Introductory Chapter: Model-Based Control Engineering and Its Significance for Automation Technology and Autonomous Systems

1. Introduction

The progress in industrialisation and automation engineering creates a lot of new opportunities in the autonomous systems industry [1]. For example, in the automotive world, the term ‘software-defined’ vehicle is starting to accumulate more discussions [2]. It refers to the future where the vehicle value will be defined more on its software, in addition to the hardware platform.

Across the industries, we are seeing the need for control engineering knowledge continuously in demand. For example, in the maritime world, companies such as Wärtsila and ABB are working on autonomous vessels technologies [3, 4]. Furthermore, similar progress can be seen in aircraft technologies with the advances of flying taxis innovations [5].

For all of these technologies to be delivered safely to the public, it needs to be able to be operating continuously while being exposed to the uncertainties of the systems. Therefore, one of the methods to address the mentioned issues is model-based control engineering adoption to yield a reliable performance of the systems.

2. Model-based control engineering

Model-based control engineering facilitates the development of complex systems using a model-based design approach. Among the notable examples of application in the control engineering is model predictive control (MPC), where it is usually adopted to address the complex behaviour of nonlinear dynamic systems (Figure 1). MPC is a highly studied topic in the control system field. The inclusion of the dynamic models of a plant or process into the formulation aids in improving the control system performance.

Figure 1.

Different types of model predictive control strategies [6, 7, 8].

In typical model-based control engineering works, the plants can be modelled via several means. Once the obtained model has been verified, the controller and algorithm development can be driven with it. These include the validation and verifications of the algorithms with the modelled plant. For example, a vehicle collision avoidance control system development can be simulated with a reliable vehicle model [9, 10]. With the advances of computing devices, a lot of researchers are now integrating model-based applications with machine learning applications for complex autonomous systems [11, 12].

With model-based control engineering, a lot of benefits can be attained. For example, the time-to-market of the product can be reduced by solving the bottleneck subjects between hardware readiness and control system development [13, 14]. Furthermore, a model with good fidelity will allow for simulation-based testing of the control systems in different scenarios during the system testing stage. Despite this, model-based control engineering encounters more challenges too. Among the topics that are related to model-based control engineering are system identifications, modelling, and optimization.

3. Aim of this book

With advances in computational devices, model-based control applications, particularly MPC has started to gain recognition by practitioners in varied industrial sectors such as chemical engineering and industrial plant applications, and recently in robotics and autonomous vehicles, among many others. However, despite the advance and progress, implementation in uncertain environments and highly nonlinear scenario remains challenging.

This book aims to provide model-based control engineering topics high-level discussions to the generic audience with varied use cases. It is hoped to provide a good overview of model-based control engineering for interested readers. As we are seeing more autonomous systems entering the market, the editors of this book believe the discussions made in this book will be useful for the readers’ knowledge of model-based control engineering.

As this book is aimed to be brief and cover different perspectives, the editors are also encouraging interested readers to read more extensive discussions on the theoretical part of model-based control engineering in these works [15, 16, 17].

Acknowledgments

The editors would like to thank all the reviewers and authors involved in the works.