Browsing by Author "Wang, Cong"

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    Learning-Based Propulsion Control for Amphibious Quadruped Robots With Dynamic Adaptation to Changing Environment
    (IEEE, 2023-12-01) Yao, Qingfeng; Meng, Linghan; Zhang, Qifeng; Zhao, Jing; Pajarinen, Joni; Wang, Xiaohui; Li, Zhibin; Wang, Cong; Department of Electrical Engineering and Automation; Robot Learning; CAS - Shenyang Institute of Automation; Heriot-Watt University; University College London
    This letter proposes a learning-based adaptive propulsion control (APC) method for a quadruped robot integrated with thrusters in amphibious environments, allowing it to move efficiently in water while maintaining its ground locomotion capabilities. We designed the specific reinforcement learning method to train the neural network to perform the vector propulsion control. Our approach coordinates the legs and propeller, enabling the robot to achieve speed and trajectory tracking tasks in the presence of actuator failures and unknown disturbances. Our simulated validations of the robot in water demonstrate the effectiveness of the trained neural network to predict the disturbances and actuator failures based on historical information, showing that the framework is adaptable to changing environments and is suitable for use in dynamically changing situations. Our proposed approach is suited to the hardware augmentation of quadruped robots to create avenues in the field of amphibious robotics and expand the use of quadruped robots in various applications.
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    Robust Coordinated Planning of Multi-Region Integrated Energy Systems With Categorized Demand Response
    (IEEE, 2024-07-23) Dong, Yingchao; Li, Zhengmao; Zhang, Hongli; Wang, Cong; Zhou, Xiaojun; Department of Electrical Engineering and Automation; Multi-energy System Planning and Operation; Xinjiang University; Central South University
    In this paper, categorized demand response (DR) programs are proposed to address the coordinated planning problem in multi-region integrated energy systems (MRIESs). The categorized DR programs comprise a discrete manufacturing production model for industrial areas, a real-time pricing-based DR program for commercial areas, and diverse operational tasks for various electrical appliances in residential areas. Subsequently, the detailed DR model is leveraged to minimize the operation cost and gas emissions in a renewable-integrated MRIES considering the uncertainties from wind and solar power. Then, a flexible adjustable robust optimization (FARO) approach is presented to deal with all uncertainty sources. The FARO approach aims to ensure the safe operation of the MRIES against any uncertainty while meeting predefined performance objectives. Furthermore, a bi-level solution algorithm is designed by combining the stochastic dichotomy method and the column-and-constraint generation (C&CG) algorithm to solve our coordinated planning model. Finally, case studies are conducted on a practical MRIES in Changsha, China. Experimental results indicate the effectiveness of the categorized DR programs in adjusting allocable resources to maximize holistic system profits. Besides, compared to the commonly used information-gap decision theory (IGDT) method, our FARO approach can maintain the optimality of the solution while reducing conservatism.