BME 5540

Digital Control Strategies for Biomedical Applications

Course Description

Course information provided by the 2026-2027 Catalog.

This course introduces the principles and applications of digital control systems in biomedical engineering. Students learn how to model, simulate, and design feedback controllers for physiological and electromechanical systems using MATLAB and Simulink. The curriculum bridges continuous and discrete-time control concepts, emphasizing stability, response optimization, and implementation in digital environments. Topics include sampling, discretization, state-space representation, and adaptive strategies for real-time control of medical and biotechnological systems. The course also explores emerging approaches using Agentic Artificial Intelligence (IA Agêntica) for autonomous and adaptive feedback in medical devices. Designed for graduate and advanced undergraduate students, it equips learners with practical skills for simulation, analysis, and innovation in intelligent biomedical technologies. Course requires: Basic understanding of linear systems, differential equations, and MATLAB is recommended. Familiarity with control concepts such as transfer functions and feedback stability will be helpful.

Last 4 Terms Offered (None)

Learning Outcomes

• Model biomedical and engineering systems in discrete time using difference equations, transfer functions, and state-space representations to describe the dynamic behavior of sensors, actuators, and physiological processes.
• Apply sampling theory and the Z-transform to analyze how continuous signals are converted into digital form and assess system stability using techniques such as the Jury test and eigenvalue analysis.
• Simulate and evaluate digital control system responses in MATLAB, interpreting how controller parameters affect performance, stability, and steady-state error in biomedical applications.
• Design and implement fundamental digital control strategies—including PID, dead-beat, and pole-placement controllers—and understand their integration into embedded systems used in medical devices.
• Translate theory into practice through biomedical case studies involving ventilators, infusion pumps, incubators, and prosthetic systems, demonstrating how digital control enhances precision, safety, and reliability in healthcare technologies.
• Explore emerging applications of Agentic Artificial Intelligence (IA Agêntica) in adaptive and autonomous control, recognizing how intelligent agents can support decision-making, optimization, and real-time adaptation in biomedical systems.

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