A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and th...A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.展开更多
现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power gene...现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power generation),针对数据中心冷热电联产系统,联合控制供电子系统和制冷子系统,优化用电成本、碳排放量和能效。DeepCCHP结合长、短期时间序列网络和深度强化学习方法对联合优化问题进行求解,实现前摄式的联合控制发电设备和制冷设备。在基于Trnsys软件的仿真环境中,通过阿里巴巴数据中心集群数据的训练和验证。实验结果表明,与基准算法相比,DeepCCHP算法可以节省最高40%的成本和28%的碳排放量,且能够在能源成本、碳排放和能效三者之间取得更好的折中与平衡。展开更多
基金This Project was supported by the National Natural Science Foundation of China (60374037 and 60574036)the Opening Project Foundation of National Lab of Industrial Control Technology (0708008).
文摘A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.
基金Supported by the National Creative Research Groups Science Foundation of P.R. China （NCRGSFC： 60421002） and National High Technology Research and Development Program of China （863 Program） （2006AA04 Z182）
文摘现有的数据中心节能降碳优化方法没有综合考虑碳足迹涉及的能源输入、生产耗能以及废余利用等环节的耦合性,难以实现系统性节能降碳。为此,提出了一种基于深度强化学习的优化算法DeepCCHP(deep combined cooling,heating and power generation),针对数据中心冷热电联产系统,联合控制供电子系统和制冷子系统,优化用电成本、碳排放量和能效。DeepCCHP结合长、短期时间序列网络和深度强化学习方法对联合优化问题进行求解,实现前摄式的联合控制发电设备和制冷设备。在基于Trnsys软件的仿真环境中,通过阿里巴巴数据中心集群数据的训练和验证。实验结果表明,与基准算法相比,DeepCCHP算法可以节省最高40%的成本和28%的碳排放量,且能够在能源成本、碳排放和能效三者之间取得更好的折中与平衡。
