Timing, synchronization and data flow; parallel, serial, and analog interfaces with sensors and actuators; microprocessor system architecture; buses; direct memory access (DMA); interfacing considerations. System simulation in CAD.

An introduction to the feedback control of dynamic systems. Laplace transforms and transfer function techniques; frequency response and Bode diagrams. Stability analysis via root locus and Nyquist techniques. Performance specifications in time and frequency domains, and design of compensation strategies to meet performance goals.

Focuses on designing mechanical motion transmission systems, including gearing, couplings, belts, and lead screws. Students will learn to measure and sense mechanical motion while selecting appropriate sensors and electromechanical actuators. Topics include programmable logic controllers (PLCs), sequential controller design, and digital input/output systems. Practical applications and case studies provide hands-on experience in integrating these components into functional systems.

General domain knowledge. Manipulators, rovers, flight vehicles/quadcopters. Constraint considerations and analysis. Introduces the Robot Operating System (ROS) framework.

Machine learning (ML) principles along with their application to mechatronic systems are introduced. Different ML paradigms, such as supervised, unsupervised, and reinforcement learning are covered. Topics include data pre-processing and feature engineering, linear and polynomial regression, anomaly detection, deep learning for computer vision, and reinforcement learning. Students learn to model, control, and optimize mechatronic systems using ML models through weekly labs, programming assignments, and a hands-on final project.