In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driv...In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper.A probabilistic system is constructed by considering the variance of states.The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps.First,the cost function is separated into mean and variance components.The mean component is calculated online,whereas the variance component can be calculated offline.Second,Cantelli’s inequality is adopted for the deterministic reformulation of constraints.Consequently,the original probabilistic problem is transformed into a quadratic programming problem.To validate the feasibility and effectiveness of the proposed control method,we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario.The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.展开更多
为了达到能量转换最优化和减小机械结构的疲劳负荷的要求,基于双馈感应发电机风能转换系统建立了数学模型,提出一种双频环滑模预测优化控制方法。该方法采用双频环多目标结构,低频环引入基于ARMA(autoregressive and moving average mod...为了达到能量转换最优化和减小机械结构的疲劳负荷的要求,基于双馈感应发电机风能转换系统建立了数学模型,提出一种双频环滑模预测优化控制方法。该方法采用双频环多目标结构,低频环引入基于ARMA(autoregressive and moving average model)模型预测后的风速低频分量,采用PI控制对应于最优叶尖速度以保证其工作点运行在最优控制特性曲线上;高频环引入风速的湍流分量,将预测控制与滑模控制相结合实现系统的动态优化。仿真结果表明:双频环滑模预测控制有效避免了不确定性对系统的影响,实现了部分负荷状态下的最优控制特性跟踪,减少了控制输入量的变化量,降低了机械疲劳,保证了系统的优化稳定运行。展开更多
基金the Science and Technology Commission of Shanghai Municipality(No.19511103503)。
文摘In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper.A probabilistic system is constructed by considering the variance of states.The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps.First,the cost function is separated into mean and variance components.The mean component is calculated online,whereas the variance component can be calculated offline.Second,Cantelli’s inequality is adopted for the deterministic reformulation of constraints.Consequently,the original probabilistic problem is transformed into a quadratic programming problem.To validate the feasibility and effectiveness of the proposed control method,we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario.The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.
文摘为了达到能量转换最优化和减小机械结构的疲劳负荷的要求,基于双馈感应发电机风能转换系统建立了数学模型,提出一种双频环滑模预测优化控制方法。该方法采用双频环多目标结构,低频环引入基于ARMA(autoregressive and moving average model)模型预测后的风速低频分量,采用PI控制对应于最优叶尖速度以保证其工作点运行在最优控制特性曲线上;高频环引入风速的湍流分量,将预测控制与滑模控制相结合实现系统的动态优化。仿真结果表明:双频环滑模预测控制有效避免了不确定性对系统的影响,实现了部分负荷状态下的最优控制特性跟踪,减少了控制输入量的变化量,降低了机械疲劳,保证了系统的优化稳定运行。