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We Badly Need more Control Engineers
IoT revolution, self-driving cars, remote monitoring, prognostics and diagnostics are among the mega trends today. These trends have one main thing in common: controls. Whether it is a temperature controller that is linked to the ambient temperature and time of the day or a diagnostic application for an oil-rig, a control engineer needs to design the control logic and/or build a simulation model for the machine(s). As someone who has been leading teams of control engineers, I know how hard it has been to recruit one. The need for control engineers will only increase in the next decade. Even today, there is a significant shortage of automation engineers, control engineers and embedded control engineers.
A control engineer usually has four basic skills:
- understands the fundamental physics behind the application (combustion and chemical reactions, electric circuits, HVAC, dynamics, computational fluid dynamics, etc.)
- knows the basics of control theory (feedback control, PID, stability, transfer functions, observers, etc.)
- knows one programming environment that he/she uses to write the control logic
- knows the process to deploy the developed control logic into the microcontroller
These skills are rarely found together in candidates who don’t have a college degree in control.
To compound the problem of scarcity of control engineers, the future of smart machines and integrated systems will require advanced control techniques that are rarely found among control engineers. Advanced control techniques are either taught at a Masters level in college, or acquired through self-learning and experimentation post college while on the job. The latter is not an easy task by any measure. Furthermore, many control engineers don’t get the chance to design the control algorithms. More than 90% of control engineers are maintaining legacy algorithms and not developing new control algorithms. In a summary, control engineers that know how to design, tune and deploy control algorithms are very scarce.
In an attempt to promote advanced controls and provide a practical guide for designing a highly regarded advanced control technique (Model Predictive Control-MPC), my friend, Bibin Pattel, and I authored Practical Design and Application of Model Predictive Control. This book summarizes our self-learning journey of MPC when we were designing an industrial controller for a nonlinear application. We have learned, the hard way, that there many details in the design process that are omitted in the existing publications of MPC. For example, when we were designing MPC, we didn’t know if the identified model was good enough. We also didn’t know what to do when we designed the controller and it didn’t perform. Through a process of multiple redesigns, we were able to successfully design and deploy MPC successfully. It did require a lot of coding tricks, system identification iterations, controller redesign, controller tuning and issues with hardware deployment.
In the book, we streamlined the process of designing and deploying MPC. This was done mainly to make it easier for control engineers to self-learn MPC in a practical way without diving into the theory too much.
In chapter 2 of the book, we propose a new approach to explain MPC for someone who doesn’t know anything about it. In the subsequent chapters, we cover, to a great length of details, mass-spring system, applications related to ship navigation control, photovoltaic cells control, air-handling control of diesel engines, in addition to Monte-Carlo simulations as a means to test the robustness of the controller.
If you found this story stimulating, you may be interested in browsing more content within this book on ScienceDirect. We are pleased to offer you a free chapter – access this content by clicking on this link – Single MPC Design for a Ship.
Need a copy of your own? Save 30% on this book on elsevier.com. Enter discount code STC317 at checkout.
We dedicated a website for the book to allow those who are interested to learn more about MPC, purchase robotic platforms with MPC controller, or provide feedback about the book or want us to collaborate with us: https://www.practicalmpc.com/
Furthermore, all the Matlab® and Simulink® codes are available on https://www.mathworks.com/matlabcentral/fileexchange/
About the book:
Practical Design and Application of Model Predictive Control is a self-learning resource on how to design, tune and deploy an MPC using MATLAB® and Simulink®. This reference is one of the most detailed publications on how to design and tune MPC controllers.
- Illustrates how to design, tune and deploy MPC for projects in a quick manner
- Demonstrates a variety of applications that are solved using MATLAB® and Simulink®
- Bridges the gap in providing a number of realistic problems with very hands-on training
- Provides MATLAB® and Simulink® code solutions. This includes nonlinear plant models that the reader can use for other projects and research work
- Presents application problems with solutions to help reinforce the information learned
About the authors:
Dr. Nassim Khaled has extensive industrial and academic experience in the field of dynamics, controls and IoT solutions. He is the Controls and Engineering Systems Manager at Dover. He is an innovator with more than 30 patents and patent applications in the fields of smart systems and energy. He is the author of “Virtual Reality for Matlab and Simulink Users”. He also has numerous publications in the field of controls and autonomous navigation. Dr. Khaled is a green-belt six-sigma certified. He received the status of “Outstanding Researcher” granted by the U.S Government in 2012. You can contact him by visiting: https://www.practicalmpc.com/
Dr. Bibin Pattel has a Master of Science in Mechanical engineering and 10 years of industrial experience in the field of Controls. He is currently working as a Technical Advisor with KPIT Infosystems Inc, USA. Bibin has worked on vehicle, after treatment, air-handling and engine modelling and controls and on board diagnostic development. He is an expert in Matlab and Simulink as well as Hardware and Software solutions for the control of diesel engines. He has 6 patents applications and published 4 journal and conference papers.
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