This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,...This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.展开更多
Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency.In this paper,first,the coordinated taxiing path planning problem is t...Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency.In this paper,first,the coordinated taxiing path planning problem is transformed into a centralized optimal control problem where collision-free conditions and mechanical limits are considered.Since the formulated optimal control problem is of large state space and highly nonlinear,an efficient hierarchical initialization technique based on the Dubins-curve method is proposed.Then,a model predictive controller is designed to track the obtained reference trajectory in the presence of initial state error and external disturbances.Numerical experiments demonstrate that the proposed“offline planningþonline tracking”framework can achieve efficient and robust coordinated taxiing planning and tracking even in the presence of initial state error and continuous external disturbances.展开更多
“双碳”背景下要求直流炉供热机组具备快速变负荷能力。对此,提出一种基于线性时变模型预测控制(linear time-varying model predictive control,LTV-MPC)的电热协调变负荷策略,可同时利用锅炉蓄热和热网蓄热以提高变负荷速率。首先,...“双碳”背景下要求直流炉供热机组具备快速变负荷能力。对此,提出一种基于线性时变模型预测控制(linear time-varying model predictive control,LTV-MPC)的电热协调变负荷策略,可同时利用锅炉蓄热和热网蓄热以提高变负荷速率。首先,对热负荷信号偏差进行积分从而建立等效热负荷模型,并将其作为预测模型的被控量之一;然后,以负荷快速跟踪、机组运行稳定以及供热及时补偿作为MPC滚动优化的目标,进而在线求解每个时刻的最优控制律并作用于机组,此外,显式处理了机组运行约束,确保供热抽汽流量变化不会影响低压缸运行稳定性;最后,基于某350 MW机组进行仿真验证,结果表明,该策略能够实现对5%Pe/min变负荷指令的精准跟踪,且相较于基于PID的电热协调变负荷策略,热负荷恢复时间缩短26%。仿真结果验证了所提策略在提升供热机组快速变负荷能力方面的优越性。展开更多
This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration s...This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation.The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed,stable end body's libration,and overall control input via model predictive control.The transfer period is divided into several sections to reduce computational burden.The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation.Numerical results reveal that the optimized LD-ASF results in higher transportation speed,stable end body's libration,lower thrust fuel consumption,and more flexible optimization space than the classic analytical speed function.展开更多
针对定速地形跟随飞行全局离线规划方案数据存储量大、机动飞行油耗大等问题,提出基于级联模型预测控制(cascaded model predictive control,CMPC)的能量协调在线轨迹规划与跟踪方案。首先,利用飞行器纵向质点运动学模型设计模型预测控...针对定速地形跟随飞行全局离线规划方案数据存储量大、机动飞行油耗大等问题,提出基于级联模型预测控制(cascaded model predictive control,CMPC)的能量协调在线轨迹规划与跟踪方案。首先,利用飞行器纵向质点运动学模型设计模型预测控制(model predictive control,MPC)在线轨迹规划器。从能量分配原理出发确定速度变化规律,对于飞行时总能量不变而导致能量分配不合理的情况,引入虚拟控制量实现总能量动态调节,完成能量协调策略设计。其次,引入地形粗糙度概念描述地形起伏程度,基于此提出规划器自适应时域方案,对于不同地形实现预测时域动态调节。结合MPC轨迹跟踪控制器,并利用真实地形数据进行仿真实验。实验结果表明,所提方案可在与全局离线规划方案航迹差异不大的前提下,实现在线轨迹规划,显著降低油耗,提高航程极限,完成复杂地形的机动飞行任务。展开更多
为实现混合动力两栖车综合效率最优,提出一种功率协调预测控制策略。该策略旨在协同优化能量管理策略与车速控制策略之间的耦合关系。针对车速预测模型失配的问题,提出利用极限学习机进行实时误差预测,并通过预测值进行预测模型校正。...为实现混合动力两栖车综合效率最优,提出一种功率协调预测控制策略。该策略旨在协同优化能量管理策略与车速控制策略之间的耦合关系。针对车速预测模型失配的问题,提出利用极限学习机进行实时误差预测,并通过预测值进行预测模型校正。设计模型预测控制器实现能量管理与车速控制的实时优化控制,并通过仿真进行验证。研究结果表明:提出的策略相较于传统的基于模型预测控制的能量管理策略能够降低等效燃油消耗、荷电状态(State of Charge,SOC)标准差、母线电压标准差和电池容量衰退,降低幅度分别为9.35%、59.63%、15.79%和45.33%;通过有无模型校正的功率协调预测控制对比,表明通过模型校正可实现等效燃油消耗、SOC标准差、母线电压标准差和电池容量衰退分别降低6.95%、25.91%、13.46%和24.07%,体现了所提出的基于极限学习机模型校正的功率协调预测控制在提升燃油经济性、维持电气系统稳定性和降低电池损耗方面的优越性。展开更多
基金“National Science and Technology Council”(NSTC 111-2221-E-027-088)。
文摘This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.
文摘Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency.In this paper,first,the coordinated taxiing path planning problem is transformed into a centralized optimal control problem where collision-free conditions and mechanical limits are considered.Since the formulated optimal control problem is of large state space and highly nonlinear,an efficient hierarchical initialization technique based on the Dubins-curve method is proposed.Then,a model predictive controller is designed to track the obtained reference trajectory in the presence of initial state error and external disturbances.Numerical experiments demonstrate that the proposed“offline planningþonline tracking”framework can achieve efficient and robust coordinated taxiing planning and tracking even in the presence of initial state error and continuous external disturbances.
基金funded by the National Natural Science Foundation of China(12102487)Basic and Applied Basic Research Foundation of Guangdong Province,China(2023A1515012339)+1 种基金Shenzhen Science and Technology Program(ZDSYS20210623091808026)the Discovery Grant(RGPIN-2024-06290)of the Natural Sciences and Engineering Research Council of Canada。
文摘This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation.The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed,stable end body's libration,and overall control input via model predictive control.The transfer period is divided into several sections to reduce computational burden.The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation.Numerical results reveal that the optimized LD-ASF results in higher transportation speed,stable end body's libration,lower thrust fuel consumption,and more flexible optimization space than the classic analytical speed function.
文摘为实现混合动力两栖车综合效率最优,提出一种功率协调预测控制策略。该策略旨在协同优化能量管理策略与车速控制策略之间的耦合关系。针对车速预测模型失配的问题,提出利用极限学习机进行实时误差预测,并通过预测值进行预测模型校正。设计模型预测控制器实现能量管理与车速控制的实时优化控制,并通过仿真进行验证。研究结果表明:提出的策略相较于传统的基于模型预测控制的能量管理策略能够降低等效燃油消耗、荷电状态(State of Charge,SOC)标准差、母线电压标准差和电池容量衰退,降低幅度分别为9.35%、59.63%、15.79%和45.33%;通过有无模型校正的功率协调预测控制对比,表明通过模型校正可实现等效燃油消耗、SOC标准差、母线电压标准差和电池容量衰退分别降低6.95%、25.91%、13.46%和24.07%,体现了所提出的基于极限学习机模型校正的功率协调预测控制在提升燃油经济性、维持电气系统稳定性和降低电池损耗方面的优越性。
