**Preface to "Complex Dynamic System Modelling, Identification and Control"**

Systems are naturally or purposely formed with functional components and connection structures. Their complicities are induced by nonlinearity, dynamics, time delay, uncertainties, disturbances, irreversible processes, and those characteristics that are generally explained in other literature. Modeling represents the innate tendency of humans to find rules or mechanisms that govern phenomena (a process/plant in a human-made system or a natural system, such as the Earth's global climate, organisms, and the human brain). This is generally consistent with the journal titled *Entropy*, as the idea of entropy provides a mathematical way to encode/model the intuitive notion of which processes are obviously complex due to their irreversible characteristics, even though they would not violate the fundamental law of the conservation of energy. There are two predominant approaches to establishing models: principle-based (e.g., information theory, statistic physics, statistical mechanics, etc.) analytical equations and data (measured and simulated) driven input/output fitted sets of regression numerical polynomials (most commonly called identification). Control is a way to improve a system's behavior/performance by adding additional functional components and revising the system structure to form a closed-loop framework with adaptation and robustness against uncertainties. Accordingly, modeling, identification, and control (MIC) is a cross-discipline from all engineering (human-made) systems to all natural scientific research discoveries. This reprint encourages those emerging insights and approaches to provide concise/effective solutions in complex dynamic system modeling, identification, and control. The philosophy embedded in the SI is to seek simplicity (solutions) from complicity (problems). This reprint is a forum for presenting new and improved insight, methodologies, and techniques of MIC for complex systems that are challenging for research and (potentially) significant for a wide range of applications in real-world natural and engineering domains. Fundamentally, the papers should justify why the works have not been undertaken by other colleagues and what the bottleneck issues have been for such research progression and applications.

As the editors, we hope that the chapters in this reprint will stimulate further research in complex system modeling and utilize them in real-world applications. We hope that this reprint, covering so many different aspects, will be of value to all readers.

We would like to also thank the reviewers for their diligence in reviewing the chapters.

### **Quanmin Zhu, Giuseppe Fusco, Jing Na, Weicun Zhang, and Ahmad Taher Azar** *Editors*
