eess.SY(2025-04-07)

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支柱一:机器人控制 (Robot Control) (4) 支柱二:RL算法与架构 (RL & Architecture) (2)

🔬 支柱一:机器人控制 (Robot Control) (4 篇)

#题目一句话要点标签🔗
1 Real-Time Model Predictive Control for the Swing-Up Problem of an Underactuated Double Pendulum 提出基于实时非线性模型预测控制的双摆起摆与稳定控制方法 MPC model predictive control
2 Bilinear Data-Driven Min-Max MPC: Designing Rational Controllers via Sum-of-squares Optimization 提出一种基于数据的双线性Min-Max MPC方法,通过平方和优化设计鲁棒控制器。 MPC model predictive control
3 Reducing the Communication of Distributed Model Predictive Control: Autoencoders and Formation Control 提出基于自编码器的分布式模型预测控制通信量缩减方法,应用于机器人编队控制。 model predictive control
4 Trajectory Optimization of Stochastic Systems under Chance Constraints via Set Erosion 提出基于集合侵蚀的随机系统轨迹优化方法,解决机会约束下的轨迹规划问题 trajectory optimization

🔬 支柱二:RL算法与架构 (RL & Architecture) (2 篇)

#题目一句话要点标签🔗
5 Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning 提出物理信息模块化神经网络,用于深度强化学习优化建筑控制 reinforcement learning deep reinforcement learning DRL
6 Multi-Agent Deep Reinforcement Learning for Multiple Anesthetics Collaborative Control 提出基于价值分解的多智能体深度强化学习,用于多麻醉剂协同控制 reinforcement learning deep reinforcement learning

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