Calls for papers
International Journal of Modelling, Identification and Control
Special Issue on: "Recent Advances on Learning-Based Control: Theory and Application"
Prof. Jun Chen, Oakland University, USA
Prof. Xiangyu Meng, Louisiana State University, USA
Prof. Weinan Gao, Florida Institute of Technology, USA
Learning-based control has been considered as an effective control technique for systems with unknown dynamics/disturbances. Conventional learning-based control has been proven and applied to both continuous-time and discrete-time systems, as well as large-scale interconnected systems. Recently there is a burst of reinforcement learning research, which relies on the interaction between a decision-making agent and its environment to achieve long-term goals. Both model-based and model-free reinforcement learning has been widely studied and applied to various applications. Another recent research direction is to combine both conventional and advanced learning-based control with other control techniques such as optimal control and adaptive control, to provide a systematic control solution with theoretical performance guarantees. This special issue will focus on recent research and development in learning-based control and its applications including but not limited to automotive systems, energy systems, real-time embedded control, etc.Subject Coverage
Suitable topics include, but are not limited, to the following:
- Reinforcement learning
- Iterative learning control
- Gaussian process
- Adaptive control
- Adaptive estimation
- System Identification
- Cooperative robotics
- Intelligent transportation systems
- Model-based and model free RL
- Distributed control
- Model predictive control
- Optimal control
- Stochastic control
- Time varying systems
- Connected and autonomous vehicles
- Electric vehicles
- Power and energy systems
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).
All papers are refereed through a peer review process.
All papers must be submitted online. To submit a paper, please read our Submitting articles page.
Manuscripts due by: 30 June, 2022
Notification to authors: 31 October, 2022
Final versions due by: 15 January, 2023