Purpose–The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller(ENMPC)by linearizing the trajectory with Quantum-behaved Pigeon-Inspired Optimization(QPIO).Design/methodology/appro...Purpose–The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller(ENMPC)by linearizing the trajectory with Quantum-behaved Pigeon-Inspired Optimization(QPIO).Design/methodology/approach–The paper deduces the nonlinear model of the quadrotor and uses the ENMPC to track the trajectory.Since the ENMPC has high demand for the state equation,the trajectory needed to be differentiated many times.When the trajectory is complicate or discontinuous,QPIO is proposed to linearize the trajectory.Then the linearized trajectory will be used in the ENMPC.Findings–Applying the QPIO algorithm allows the unequal distance sample points to be acquired to linearize the trajectory.Comparing with the equidistant linear interpolation,the linear interpolation error will be smaller.Practical implications–Small-sized quadrotors were adopted in this research to simplify the model.The model is supposed to be accurate and differentiable to meet the requirements of ENMPC.Originality/value–Traditionally,the quadrotor model was usually linearized in the research.In this paper,the quadrotormodel waskept nonlinear and the trajectorywill be linearizedinstead.Unequaldistance sample points were utilized to linearize the trajectory.In this way,the authors can get a smaller interpolation error.This method can also be applied to discrete systems to construct the interpolation for trajectory tracking.展开更多
针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud contr...针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud control system,IPPCCS)解决方案.基于智能电厂云控制系统,针对绿色能源发电波动性强、抗扰能力差的问题,利用机器学习算法对采集到的风电、光伏输出功率进行短时预测,获知未来风、光机组功率输出情况.在云端使用经济模型预测控制(Economic model predictive control,EMPC)算法,通过实时滚动优化得到水轮机组的功率预测调度策略,保证绿色能源互补发电的鲁棒性,充分消纳风、光两种能源,减少水轮机组启停和穿越振动区次数,在为用户清洁、稳定供电的同时降低了机组寿命损耗.最后,一个区域云数据中心的供电算例表明了本文方法的有效性.展开更多
文摘Purpose–The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller(ENMPC)by linearizing the trajectory with Quantum-behaved Pigeon-Inspired Optimization(QPIO).Design/methodology/approach–The paper deduces the nonlinear model of the quadrotor and uses the ENMPC to track the trajectory.Since the ENMPC has high demand for the state equation,the trajectory needed to be differentiated many times.When the trajectory is complicate or discontinuous,QPIO is proposed to linearize the trajectory.Then the linearized trajectory will be used in the ENMPC.Findings–Applying the QPIO algorithm allows the unequal distance sample points to be acquired to linearize the trajectory.Comparing with the equidistant linear interpolation,the linear interpolation error will be smaller.Practical implications–Small-sized quadrotors were adopted in this research to simplify the model.The model is supposed to be accurate and differentiable to meet the requirements of ENMPC.Originality/value–Traditionally,the quadrotor model was usually linearized in the research.In this paper,the quadrotormodel waskept nonlinear and the trajectorywill be linearizedinstead.Unequaldistance sample points were utilized to linearize the trajectory.In this way,the authors can get a smaller interpolation error.This method can also be applied to discrete systems to construct the interpolation for trajectory tracking.
文摘针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题,基于云控制系统理论,以智能电厂为研究对象,本文提出了智能电厂云控制系统(Intelligent power plant cloud control system,IPPCCS)解决方案.基于智能电厂云控制系统,针对绿色能源发电波动性强、抗扰能力差的问题,利用机器学习算法对采集到的风电、光伏输出功率进行短时预测,获知未来风、光机组功率输出情况.在云端使用经济模型预测控制(Economic model predictive control,EMPC)算法,通过实时滚动优化得到水轮机组的功率预测调度策略,保证绿色能源互补发电的鲁棒性,充分消纳风、光两种能源,减少水轮机组启停和穿越振动区次数,在为用户清洁、稳定供电的同时降低了机组寿命损耗.最后,一个区域云数据中心的供电算例表明了本文方法的有效性.
