Executive Development Programme in Robotics: Implementing Model Predictive Control in Simulation

This course is designed for executives, mid-level managers, and technical leads in robotics and automation who seek to enhance their understanding of Model Predictive Control (MPC) and its practical implementation. Participants will gain the knowledge to effectively develop and apply MPC strategies in simulation environments, enabling them to optimize robotic systems for efficiency, reliability, and performance in various applications.

By the end of the program, attendees will be proficient in using simulation tools to design MPC algorithms tailored to specific robotic tasks, and they will be able to evaluate and refine these algorithms based on real-world scenarios, contributing to more advanced and intelligent robotic systems.

1. 1. Introduction to Model Predictive Control (MPC): Learners will study the foundational concepts of Model Predictive Control, including its principles, benefits, and applications. They will gain an understanding of how to formulate MPC problems and set up the necessary constraints for control systems.
2. 2. System Identification and Modeling: This module covers the process of identifying and modeling systems for MPC. Learners will learn techniques for creating accurate models and will practice using system identification tools and software.
3. 3. Basic MPC Design and Implementation: Students will design and implement basic MPC controllers for simple robotic systems. They will learn to use simulation tools to test and validate their controllers before deploying them in real-world scenarios.
4. 4. Advanced MPC Techniques: This module delves into advanced MPC techniques such as moving horizon estimation and robust MPC. Learners will explore how these techniques enhance the performance and reliability of control systems.
5. 5. Constraint Handling in MPC: In this module, learners will focus on the practical aspects of handling constraints in MPC, including input and state constraints, and will practice implementing these constraints in simulations.
6. 6. MPC in Dynamic Environments: This module introduces learners to the challenges and solutions for implementing MPC in dynamic and changing environments. They will learn how to adapt MPC strategies to maintain system stability and performance.
7. 7. MPC for Multi-Agent Systems: Students will study the application of MPC in multi-agent robotic systems. They will learn how to coordinate multiple robots using MPC to achieve complex tasks efficiently.
8. 8. Real-Time MPC Implementation: This module covers the practical aspects of implementing MPC in real-time systems, including hardware considerations and optimization techniques for efficient execution.
9. 9. Advanced Simulation Techniques for MPC: Learners will explore advanced simulation methods for testing and validating MPC controllers, including Monte Carlo simulations and multi-objective optimization techniques.
10. 10. Case Studies in MPC for Robotics: In this final module, students will apply their knowledge to real-world case studies in robotics, working on projects that integrate MPC into robotic systems for various applications.