New Sights of Intelligent Robust Control in Aerospace

Dear Colleagues,

This Special Issue focuses on innovative advancements and research developments in intelligent, robust control within the aerospace sector. Intelligent, robust control refers to using artificial intelligence and advanced control techniques to accomplish the control objectives for uncertain systems without resorting to an accurate system model. It collects information from actual data and experience and focuses on utilizing data generated during the operation of an uncertain system to learn its behavior and performance, enabling the formulation of more adaptive and intelligent control strategies. Intelligent, robust control has the advantages of effectively handling complex systems with multiple variables, nonlinear behaviors, and uncertain features, optimizing control strategies to achieve specific objectives, and continuously improving performance by learning from data and past experiences. Typical intelligent, robust control includes machine learning, fuzzy logic control, reinforcement learning, model predictive control, and genetic algorithms. In recent years, significant progress has been made in learning-based algorithms. Learning-based algorithms can be combined with robust control to realize adaptive robust control, which plays a role in aerospace control. However, intelligent, robust control and its practical applications in aerospace remain an open problem. Potential topics of interest include, but are not limited to, the following:

• Intelligent, robust control of unmanned aerial vehicles;
• Learning-based control algorithms on guidance and navigation of aerospace systems;
• Decision-making and model-free control of complex, uncertain systems;
• Adaptive and robust control for complex systems with data-driven control algorithms;
• Fault detection and fault tolerant control for complex systems with intelligent, robust control;
• Learning-based enabled attack-resilient control of aerospace systems against attacks;
• Robust control of uncertain, complex systems in the presence of actuator saturation;
• Path planning of aerospace vehicles with data-driven control algorithms;
• Task allocation and cooperative execution for multiple unmanned systems with learning-based control algorithms;
• Robust autonomous driving in complex traffic environment.

Dr. Hao Liu
Dr. Zhan Li
Dr. Yuxin Liao
Dr. Zhong Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• robust control
• intelligent control
• learning-based control
• aerospace control
• unmanned aerial systems