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Control Systems Fundamentals and Applications using MATLAB Course Overview

Control Systems Fundamentals and Applications using MATLAB Course Overview

The Introduction to Model Predictive Control (MPC) course offers a thorough foundation in MPC principles, focusing on key concepts like stability, optimality, and state feedback. Participants will learn to design and implement both basic and constrained MPC techniques within various real-world controls. Ideal for control engineers, system designers, and graduate students, this course ensures you possess the essential tools for effective MPC application. Structured across five engaging lectures, you will explore different models, understand their significance, and analyze the implications of constraints, equipping you to tackle complex control challenges with confidence. Join us to advance your expertise in this critical area of automation.

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  • Live Training (Duration : 40 Hours)
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Free Pre-requisite Training

Join a free session to assess your readiness for the course. This session will help you understand the course structure and evaluate your current knowledge level to start with confidence.

Assessments (Qubits)

Take assessments to measure your progress clearly. Koenig's Qubits assessments identify your strengths and areas for improvement, helping you focus effectively on your learning goals.

Post Training Reports

Receive comprehensive post-training reports summarizing your performance. These reports offer clear feedback and recommendations to help you confidently take the next steps in your learning journey.

Class Recordings

Get access to class recordings anytime. These recordings let you revisit key concepts and ensure you never miss important details, supporting your learning even after class ends.

Free Lab Extensions

Extend your lab time at no extra cost. With free lab extensions, you get additional practice to sharpen your skills, ensuring thorough understanding and mastery of practical tasks.

Free Revision Classes

Join our free revision classes to reinforce your learning. These classes revisit important topics, clarify doubts, and help solidify your understanding for better training outcomes.

Inclusions in Koenig's Learning Stack may vary as per policies of OEMs

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Target Audience for Introduction to Model Predictive Control (MPC)

Introduction:
The "Introduction to Model Predictive Control (MPC)" course equips learners with essential knowledge and practical skills in advanced control techniques, targeting professionals in various engineering and research fields.


Job Roles and Audience:


  • Control Engineers
  • Automation Professionals
  • System Designers
  • Graduate Students in Control Systems
  • Researchers in Advanced Control Techniques
  • Mechanical Engineers
  • Electrical Engineers
  • Process Engineers
  • Software Developers working on control systems
  • Data Scientists specializing in predictive modeling
  • Quality Assurance Engineers in automated systems
  • Project Managers in technology and engineering sectors
  • University Professors teaching control systems
  • Industry Analysts in automation and control systems
  • Robotics Engineers
  • Manufacturing Engineers
  • Consultants in industrial automation
  • Technical Trainers and Instructors in related fields


Learning Objectives - What you will Learn in this Introduction to Model Predictive Control (MPC)?

Introduction:
The Introduction to Model Predictive Control (MPC) course equips students with foundational knowledge and practical skills to understand and implement MPC techniques, focusing on stability, optimality, and constraints in control systems.

Learning Objectives and Outcomes:

  • Understand the fundamentals of Model Predictive Control (MPC).
  • Explore different models and their significance in control systems.
  • Design and implement basic MPC with state feedback.
  • Analyze stability and optimality in MPC applications.
  • Gain insights into constrained MPC and its applications.
  • Learn the key principles and real-world applications of MPC.
  • Examine the impact of various models on MPC performance.
  • Grasp the importance of state-space representation in MPC.
  • Develop skills for addressing real-time constraints in control systems.
  • Engage in hands-on applications and problem-solving in MPC scenarios.

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