良好的控制策略是实现并联型有源电力滤波器(active power filter,APF)补偿功能的关键。由于并联型APF常规电流PI控制方法的闭环增益受系统稳定性条件约束,并联型APF对负载主要谐波分量补偿不充分。针对该问题,提出一种用于APF的新型选...良好的控制策略是实现并联型有源电力滤波器(active power filter,APF)补偿功能的关键。由于并联型APF常规电流PI控制方法的闭环增益受系统稳定性条件约束,并联型APF对负载主要谐波分量补偿不充分。针对该问题,提出一种用于APF的新型选择性谐波电流控制策略。该控制策略在常规电流PI控制策略的基础上,对负载电流主要谐波(该文主要指5次、7次谐波)单独提取与控制,而对其余次谐波采用一个常规电流PI控制器控制。该设计方法,增大了系统对主要谐波分量的跟踪增益,提高了APF对谐波的补偿率,实现了控制系统更好的频率响应。将该方法应用于实验室制作的一台30 kVA并联型APF实验装置,可将电流总谐波畸变率(total harmonic distortion,THD)由23.21%补偿为3.75%。仿真与实验结果证明了以上结论。展开更多
针对一类具有不确定系统函数和方向未知的不确定增益函数的非线性系统,提出了一种鲁棒自适应神经网络控制算法.本算法采用RBF神经网络(Radial based function neural network,RBFNN)逼近模型不确定性,外界干扰和建模误差采用非线性阻尼...针对一类具有不确定系统函数和方向未知的不确定增益函数的非线性系统,提出了一种鲁棒自适应神经网络控制算法.本算法采用RBF神经网络(Radial based function neural network,RBFNN)逼近模型不确定性,外界干扰和建模误差采用非线性阻尼项进行补偿,将动态面控制(Dynamic surface control,DSC)与后推方法结合,消除了反推法的计算膨胀问题,降低了控制器的复杂性;尤其是采用Nussbaum函数处理系统中方向未知的不确定虚拟控制增益函数,不仅可以避免可能存在的控制器奇异值问题,而且还能使得整个系统的在线学习参数显著减少,与DSC方法优点结合,使得控制算法的计算量大为减少,便于计算机实现.稳定性分析证明了所得闭环系统是半全局一致最终有界(Semi-global uniformly ultimately bounded,SGUUB)的,并且跟踪误差可以收敛到原点的一个较小邻域.最后,计算机仿真结果表明了本文所提出控制器的有效性.展开更多
现有电力系统稳定器(power system stabilizer,PSS)和直流调制多使用本地信号作为控制器反馈输入信号,控制器间的交互作用可能降低甚至破坏系统稳定性。提出了一种基于广域测量信号的PSS与直流调制协调策略,首先通过留数法选择对于振荡...现有电力系统稳定器(power system stabilizer,PSS)和直流调制多使用本地信号作为控制器反馈输入信号,控制器间的交互作用可能降低甚至破坏系统稳定性。提出了一种基于广域测量信号的PSS与直流调制协调策略,首先通过留数法选择对于振荡模态可观性较强的广域信号作为阻尼控制器备选反馈信号;其次通过相对增益方法选择使PSS和直流调制交互影响最小的备选信号作为最佳反馈信号;而后设计分散控制器,并运用基于混沌和差分进化的混合粒子群优化算法对PSS和直流调制控制器参数进行协调优化。最后,通过EPRI 36节点系统仿真验证了协调策略的正确性和有效性。展开更多
A novel gain-scheduled switching control method for the longitudinal motion of a flexible air-breathing hypersonic vehicle （FAHV） is proposed. Firstly, velocity and altitude are selected as scheduling variables, a p...A novel gain-scheduled switching control method for the longitudinal motion of a flexible air-breathing hypersonic vehicle （FAHV） is proposed. Firstly, velocity and altitude are selected as scheduling variables, a polytopic linear parameter varying （LPV） model is developed to represent the complex nonlinear longitudinal dynamics of the FAHV. Secondly, based on the obtained polytopic LPV model, the flight envelope is divided into four smaller subregions, and four gain-scheduled controllers are designed for these parameter subregions. Then, by the defined switching characteristic function, these gain-scheduled controllers are switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a given tracking error performance criterion. The condition of gain-scheduled switching controller synthesis is given in terms of linear matrix inequalities （LMIs） which can be easily solved by using standard software packages. Finally, simulation results show the effectiveness of the presented method.展开更多
文摘针对一类具有不确定系统函数和方向未知的不确定增益函数的非线性系统,提出了一种鲁棒自适应神经网络控制算法.本算法采用RBF神经网络(Radial based function neural network,RBFNN)逼近模型不确定性,外界干扰和建模误差采用非线性阻尼项进行补偿,将动态面控制(Dynamic surface control,DSC)与后推方法结合,消除了反推法的计算膨胀问题,降低了控制器的复杂性;尤其是采用Nussbaum函数处理系统中方向未知的不确定虚拟控制增益函数,不仅可以避免可能存在的控制器奇异值问题,而且还能使得整个系统的在线学习参数显著减少,与DSC方法优点结合,使得控制算法的计算量大为减少,便于计算机实现.稳定性分析证明了所得闭环系统是半全局一致最终有界(Semi-global uniformly ultimately bounded,SGUUB)的,并且跟踪误差可以收敛到原点的一个较小邻域.最后,计算机仿真结果表明了本文所提出控制器的有效性.
文摘现有电力系统稳定器(power system stabilizer,PSS)和直流调制多使用本地信号作为控制器反馈输入信号,控制器间的交互作用可能降低甚至破坏系统稳定性。提出了一种基于广域测量信号的PSS与直流调制协调策略,首先通过留数法选择对于振荡模态可观性较强的广域信号作为阻尼控制器备选反馈信号;其次通过相对增益方法选择使PSS和直流调制交互影响最小的备选信号作为最佳反馈信号;而后设计分散控制器,并运用基于混沌和差分进化的混合粒子群优化算法对PSS和直流调制控制器参数进行协调优化。最后,通过EPRI 36节点系统仿真验证了协调策略的正确性和有效性。
基金supported by the National Outstanding Youth Science Foundation(61125306)the National Natural Science Foundation of Major Research Plan(91016004+2 种基金61034002)the Specialized Research Fund for the Doctoral Program of Higher Education of China (20110092110020)the Scientific Research Foundation of Graduate School of Southeast University(YBJJ1103)
文摘A novel gain-scheduled switching control method for the longitudinal motion of a flexible air-breathing hypersonic vehicle （FAHV） is proposed. Firstly, velocity and altitude are selected as scheduling variables, a polytopic linear parameter varying （LPV） model is developed to represent the complex nonlinear longitudinal dynamics of the FAHV. Secondly, based on the obtained polytopic LPV model, the flight envelope is divided into four smaller subregions, and four gain-scheduled controllers are designed for these parameter subregions. Then, by the defined switching characteristic function, these gain-scheduled controllers are switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a given tracking error performance criterion. The condition of gain-scheduled switching controller synthesis is given in terms of linear matrix inequalities （LMIs） which can be easily solved by using standard software packages. Finally, simulation results show the effectiveness of the presented method.
