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Model Predictive Control (MPC) Fundamentals Course Overview

Model Predictive Control (MPC) Fundamentals Course Overview

Discover the Model Predictive Control (MPC) Fundamentals course at Koenig Solutions, designed for those seeking to enhance their expertise in control systems. This course covers essential topics such as prediction models, optimization techniques, and constraint handling.

By the end of the course, participants will be able to formulate and implement MPC strategies, enabling them to effectively manage complex systems in real-world applications. The learning objectives focus on developing a solid understanding of MPC theory and its practical uses in industries like robotics, automotive, and process control. Equip yourself with the skills to tackle modern challenges in engineering and automation through this engaging course.
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Target Audience for Model Predictive Control (MPC) Fundamentals

Model Predictive Control (MPC) Fundamentals offers an in-depth understanding of advanced control strategies, catering to professionals eager to enhance efficiency in dynamic systems.


  • Control Systems Engineers
  • Automation Engineers
  • Process Engineers
  • Robotics Engineers
  • Software Developers
  • Data Scientists
  • Systems Analysts
  • Researchers in Control Theory
  • Mechanical Engineers
  • Electrical Engineers
  • Project Managers in Automation
  • Graduate Students in Engineering
  • Industrial Engineering Professionals
  • Operations Researchers
  • AI/ML Engineers focusing on optimization


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

Introduction:
The Model Predictive Control (MPC) Fundamentals course equips students with foundational knowledge and practical skills in MPC methodology, focusing on optimization techniques and real-time control applications.

Learning Objectives and Outcomes:

  • Understand the principles of Model Predictive Control and its advantages.
  • Learn how to formulate MPC problems using optimization frameworks.
  • Analyze the dynamics of controlled systems for effective modeling.
  • Implement control strategies for discrete and continuous systems.
  • Design and tune MPC controllers for enhanced performance.
  • Explore stability and robustness considerations in MPC design.
  • Gain insights into the computational aspects of real-time control.
  • Apply MPC techniques to various industrial applications.
  • Evaluate performance metrics and assessment methods for MPC systems.
  • Develop hands-on experience through practical demonstrations and case studies.

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