Control Systems Fundamentals and Applications using MATLAB Training

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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Flexi Video	16,449
Official E-coursebook
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Hands-On-Labs²	4,159
+ GST 18%	4,259
Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with exam & Labs)	28,359 (INR)

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Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

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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.

Control Systems Fundamentals and Applications using MATLAB Course Overview

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Target Audience for Introduction to Model Predictive Control (MPC)

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

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