This course takes the concepts of differential equations, linear algebra, and Laplace transforms to physical and digital systems. In the true essence of mechatronics, students are asked to model mixed systems using transfer functions, block diagrams, state space, identification, cost function, optimization, gradient descent, numerical solutions and difference equations. Below are samples of analytical and code-based approaches taken throughout the course.
Analysis: Statespace, Runge-Kutta Method
Analysis: Transfer Function, Statespace
Analysis: Discretization, Total Solution Comparision
MatLab: Use a Difference Equation to Perform System Identification through Gradient Descent
MatLab: Calculate the State Model of a System and Use the (Vectorized) Runge-Kutta Method to Simulate the Response
This project required teams to model the response of a Quanser AERO, which is a dual motor machine that is intended to demonstrate mechatronics and control concepts at an undergraduate level. It contains two motors attached to propellers that exist on a rigid arm that is free to move in pitch and yaw. The propellers can be loosened and rotated to face any direction within 360 degrees.
Quanser AERO
Dynamic Modeling
The deliverables for this project were normalized models of the thruster, pitch and yaw. In this, the thruster and pitch were represented as as single-input, single-output linear differential equations, and the yaw was represented as a multi-input, single-output differential equation.
Each of these models were derived from schematics of the product which required teams to think critically about interactions between the subsystems. Ultimately, the project required us to construct differential equations based on an electrical-to-rotational transducer, equations of motion, and nonlinear relationships between pitch and yaw angle.
Once the differential equations were constructed of both known and unknown terms, they were ready to be taken to the next stage of the project: modeling and simulating potential responses.
Thruster Model Schematic
Yaw Model Schematic
Pitch Model Schematic
Model Characteristics and Simulation
After derivation, the next task was to use experimental data to validate the accuracy of the calculated model. This is done by running the system through an experiment where the input and output of that system is observed and comparing that real output to a simulated output given the same input, all of which can be done using MatLab Simulink. First, the differential equations must be re-expressed in terms of either a transfer function, or a state-space model.
Using Laplace transforms, characteristics of underdamped systems, interpolation from experimental data, state space, logarithmic decrement, and a polynomial expression of a line of best fit, our team was able to represent the pitch, thruster, and thruster forces associated with defined initial conditions and experimentally gathered responses.
The differences between our calculated model and the experimental data likely originated from our linearization of a trigonometric term under the assumption of small angles (tan(θ) ≈ θ) .
Simulink Model of Experimental Pitch Data against Calculated Pitch Data
Polynomial Relationship Between Angular Velocity and Thruster Force
Simulink Model of Pitch Data Calculated Based on Thruster Force
Calculated Pitch Angle (Yellow) Compared to Experimental Pitch Data (Blue)
This course prompts students to explore the intersections between mechanical and electrical engineering, computer science, and control and information technologies. Students will be presented fundamental concepts in mechatronics design, including analog and digital electronics, serial communication, sensors, actuators, motors, microcontrollers, and interfacing microcontrollers with electromechanical systems.
Arduino Sketch: Control LED Brightness using Potentiometer
Arduino Sketch: Move bot in the shape of a square
In this course, students were challenged to design and fabricate robots for two distinct, hands-on competitions: the Sumo and the Billiard competition. Both projects required students to apply mechanical design, programming, and problem-solving skills within specific constraints.
Integrated Arduino Circuit
Enhanced Robo-Billiards
This project revolved around a Robo-Billiard competition where the goal was to design a robot capable of navigating a raised platform, sorting, and depositing pool balls into designated pockets within an 8-minute timeframe. The robot was built using VEX Motors, an Arduino Uno R3 Board, a Motor Driver L298P Shield, and 3D-printed components with a material cost limit, as well as LEGO pieces. Our team's approach focused on maximizing the robot's reach by incorporating a large grappling arm with a hand to pick up and sort balls, with additional tasks such as lifting a ping pong ball and sorting by ball type for extra points.
Robo-Billiard Bot
Sumo-Bot
Sumo-Bot Competition
This project was centered around an Illinois-Class Sumo Robot competition with specific constraints, including size, weight, and budget for parts, as well as a class-issued rechargeable battery. Our team's design focused on creating a compact robot with a front-heavy center of mass to avoid being pushed off the edge, and incorporating a slanted front edge to confuse opposing sensors. We used a three-wheel drive system with omni-directional front wheels. This competition required the robot to function autonomously, introducing new challenges due to the unpredictable nature of external factors and opponent strategies.