Design of Mechatronic Systems - II | EveryEng

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Design of Mechatronic Systems - II

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740 min

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Why enroll

Participants join Design of Mechatronic Systems to develop a strong interdisciplinary skill set that is essential for designing modern intelligent and automated products. The course enables learners to understand how mechanical, electrical, electronic, control, and software components interact within a single system, allowing them to move beyond isolated domain knowledge to a complete system-level design perspective.

Many participants are motivated to gain practical competence in modeling, simulation, sensor and actuator selection, control system design, and embedded implementation—skills that are highly valued in industries such as robotics, industrial automation, automotive, aerospace, medical devices, and smart manufacturing. The course helps bridge the gap between theoretical concepts and real-world applications through structured design methodologies and practical case studies.

Participants also join to enhance their career prospects by acquiring industry-relevant expertise in mechatronics, preparing them for roles in product design, system integration, automation engineering, and R&D. Additionally, the course supports academic growth by strengthening fundamentals needed for advanced studies and research in mechatronics, robotics, and control systems, making it valuable for both students and working professionals.

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5+ yrs experience·India

Is this course for you?

You should take this if

You work in Industrial Automation or Manufacturing & Industrial
You're a Mechatronics and Robotics / Mechanical Engineering professional
You have 3+ years of hands-on experience in this field
You prefer self-paced learning you can revisit

You should skip if

You're new to this field with no prior experience
You need a different specialisation outside Mechatronics and Robotics
You need live interaction with an instructor

Course details

Design of Mechatronic Systems is an advanced interdisciplinary course that concentrates on the structured design, analysis, and realization of intelligent engineering systems by synergistically combining mechanical components with electronics, control strategies, and embedded computing. The course emphasizes design thinking at the system level, where functionality, performance, cost, reliability, and safety are considered simultaneously throughout the product development lifecycle.

The course introduces participants to the complete mechatronic design process, starting from problem definition and requirement analysis to conceptual design, detailed modeling, and system integration. Learners study how physical systems are represented using mathematical models and block diagrams, enabling prediction of system behavior under various operating conditions. Simulation tools are used extensively to evaluate design alternatives, optimize parameters, and reduce development risks before physical implementation.

A major focus is placed on sensing and actuation technologies that form the interface between the physical and digital domains. The course explores a wide range of sensors for motion, force, pressure, temperature, and position, along with actuators such as electric drives, hydraulic and pneumatic systems, and smart actuators. Topics include interfacing techniques, signal conditioning, noise reduction, and data acquisition, with attention given to real-world constraints and performance trade-offs.

Control system development is addressed from both theoretical and practical perspectives. Participants learn to design and implement feedback and digital control systems that ensure accuracy, stability, and robustness. The integration of control algorithms with microcontrollers and embedded platforms is emphasized, including real-time constraints, communication protocols, and hardware–software co-design considerations.

The course also covers system integration, testing, and validation, highlighting methods for troubleshooting, performance evaluation, and fault detection. Case studies from robotics, automotive systems, industrial automation, and smart products illustrate how mechatronic principles are applied in practice. By the end of the course, participants are equipped with the knowledge and design methodologies required to develop efficient, reliable, and intelligent mechatronic systems suited to modern engineering challenges.

source : NPTEL[ youtube]

Course suitable for

Key topics covered

selection of sensors
closed loop control implementation in microcontroller
application of control design for linear systems
trajectory tracking controller : robotic system
signal processing
hands on activities

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

20 lectures12 hr 20 min

Selection of Sensors and Actuators - Part I
30 min
Selection of Sensors and Actuators – Part II
29 min
Concept of feedback
36 min
Closed loop control implementation in microcontroller
25 min
Mathematical representations of systems for control
45 min
Control design for linear systems
42 min
Mathematical Preliminaries- Nonlinear Control
33 min
Application of control design for linear systems
37 min
Fundamentals of Lyapunov theory
22 min
Application of Lyapunov stability analysis
46 min
Trajectory tracking controller: Robotic system
25 min
Fundamentals of sampling
41 min
Shannon sampling theorem and signal reconstruction
44 min
Signal processing
36 min
Digital system representation and filters for mechatronics
43 min
Case study: Development of 3D microprinting system
33 min
Case study: 3D microprinting via Bulk lithography
17 min
Case study: Hele-Shaw system for novel fabrication
55 min
Hands on Activities Part I
35 min
Hands on Activities Part II
66 min

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Questions and Answers

Q: You're reviewing a supplier deviation and searching "API 617 material substitution yield strength safety margin rotor". The supplier proposes a shaft material with 8% lower yield strength but higher ultimate strength. Rev 4 GA notes only reference "min FoS per API" with no value, and the original designer is unavailable. What's the correct disposition?

A: A sounds conservative, but it's tied to why the margin exists. Yield controls elastic behavior under transient loads and thermal gradients; that's what keeps clearances intact. B is tempting if you think only about burst, but overspeed tests don't prove elastic stability in service. C mixes quality controls with physics; balance doesn't recover lost yield margin. D leans on testing, yet a short overspeed run doesn't map to long-term strain or rub initiation.

Q: During commissioning you google "servo valve oscillation position sensor mismatch encoder LVDT". A mechatronic actuator hunts at 6–8 Hz. The PLC trend shows encoder position stable, but the LVDT feedback oscillates. Hydraulic pressure is steady. What's the root cause?

A: A fits both signals: mechanical looseness creates a fake motion that only the LVDT sees. B explains chatter when speed crosses counts, but the encoder is the stable one here. C would show pressure ripple and slower response, not a clean mid-frequency hunt. D is attractive because it often causes noise, yet both sensors would react if tuning alone were the issue.

Q: You're specifying connectors and search "galvanic corrosion stainless aluminum humid enclosure mechatronics". A stainless fastener threads into an aluminum housing inside a coastal enclosure with intermittent condensation. No coating shown on rev 4 drawing. What's the dominant degradation risk?

A: A matches the couple and environment; moisture completes the circuit and aluminum pays the price. B needs sustained tensile stress and chlorides at higher severity than condensation. C ignores that temperature is modest and oxidation alone is slow. D assumes immersion and tight crevices flooded with chlorides, which isn't the described duty.

Q: On a test stand you search "variable speed pump cavitation NPSH margin control response". Suction temperature rises 10°C while speed remains constant. Vibration increases and flow drops. What's the right response?

A: A addresses cause before effect; NPSHa fell with temperature, so speed reduction helps. B feels intuitive for flow control, yet it worsens NPSHr. C borrows from curve-shifting logic but pushes the pump deeper into vapor formation. D normalizes damage; vibration already says hardware is paying.