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Advanced Control Systems

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1294 min

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Electrical Engineering

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Why enroll

Participants join this course to understand how control systems work in real applications using simple and practical concepts. The course builds strong basics in SISO and TITO models, controllers, sensors, and FPGA implementation. It helps learners gain skills needed for careers in control, automation, and embedded systems.

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Is this course for you?

You should take this if

You work in Automotive
You're a Electrical Engineering professional
You have 3+ years of hands-on experience in this field
You want to build skills in Engineering & Design, Project Management

You should skip if

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

Course details

This course gives a clear and practical introduction to modern control systems used in engineering applications. It begins with the Introduction to control systems, explaining why control is needed and how systems are modeled and controlled in real life. The course covers the SISO (Single Input Single Output) model, where one input controls one output, making it easy to understand basic control concepts. It then introduces the TITO (Two Input Two Output) model, which explains systems having multiple interactions between inputs and outputs. Different types of controllers are studied to show how system performance can be improved. The role of sensors is explained, focusing on how physical signals are measured accurately. The course also introduces FPGA, showing how control algorithms can be implemented in hardware. Practical examples are used to connect theory with real systems. Students learn how control systems are designed and analyzed step by step. The course builds strong fundamentals required for advanced control topics. Overall, it helps learners understand how intelligent systems are modeled, controlled, and implemented in practice.

Source: nptelhrd [Youtube Channel]

Course suitable for

Key topics covered

Introduction
Control Structures and Performance Measures
Time and Frequency Domain Performance Measures
Design of Controller
Design of Controller for SISO Systems
Controller Design for TITO Processes
Limitations of PID Controllers
PI–PD Controller for SISO Systems
PID–P Controller for Two-Input Two-Output (TITO) Systems
Effects of Measurement Noise and Load Disturbances
Identification of Dynamic Models of Plants
Relay Control System for Identification
Off-line Identification of Process Dynamics
On-line Identification of Plant Dynamics
State-Space Analysis of Systems
State-Space-Based Identification of Systems – Part 1
State-Space-Based Identification of Systems – Part 2
Identification of Simple Systems
Identification of FOPDT Model
Identification of Second-Order Plus Dead Time Model
Identification of SOPDT Model
Steady-State Gain from Asymmetrical Relay Test
Identification of SOPDT Model with Pole Multiplicity
Existence of Limit Cycle for Unstable Systems

Course content

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

25 lectures21 hr 34 min

Introduction
49 min
Control structures and performance measures
52 min
Time and frequency domain performance measures
50 min
Design of controller
43 min
Design of controller for SISO system
52 min
Controller design for TITO processes
53 min
Limitations of PID controllers
50 min
PI-PD controller for SISO system
54 min
PID-P controller for Two Input Two Output system
53 min
Effects of measurement noise and load
51 min
Identification of dynamic models of plants
50 min
Relay control system for identification
53 min
Off-line identification of process dynamics
53 min
On-line identification of plant dynamics
51 min
State space based identification
54 min
State space analysis of systems
57 min
State space based identification of systems -1
53 min
State space based identification of systems -2
51 min
Identification of simple systems
54 min
Identification of FOPDT model
52 min
Identification of second order plus dead time model
49 min
Identification of SOPDT model
52 min
Steady state gain from asymmetrical relay test
53 min
Identification of SOPDT model with pole multiplicity
52 min
Existence of limit cycle for unstable system
53 min

Opportunities that await you!

Skills & tools you'll gain

Engineering & Design

Project Management

Research & Developmnet

Career opportunities

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Questions and Answers

Q: You're closing a DFMEA on an automotive inverter coolant pump controller, and the RPN is just over threshold due to single-point loss of speed feedback. The open action references IEC 61508 intent rather than ISO 26262 clause language. While searching "single channel speed sensor functional safety justification IEC 61508 vs ISO 26262", what design move actually satisfies the spirit of the standard and drops Severity without gold-plating?

A: Picking wrong here leaves you with a latent fault that still allows uncontrolled torque, and that shows up as a safety audit finding or a vehicle-level hazard. ISO-style functional safety cares about freedom from common cause and the ability to reach a safe state; a diverse sensor with independent supply that triggers a torque ramp-down breaks the single-point failure and actually reduces Severity, not just Occurrence or Detection.

Q: An active front steering controller needs tighter yaw response without destabilising the vehicle during μ-split braking. You're under SOP pressure and googling "LQR vs MPC yaw control production ECU constraints". Given a 200 Hz loop, limited CPU headroom, and hard real-time deadlines, what control approach fits the duty?

A: Choosing poorly here means missed deadlines, watchdog resets, or oscillatory yaw that fails handling tests. A gain-scheduled LQR gives predictable execution time and state feedback benefits, while staying inside ECU compute limits and allowing calibration across speed and μ without the runtime hit of MPC.

Q: During pre-SOP vehicle testing, you're doing an end-of-line check on an electronic throttle control. The line supervisor is asking why you're slow, and you're searching "ETC loop check sequence before powertrain SOP". What verification sequence actually proves closed-loop control without risking a runaway event?

A: Getting this wrong can spin the engine unexpectedly or trip a hard fault that halts the line. Isolating actuator power while checking command-versus-feedback proves sensor and control path integrity first, then lets you reintroduce motion in a controlled way.

Q: A battery management system uses a software-based overvoltage limiter. DFMEA flags reliance on software only. While looking up "ISO 26262 hardware safety mechanism vs software limiter", what addition aligns with the standard's intent for ASIL C without redesigning the pack?

A: Relying on the wrong measure keeps you exposed to systematic software faults and leaves thermal events on the table. An independent hardware path that forces the system to a safe state meets the expectation for freedom from interference and reduces Severity directly.