**Preface**

A complex system is a system composed of many components (elements) which interrelate with each other, and the collective behaviour of these elements results in the emergence of properties that can hardly, if not at all, be inferred from properties of the elements alone.

Complex systems are intrinsically complicated, and difficult to model or control, due to inherent nonlinearity, coupling, chaotic behaviour, uncertainty, embedded stochastic patterns and parameter sensitivities, under multi-scale responses. Complex systems are pervasive in today's world, yet visions into their unanticipated behaviour remains limited, severely reducing the ability to design and control them for particular desired responses.

The general approaches that can be used to simplify complex systems are: (i) divide and conquer, (ii) shift complexity, (iii) simplified commands, and (iv) structural methodologies.

From an application and technological development perspective, the engineering world is in great need of methods and tools for identifying complex systems' behaviour, as well as tractable methods for their design and analysis. Therefore, any new methods of computation and/or processing (e.g. using novel machine learning-based operations) that leads to successfully applying automation, automated control and optimized energy efficiency will be appreciated by academia and engineering communities.

This book covers some significant impacts from recent research contributions, in both the private and public sectors of engineering complex systems, in which automation, control, energy analysis, energy modelling, energy management, and energy efficiency are outcomes. This book is also a collection of eleven different crucial complex systems arranged in three groups: Transportation Systems, Building Systems, and Manufacturing Systems, which are focused on applied engineering problems.

The first group; Transportation Systems complex challenges, which are: capacity, transfer, reliability and integration to reduce time and energy consumption. Chapter 1, 2, and 3 cover the automated controls for operating electric vehicles, including hybrid electric vehicle and plug-in hybrid electric vehicles and charging infrastructure, as part of transportation systems. Chapter 4 provides a study of an active controller for four-wheeled steering vehicles. Chapter 5 investigates an energy consumption model for multi-train urban rail transit systems. Chapter 6 explores a switching coordination of multi-agent systems for transportation networks, which permits rapid and safe.

The second group; Building Systems complex challenges, which are: mechanical systems (involving topics of energy consumption, heating, air conditioning, boiler systems automatic temperature controls) and electrical systems (such as: electrical power service). Chapter 7 and 8 extensively studies energy reductions of buildings through the modelling and control of lighting and air-conditioning systems. Chapter 9 introduces a new designing method of an intelligent Fuzzy Cognitive Map (FCM) controller for the energy reduction of the building systems.

The third group; Manufacturing Systems complex challenges, which are: improving production processes, control and optimized energy efficiency. Chapter 10 begins with an extensive article on how to reduce energy consumption in the carbon fiber production industry. Chapter 11 is a comprehensive review of some robust speed control methods of Permanent Magnet Synchronous Motors (PMSM) for industrial automation applications.

Due to the nonlinearity and robustness of complex systems, the ability to apply automation and automated control with minimal human assistance would provide us with a great ability and motivation to control dynamic energy systems. Artificial intelligence may be defined as the branch of computer science that is concerned with the automation of intelligent behaviour. Artificial intelligence techniques learn about the data they are trained on, and learning algorithms are designed to generalize from that data.

Artificial intelligence in automation, uses intelligent control techniques such as fuzzy logic systems, neural networks, machine learning, and optimization algorithms, which are deployed to achieve energy efficiency in many spheres of engineering complex systems. The positive aspects of intelligent controllers are their simplicity, having the benefit of independence from models, not requiring extensive knowledge of the problem field, reduced cost and environmental impact, and solvability for several energy reduction strategies of engineering systems.

> **Hamid Khayyam** *Editor*
