Complex systems are composed of interconnected parts, and the whole system shows one or more properties that are not much different from those of a single part. The complexity of the system can be one of the following: disordered complexity or grouped complexity. Essentially, an unordered complexity system contains a large number of parts; an organized complex system is the nature of a subject system (probably with only a limited number of parts). Examples of models for the complexity of complex systems include ant colonies, human economics, social structures, climate, the nervous system, cells and living organisms, modern energy, or communication infrastructure. In fact, many systems of interest to humans are complex systems.

Many fields of natural sciences, mathematics, and social sciences study complex systems. The special fields of interdisciplinary research on complex systems include system theory, complexity theory, system ecology, and cybernetics. It can be seen that studying complex systems is of great significance to all aspects of human beings. Learning is a basic feature of all intelligent behaviours. Recently, various learning methods have been used for the control of complex dynamical systems, for example, composite learning, concurrent learning, and quantization interval learning. It is well known that system uncertainties usually exist in most real-world systems. Thus, exploring robust control methods for complex systems is of interest.

The aim of this Special Issue is to collect the latest research results on the relevant topics of dynamic analysis, learning, and robust control for complex systems. Authors are invited to present new complex systems, learning or control of complex chaotic systems, complex circuits, and complex networks that can bring new information about relevant theories and techniques of complex systems. This Issue encourages scholars to submit their original research or reviews, including simulation studies, algorithm design, experiments, and applications of advanced control and learning techniques for complex systems.

Potential topics include but are not limited to the following:

• Dynamics analysis of complex systems
• Design of new complex systems
• Adaptive control of complex systems
• Learning from complex systems
• Composite learning control of complex dynamical systems
• Adaptive fuzzy control of complex dynamical systems
• Stability analysis of complex systems
• Fractional-order complex systems
• Fractional-order control of complex systems