Dear Colleagues, Manuscript Submission Deadline 01 September 2023 https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.frontiersin.org%2Fresearch-topics%2F50724%2Fmodel-free-adaptive-control-of-uncertain-autonomous-systems&data=05%7C01%7Cuai%40engr.orst.edu%7C4a8f5f8f4a6f4c1f02f408db507e4da0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638192275627069674%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=C5ayQ16aQD3fRT%2FTBmLvxPBIsuGfO4GuPpZXYOW6X%2Bo%3D&reserved=0
Guidelines Model-free adaptive control is a promising approach implemented in various complex and uncertain systems such as unmanned air vehicles, humanoid robots, and autonomous cars. Its key feature is that it does not require an accurate analytical model of the real systems and the dynamic environments since the learning occurs through a trial-and-error process or with partial model knowledge. However, this learning process is time consuming, computationally expensive, and the obtained solutions are generally not optimal which may result in failures or poor performances in real time applications. Therefore, an efficient exploration-exploitation adaptive control strategy is necessary to ease these disadvantages. Under-actuated mechanical systems such as underwater vehicles, robot manipulators, legged robots, quadrotors and satellites are ubiquitous advanced engineering problems. They have a number of advantages over the fully actuated ones because they weigh less, consume less energy, require simpler communication tools and fault tolerant approaches. Furthermore, these under-actuated mechanical systems have fewer actuators to be controlled. Note that it is not possible to control each mechanical component of these systems and development of the control approaches, especially the model free intelligent ones, is still a challenging problem. Additionally, external-internal parametric and non-parametric uncertainties such as high frequency measurement noises, unpredictable chaotic dynamics, control signal time delay and actuator saturations being unavoidable in real-time applications result in poor control performance. Topics of interest include, but are not limited to: • Partially model-free adaptive control approaches, • Fully model-free adaptive control approaches, • Uncertainty prediction and uncertainty control approaches, • Machine learning-based intelligent control approaches, • Vision-based intelligent control approaches, • Data-driven intelligent control approaches, • Adaptive control of under-actuated autonomous systems, • Adaptive control of redundant autonomous systems, • Reduced order observer-based adaptive control approaches, • Reduced order adaptive control of uncertain autonomous systems, • Optimization of the challenging control problems for autonomous systems, • Design, development and adaptive control of autonomous systems, • Real time experimental research on adaptive control of autonomous systems. Topic Editors Önder Tutsoy Adana Science and Technology University Adana, Türkiye Aysegul Ucar Firat University Elazig, Türkiye Jamshed Iqbal University of Hull Hull, United Kingdom Omer Saleem Department of Electrical Engineering, National University of Computer and Emerging Sciences Lahore, Pakistan Yong Cheng University of Hull Hull, United Kingdom Participating Journals Manuscripts can be submitted to this Research Topic via the following journals: Frontiers in Robotics and AI Robotic Control Systems
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