Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain sch...Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain schedule. Local LQR control laws and the corresponding maximum control invariant sets can be designed for finite equilibrium points. It is guaranteed that control invariant sets are overlapped each other. The union of the control invariant sets is treated as the terminal constraint set of predictive control. The feasibility and stability of the novel dual-mode model predictive control are investigated with both variable and fixed horizon. Because of the introduction of extended terminal constrained set, the feasibility of optimization can be guaranteed with short prediction horizon. In this way, the size of the optimization problem is reduced so it is computationally efficient. Finally, a simulation example illustrating the algorithm is presented.展开更多
An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the ...An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.展开更多
Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was...Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.展开更多
针对长时间充放电后锂电池模组之间荷电状态(state of charge,SOC)不一致的问题,传统集中式均衡电路存在均衡速度过低的缺陷,以对称式开关阵列、Boost变换器与LC准谐振电路作为均衡主电路,提出了一种基于连续集模型预测控制(continuous ...针对长时间充放电后锂电池模组之间荷电状态(state of charge,SOC)不一致的问题,传统集中式均衡电路存在均衡速度过低的缺陷,以对称式开关阵列、Boost变换器与LC准谐振电路作为均衡主电路,提出了一种基于连续集模型预测控制(continuous control set model predictive control,CCS-MPC)的均衡控制策略。首先,对均衡系统进行建模,构建离散状态空间方程;然后,根据状态方程设计多步模型预测算法,并以SOC预测值和参考值、变换器开关管当前输入和上一时刻输入之间的误差作为价值函数;最后,对价值函数进行二次规划,在线求解出一组控制最优解,并应用于均衡系统,通过动态调整占空比以控制均衡电流的大小。相较于单步预测,多步预测需要考虑被控量在多个周期内保持最优,可以保证在每个均衡周期内均衡器都能输出最优的均衡电流,有效防止均衡器失稳。仿真结果表明,所提模型预测算法实现了各电池组SOC一致,保证了均衡电流的稳定输出,相比常规PI算法缩短了17%的均衡时间。展开更多
为保证全直流风电系统安全并网运行,系统直流电压稳定控制至关重要。全直流风电系统直流电压稳定控制采用比例积分(PI)控制时,PI参数较多且整定繁琐复杂,在非正常运行工况下动态响应速度相对较慢,控制精度不够高。针对以上问题,文章提...为保证全直流风电系统安全并网运行,系统直流电压稳定控制至关重要。全直流风电系统直流电压稳定控制采用比例积分(PI)控制时,PI参数较多且整定繁琐复杂,在非正常运行工况下动态响应速度相对较慢,控制精度不够高。针对以上问题,文章提出一种基于有限控制集模型预测控制(Finite Control Set-Model Predictive Control,FCS-MPC)原理对系统换流器桥臂晶体管开关状态进行控制的系统直流电压稳定控制策略。该策略结合机侧整流器及并网逆变器的电流预测模型,以换流器输出电流为控制变量构造代价函数,以代价函数为优化目标,为避免计算时延导致的控制延时,引入延时补偿提高控制准确度,并引入权重系数实现多目标优化,通过遍历计算产生最优开关组合信号触发换流器。在Matlab/Simulink中建立全直流风电系统的仿真模型,在不同工况下,对所提策略与传统PI控制进行对比仿真分析,仿真结果有效验证了所提控制策略的静态性能及动态性能。展开更多
三有源桥(triple active bridge,TAB)变换器可以灵活连接多个电压等级,在直流微网、混合储能等领域受到广泛关注.模型预测控制是提升TAB变换器动态性能、实现端口解耦的有效策略.然而,由于TAB变换器功率传输模型复杂,采用连续集模型预...三有源桥(triple active bridge,TAB)变换器可以灵活连接多个电压等级,在直流微网、混合储能等领域受到广泛关注.模型预测控制是提升TAB变换器动态性能、实现端口解耦的有效策略.然而,由于TAB变换器功率传输模型复杂,采用连续集模型预测控制,代价函数求解极其困难,工程应用价值低.本文结合有限集模型预测控制,首次提出了一种应用于TAB变换器的移相离散集模型预测控制方法.该方法在每个控制周期内通过对有限个离散化的移相角组合进行局部寻优,继而滑动寻优窗口,最终实现全局最优控制.该方法既保证了优异的动态性能、解耦性能,又避免了复杂的非线性方程组求解,极大增强了控制策略的实用性.同时,本文还分析了移相离散集模型预测控制中权重系数、离散增益、预测范围的优化配置.最后,通过实验验证了所提控制策略的有效性.展开更多
基金Supported by National Natural Science Foundation of China （60504026, 60674041） and National High Technology Research and Development Program of China （863 Program）（2006AA04Z173）.
基金Supported by National Natural Science Foundation of P. R. China (60474051, 60534020)Development Program of Shanghai Science and Technology Department (04DZ11008)the Program for New Century Excellent Talents in Universities of P. R. China (NCET)
文摘Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain schedule. Local LQR control laws and the corresponding maximum control invariant sets can be designed for finite equilibrium points. It is guaranteed that control invariant sets are overlapped each other. The union of the control invariant sets is treated as the terminal constraint set of predictive control. The feasibility and stability of the novel dual-mode model predictive control are investigated with both variable and fixed horizon. Because of the introduction of extended terminal constrained set, the feasibility of optimization can be guaranteed with short prediction horizon. In this way, the size of the optimization problem is reduced so it is computationally efficient. Finally, a simulation example illustrating the algorithm is presented.
基金Project(61074074)supported by the National Natural Science Foundation,ChinaProject(KT2012C01J0401)supported by the Group Innovation Fund,China
文摘An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.
文摘Nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive model.Here,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward window.In order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was obtained.The controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external disturbance.The results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance rejection.Parameter convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.
文摘针对长时间充放电后锂电池模组之间荷电状态(state of charge,SOC)不一致的问题,传统集中式均衡电路存在均衡速度过低的缺陷,以对称式开关阵列、Boost变换器与LC准谐振电路作为均衡主电路,提出了一种基于连续集模型预测控制(continuous control set model predictive control,CCS-MPC)的均衡控制策略。首先,对均衡系统进行建模,构建离散状态空间方程;然后,根据状态方程设计多步模型预测算法,并以SOC预测值和参考值、变换器开关管当前输入和上一时刻输入之间的误差作为价值函数;最后,对价值函数进行二次规划,在线求解出一组控制最优解,并应用于均衡系统,通过动态调整占空比以控制均衡电流的大小。相较于单步预测,多步预测需要考虑被控量在多个周期内保持最优,可以保证在每个均衡周期内均衡器都能输出最优的均衡电流,有效防止均衡器失稳。仿真结果表明,所提模型预测算法实现了各电池组SOC一致,保证了均衡电流的稳定输出,相比常规PI算法缩短了17%的均衡时间。
文摘为保证全直流风电系统安全并网运行,系统直流电压稳定控制至关重要。全直流风电系统直流电压稳定控制采用比例积分(PI)控制时,PI参数较多且整定繁琐复杂,在非正常运行工况下动态响应速度相对较慢,控制精度不够高。针对以上问题,文章提出一种基于有限控制集模型预测控制(Finite Control Set-Model Predictive Control,FCS-MPC)原理对系统换流器桥臂晶体管开关状态进行控制的系统直流电压稳定控制策略。该策略结合机侧整流器及并网逆变器的电流预测模型,以换流器输出电流为控制变量构造代价函数,以代价函数为优化目标,为避免计算时延导致的控制延时,引入延时补偿提高控制准确度,并引入权重系数实现多目标优化,通过遍历计算产生最优开关组合信号触发换流器。在Matlab/Simulink中建立全直流风电系统的仿真模型,在不同工况下,对所提策略与传统PI控制进行对比仿真分析,仿真结果有效验证了所提控制策略的静态性能及动态性能。
文摘三有源桥(triple active bridge,TAB)变换器可以灵活连接多个电压等级,在直流微网、混合储能等领域受到广泛关注.模型预测控制是提升TAB变换器动态性能、实现端口解耦的有效策略.然而,由于TAB变换器功率传输模型复杂,采用连续集模型预测控制,代价函数求解极其困难,工程应用价值低.本文结合有限集模型预测控制,首次提出了一种应用于TAB变换器的移相离散集模型预测控制方法.该方法在每个控制周期内通过对有限个离散化的移相角组合进行局部寻优,继而滑动寻优窗口,最终实现全局最优控制.该方法既保证了优异的动态性能、解耦性能,又避免了复杂的非线性方程组求解,极大增强了控制策略的实用性.同时,本文还分析了移相离散集模型预测控制中权重系数、离散增益、预测范围的优化配置.最后,通过实验验证了所提控制策略的有效性.
