This repository contains a structured portfolio of assignments completed as part of my coursework and self-study in Control Theory, with a focus on real-world applications such as autonomous vehicles and robotic systems.
From modeling differential equations to designing robust controllers and simulating closed-loop behavior, this collection reflects my skills in state-space modeling, control synthesis, observer design, and discrete control — all implemented in Python from scratch, using symbolic and numerical methods.
✅ Convert linear and nonlinear systems into state-space and transfer function representations
✅ Simulate system dynamics using both continuous and discrete-time models
✅ Design stabilizing controllers via pole placement and LQR
✅ Implement observers
✅ Analyze stability via eigenvalues, Bode plots, and step responses
✅ Derive dynamic models using Lagrangian mechanics
✅ Integrate control strategies in both linearized and nonlinear systems
✅ Build digital controllers for systems that work in discrete time
Assignment 1 – State-Space Modeling & Simulation (Notebook)
- Convert high-order ODEs into state-space form
- Derive transfer functions from symbolic equations
- Implement numerical integration (Euler method)
- Analyze stability of autonomous systems
- Simulate step responses and convergence behavior
Assignment 2 – Linear Systems Control Design (Notebook)
- Derive transfer function from state-space system
- Design linear feedback controller via:
- Pole placement
- Linear Quadratic Regulator (LQR) with cost function
- Perform eigenvalue analysis of closed-loop dynamics
- Compute gain/phase margins using Bode plots
- Discretize system with fixed time step
- Design and simulate discrete-time controller
Assignment 3 – Nonlinear Cart-Pole Stabilization (Notebook)
- Derive full nonlinear model of the cart-pole using Lagrangian mechanics
- Linearize dynamics around the upright position
- Design stabilizing controller for linearized and nonlinear system
- Implement state observer and simulate estimation vs actual state
- Simulate full nonlinear system with observer-based feedback
- Discretize and control the system using discrete LQR
- Python (Jupyter Notebook)
numpy,sympy,matplotlib,scipy- Manual symbolic derivation of dynamics and controllers
- Plots for visualizing system states, error, and control input
Valeria Neganova
Bachelor Robotics Student
Focus: Low-level control design, feedback systems, modeling for autonomous and robotic platforms
📫 Valerochka.neganova@mail.ru