Control Systems Made Simple | Beginner's Guide

Learn how system modeling uses mathematical equations to represent real components, enabling simulation and control. Explore a vehicle suspension example with mass, springs, dampers, and MATLAB models.

This lecture explains modeling basic mechanical elements: mass, spring, and damper, using Newton's law and linear assumptions to derive equivalent stiffness and damping for series and parallel configurations.

Model a mass-spring-damper system by combining mass, spring, and damper and deriving m x'' = F - B x' - K x from Newton's second law, linking force to displacement.

Simulate a mass-spring-damper system in Simulink to study its response to a step input. Build the model with gain, integrator, and summation blocks, then run parameterized simulations.

Simulate a mass–spring system with varying damping to show zero damping yields perpetual oscillations, while small to moderate damping causes decay and stabilization, using MATLAB simulations.

Transform time-domain models into the Laplace domain to simplify analysis, derive transfer functions, and simulate system behavior using matlab.

Learn to model a mass–spring–damper as a transfer function in the s-domain, build it in Matlab/Simulink, and validate with step input and damping effects.

Explore how using a discrete impulse instead of a step reveals system's response, detailing impulse input setup, sampling time and gain, and oscillation decay in a mass and damper model.

Model a two mass train system with an engine force, a spring coupling, and rolling friction; derive the equations of motion using free body diagrams and Newton's laws.

Model a two-mass train system in Simulink using integrators, summations, and gains for M1, M2, K, and G. Learn to apply a step input, simulate, and export velocity data.

Explore basic electrical definitions through a water tank analogy, covering voltage, current, and resistance. Learn ohm's law, power, and core components for battery modeling and Matlab simulations.

Learn how basic electrical elements, resistor, capacitor, and inductor, are modeled, including Ohm's law, series and parallel connections, energy storage, DC/AC behavior, and foundational Laplace-domain modeling.

Master Kirchhoff's circuit law, including the junction and closed-loop rules, to solve circuits using loop conventions, Ohm's law, voltage drops, and power balance with batteries and resistors.

Develop mathematical models to describe battery charging, discharging, and aging, and estimate state of charge and health from open-circuit voltage and terminal voltage.

Learn to estimate battery state of charge using coulomb counting by integrating current and dividing by capacity, with charging and discharging examples.

Learn the simple battery model by combining a state-of-charge dependent voltage source with charge and discharge internal resistances, yielding terminal voltage via open-circuit voltage and Kirchhoff's law.

Build a from-scratch battery model in Matlab/Simulink, mapping state of charge to open-circuit voltage and charging/discharging resistance with lookup tables, controlled by an m-script.

Configure a switch in MATLAB Simulink to select charging or discharging paths by current sign, then feed the chosen resistance through a multiplier to compute i r losses.

Explore the combined battery model, an empirical two-equation framework linking terminal voltage, resistance, current, and state of charge; learn parameter tuning and MATLAB-based simulation.

Explore how to implement a combined equivalent circuit battery model in MATLAB, reading data, defining parameters, and simulating state of charge and terminal voltage through a looping algorithm.

The lecture introduces the equivalent circuit battery model, adds RC branches to capture polarization, diffusion, and hysteresis, and covers first to third order models with SOC and OCV.

Derive the equivalent circuit model by translating voltage drops and RC dynamics into a discrete-time state equation, ready for Matlab simulation.

Build an equivalent circuit model in MATLAB from scratch, simulate state of charge and voltage variations from a current input, load data from Excel, and plot results.

Learn how a DC motor converts electrical energy to mechanical energy. Create two magnetic fields—one fixed in the stator and one powered in the rotor—to produce torque and enable modeling.

Model a DC motor by combining dynamics J theta'' = Kt I − B theta' with V = I R + L di/dt + E, where E = Kb theta'.

Discover how control systems regulate variables using sensors, feedback, and PID controllers, with examples like tank level and room temperature, and contrast manual versus automatic control.

Follow three steps to build a control system: model the plant with equations, choose a suitable sensor, and design a controller, including a PID.

Explore proportional, integral, and derivative controllers and how present, past, and future error shape the pid control action with Kepi, Ki, and Kd.

Develop a pid controller in Simulink by assembling proportional, integral, and derivative blocks, feeding them with the error signal, and packaging the result as a reusable subsystem for layered architecture.

Learn to integrate a PID controller with a mass-spring-damper plant in Simulink, adjust controller parameters, and observe how these changes affect the system response.

Integrate a pid controller with a mass-damper spring plant model in a feedback loop. Tune kp, ki, and kd to improve step response and eliminate steady-state error.

Learn how to evaluate dynamic system performance by examining key metrics—overshoot, rise time, settling time, and steady-state error—and understand how proportional-integral-derivative gains influence tuning and stability.

Learn to build and tune a pid controller in Simulink using the pid block, connect it to a plant transfer function, and visualize performance with a tuning tool.

Model a vehicle’s speed using Newton’s laws, derive a simple transfer function from input force to velocity, and introduce model reduction and PID controller design.

Combine the plant, PID controller, and feedback into one reduced transfer function via model reduction. Use the rule G1/(1+G1G2) to create a single block in Matlab for input to output.

Derive a single transfer function from the feed-forward and feedback paths (G1 divided by one plus Gwangju two) and compare a proportional controller using Matlab script for model reduction.

Define model parameters and a transfer function in MATLAB, build a PID controller, and simulate a closed-loop step response to compare proportional and PI control.

Develop a dc motor plant as a subsystem, add a controller with feedback, and tune performance using a step input and scope to visualize the response.