generating C/C++ code from Simulink for embedded deployment

• Hardware-in-the-loop testing and integration tips

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