MATLAB/SIMULINK Bible | Go From Zero to Hero + ChatGPT!

Learn to build a Simulink model by selecting blocks, adding two signals, generating and tuning a sine wave, adjusting model configuration, exporting to workspace, and plotting with a gain multiplier.

Learn to generate three sine waves at different frequencies in Simulink and sum them. Export the result to the MATLAB workspace and view it on scopes.

Pair the gain block with a signal in Simulink to multiply it and view the magnified output on a scope; use a multiplexer to plot multiple signals on one scope.

Master differentiation and integration in Simulink through the second project. Call the Simulink model from an M script and implement the mathematical equation using Simulink.

Learn to perform differentiation and integration using Simulink by dragging in integrator and derivative blocks, then plot the constant input's ramp and its derivative on a scope.

Set an external initial condition for a Simulink integrator using an x node and a constant input, starting from values like 10 or 100 during a 100-second simulation.

Implement a mathematical equation in Simulink by modeling dx/dt with two integrators, a derivative, and a feedback loop, then control parameters F, T, and A with an M script.

Explore a time-domain simulation of a mass-spring-damper system in Simulink, derive equations via Newton's second law, sketch the free-body diagram, and analyze parameter effects.

Model a mass-spring-damper system with a 1 kg mass, stiffness 20 N/m, and damping 10 N seconds per meter, using a free body diagram and Newton's second law in Simulink.

Develop a Simulink model of a mass-spring-damper system using Newton's second law, integrating acceleration to velocity to position, with a step input F and parameters M, B, K.

Explore how damping coefficient B affects oscillation and decay in a mass-spring system, using Simulink to model, tune, and simulate for vehicle design.

Explore how to simulate a mass spring damper system in the Laplace domain (s-domain), transforming it into a linear form for easy solving, and analyze impulse response effects.

Transition from time-domain mass spring damper equations to the s-domain with Laplace transforms, where derivatives become s terms and a transfer function enables a simple one-block Simulink model.

Learn to build a second-order transfer function in Simulink using a single block, mapping mass, damping, and stiffness to s-domain dynamics, and verify responses with a step input.

Learn to generate an impulse response in Simulink using a discrete impulse block and gain, compare to step input, and observe oscillation and damping in a mass-spring-damper.

Model a battery system in Simulink, learn battery cell modeling, use Excel with MATLAB/Simulink, add a lookup table, implement an if condition, and plot data with MATLAB.

Model battery behavior by linking state of charge to open circuit voltage, compare ocv and terminal voltage under load, and build a simple Simulink model using a lookup table.

Develop a simple battery model by linking state of charge to open circuit voltage and updating state of charge with Coulomb counting, including terminal voltage and internal resistance in Simulink.

Build and simulate a battery model in Simulink from scratch, mapping state of charge to open circuit voltage and resistance via lookup tables, driven by an M script.

This assignment solution shows how to use a switch in Matlab Simulink to select R charge or R discharge based on current sign, then multiply by current.

Model dynamic systems and design controllers with Simulink to achieve desired performance. Build and tune PID controllers in Simulink while exploring subsystem architectures and project-based learning outcomes.

Explore the fundamentals of control systems, from measurement and set-point comparison to computation and actuation, including PID control and Simulink modeling via a tank example.

Follow a three-step process: model the plant, select a sensor, and design a PID controller to implement control actions in Simulink.

Explore PID controllers by combining proportional, integral, and derivative actions to minimize error between set point and output, using Kp, Ki, and Kd gains and simulating in Simulink.

Build a pid controller in Simulink from scratch by defining kp, ki, kd gains and assemble a layered architecture with a subsystem to combine error, integrator, and differentiator.

Explore how to integrate a PID controller with a mass-spring-damper model in a simulation, and learn how to tune controller parameters to observe their impact on system response.

Combine a mass-spring-damper plant with a pid controller in Simulink, creating a feedback loop to compare setpoint and output, then tune kp ki kd and simulate the step response.

Adopt a simpler method to develop and tune a PID controller in Simulink, integrate it with a plant model in the s-domain, and optimize its performance parameters.

Assess dynamic system performance by analyzing PID metrics like overshoot, rise time, settling time, and steady-state error, and tune KP, KI, and KD in MATLAB/Simulink.

Build a mass-spring-damper transfer function in the s-domain, deploy a Simulink PID block, and tune PID parameters with the built-in tool for a step response.

Apply pid controller to maintain vehicle speed in automatic cruise control, building a vehicle dynamics model and deriving a transfer function for easy Simulink implementation.

Model a vehicle dynamics system using Newton's laws to derive a velocity-based transfer function, then design a pid controller in pure m-script, emphasizing model reduction.

Learn to reduce a PID-controlled plant with feedback into a single transfer function using the G1/(1+G1G2) rule, and implement it in MATLAB and Simulink.

Derive a single transfer function for the entire system from the feedforward and feedback paths, using G1/(1+G1G2). Demonstrate pid controller concepts and model reduction with matlab and simulink.

Explore building a PID controller in pure MATLAB (M-script) without Simulink, defining a plant with transfer functions, applying feedback, and analyzing step responses for proportional and PI control.