Learning-Based Control and Nonlinear Optimization: Theory, Models, Algorithms, and Applications

Dear Colleagues,

In recent years, learning and control methods have been applied in many applications, such as industrial process, robotics, aerospace, fault diagnosis and estimation, autonomous driving, and physics, just to name a few. Applying these methods has dramatically improved the automation and intelligence of real systems and applications. With the rapid development of modern industry, determining how to further develop advanced learning and control algorithms and apply them to actual applications has important research significance and prospects. One key direction is leveraging nonlinear optimization techniques to empower feedback control for achieving satisfactory system performance. Usually, engineering-oriented optimization problems can be formulated as complex combinatorial optimization problems. However, most of these problems are NP-hard so that it may not be feasible to provide satisfactory solutions via conventional optimization methods for real-time control applications. In addition, it is challenging for existing approaches to adjust their policies to accommodate time-varying environments and to achieve stable long-term performances. In order to address these challenges, this Special Issue aims to bring together new optimization strategies and advances for approaching feedback control with satisfactory performance. Topics of interest include but are not limited to contributions on:

1. Advanced strategies in nonlinear optimization;
2. Advanced strategies in representation learning;
3. Advanced dictionary learning for deep learning;
4. Applications using nonlinear representation learning;
5. Sparse representation models for feedback control;
6. Quadrotor tracking control system;
7. Visual data processing and control;
8. Dynamic modeling and control of complex systems;
9. Theory and control systems with artificial intelligence, using neural networks and machine learning;
10. Fault diagnosis via learning techniques;
11. Equipment management and its life estimation;
12. Sparse representation for control;
13. The fusion of machine learning and dynamic optimization and control.

Dr. Zhenni Li
Dr. Kan Xie
Dr. Zhigang Ren
Dr. Ci Chen
Guest Editors

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