Certificate in Model Predictive Control in Practice

The 'Certificate in Model Predictive Control in Practice' is designed for engineers, researchers, and professionals in the control systems and automation industry who seek to enhance their expertise in applying model predictive control (MPC) techniques in real-world industrial and process control scenarios. This programme provides a comprehensive understanding of MPC principles, including its theoretical foundations, practical implementation, and integration into complex control systems. Participants will learn to model processes, optimize control strategies, and implement MPC algorithms using advanced software tools and industrial standards.

Key skills and knowledge developed throughout the programme include the ability to formulate and solve optimization problems in real-time, design and tune MPC controllers for various process dynamics, and integrate MPC strategies with other control methodologies. Learners will gain proficiency in using industry-standard software for MPC development and will be equipped to handle advanced control challenges in chemical, mechanical, and electrical engineering applications. The programme also emphasizes the importance of data analytics and machine learning in enhancing MPC performance and robustness.

The career impact of this certificate is significant, as it prepares professionals to lead projects involving complex process control, optimize industrial operations, and develop innovative control solutions. Graduates can expect to advance their careers in roles such as control systems engineers, automation specialists, and process control engineers. The skills acquired will enable them to contribute to the design, implementation, and optimization of control systems in manufacturing, energy, and other industrial sectors, driving operational efficiency and process improvement.

1. 1. Introduction to Model Predictive Control (MPC): Learners will study the basic principles of MPC, including its mathematical foundations and application areas. They will gain an understanding of how MPC can be used to optimize process operations.
2. 2. Dynamic System Modeling: This module focuses on building and analyzing dynamic models of systems using first principles and data-driven techniques. Learners will develop skills in model identification and validation.
3. 3. MPC Algorithm Fundamentals: Learners will delve into the core algorithms of MPC, including the formulation of the optimization problem and the solution methods. Practical skills in implementing MPC algorithms will be developed.
4. 4. Constraints and Objective Functions in MPC: This module covers the use of constraints and objective functions in MPC design. Learners will learn how to set up and modify these elements to meet specific control objectives.
5. 5. MPC Implementation and Tuning: Learners will study practical aspects of MPC implementation, including tuning strategies and the integration of MPC with existing control systems. They will gain hands-on experience in configuring MPC controllers.
6. 6. Advanced MPC Techniques: This module explores advanced MPC strategies such as moving horizon estimation, multi-parametric programming, and economic MPC. Learners will understand the benefits and limitations of these techniques.
7. 7. MPC Case Studies: Through case studies, learners will apply MPC to real-world industrial processes. They will analyze system dynamics, design control strategies, and validate performance.
8. 8. Online Identification and MPC: This module covers online identification methods for MPC and their integration with the control loop. Learners will learn how to continuously update models and adjust control strategies in real-time.
9. 9. MPC for Distributed Systems: Learners will study MPC techniques for managing distributed systems, including networked controllers and decentralized control architectures. Practical skills in designing and implementing distributed MPC systems will be developed.
10. 10. Future Trends in MPC: This final module looks at emerging trends and technologies in MPC, including artificial intelligence, machine learning, and real-time optimization. Learners will gain insights into the future direction of MPC research and application.