Aims and Scope

The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material.

Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include:

    Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers
    Nonlinear, Robust and Intelligent Adaptive Controllers
    Linear and Nonlinear Multivariable System Identification and Estimation
    Identification of Linear Parameter Varying, Distributed and Hybrid Systems
    Multiple Model Adaptive Control
    Adaptive Signal processing Theory and Algorithms
    Adaptation in Multi-Agent Systems
    Condition Monitoring Systems
    Fault Detection and Isolation Methods
    Fault Detection and Isolation Methods
    Fault-Tolerant Control (system supervision and diagnosis)
    Learning Systems and Adaptive Modelling
    Real Time Algorithms for Adaptive Signal Processing and Control
    Adaptive Signal Processing and Control Applications
    Adaptive Cloud Architectures and Networking
    Adaptive Mechanisms for Internet of Things
    Adaptive Sliding Mode Control

Readership

Practicing electronic, electrical, mechanical, systems, sensor, instrumentation, chemical engineers · researchers in advanced control systems and signal processing · applied mathematicians · computer scientists.

Keywords

Adaptive control, adaptive signal processing, system identification, signal processing, intelligent control, learning systems, estimation, filtering, fault detection, multiple model adaptive, distributed parameter systems, biological control systems, switching systems, logic, journal, online journal, Wiley Online Library

Special Issue on Big Data-Driven Modeling, Prediction, Optimization, and Control
Submission Date: 2026-02-28

With the rapid advancements in big data technologies, the integration of data-driven approaches has fundamentally transformed the way we model, predict, optimize, and control complex systems across diverse sectors. From improving operational efficiency and real-time decision-making to enabling intelligent automation, big data methods have become essential in addressing challenges and enhancing performance in fields such as transportation, manufacturing, healthcare, energy systems, signal processing, and more.

The increasing availability of high-volume, high-velocity, and heterogeneous data sources has opened new opportunities for the development of sophisticated, data-driven models that go beyond traditional modeling paradigms. This special issue will highlight the latest theoretical advancements, algorithms, and practical applications of data-driven methodologies in system modeling, prediction, optimization, and control. The emphasis is on integrating novel machine learning techniques, including generative AI models, reinforcement learning, and knowledge representation frameworks such as knowledge graphs, to address complex, large-scale, dynamic real-world problems.

As we move towards more adaptive and intelligent systems, the ability to process and analyze large-scale data efficiently and derive actionable insights will play a pivotal role in advancing next-generation optimization and control strategies. The goal of this special issue is to provide a comprehensive platform for researchers to showcase how big data can enable intelligent decision-making, improve system performance, and optimize control mechanisms across various domains.

Topics for this call for papers include but are not restricted to:

    Data-driven modeling for complex dynamic systems
    Integration of data-driven models with physical models (hybrid modeling)
    Advanced data representation techniques for prediction
    Reinforcement learning for real-time optimization and control
    Explainable AI for decision-making, driven by large language models and knowledge graphs
    Applications of Generative AI models in improving decision-making quality and efficiency
    Digital twin technologies for modeling, prediction, optimization, and control in industrial applications
    Ethical and social implications of data-driven optimization and control

Guest Editors:

Prof. Ling Jian
China University of Petroleum
China

Dr. Yuanchuan Jiang
Hefei University of Technology
China

Prof. Chuanhou Gao
Zhejiang University
China

Keywords: big data; computer science; reinforcement learning