Distributionally Robust Stochastic Model Predictive Control (DR-SMPC)是一种控制方法,旨在处理存在不确定性的系统。与传统的随机优化方法不同,DR-SMPC关注的是通过最小化针对概率分布的鲁棒性损失来提高控制性能。该方法考虑系统状态和输入的概率分布的不确定性,以确保控制器在各种可能的分布下都能表现良好。通过将...
3 参考文献 [1]T. Br¨udigam, M. Olbrich, M. Leibold, and D. Wollherr. Combining stochastic and scenario model predictive control to handle target vehicle uncertainty in autonomous driving. In 21st IEEE International Conference on Intelligent Transportation Systems, Maui, USA, 2018. 部分理论...
该方法利用场景削减技术,进一步抑制风光出力不确定性,并采用自适应变权重方法自动调整多目标权重系数.文章比较了方法改进前,后以及梯级水电站数量对互补系统优化调度结果的影响.系统仿真表明,所提自适应(Stochastic Model Predictive Control,SMPC)方法,可有效抑制风电,光伏的不确定性与波动性,提高水电出力的可靠性与稳定...
The repository is used for presenting the code developed as part of the Adis Hodzic and Casper Knudsens Master Thesis, titled: Stochastic Model Predictive Control of Combined Sewer Overflows in Sanitation Networks control lab mpc sewerpipe smpc urban-drainage-systems ccmpc sewer Updated May 24, ...
A novel stochastic model predictive control (SMPC) scheme is proposed for automotive scenes based on high-performance and practical motion state prediction... X Qiao,L Zheng,JZH Li - 《International Journal of Control Automation & Systems》 被引量: 0发表: 2024年 Variable time-scale power schedul...