Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model t...Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model the nonlinearities. By using an appropriate uncertainty description and the linear difference inclusion technique, sufficient conditions for existence of such controller are derived based on the linear matrix inequalities （LMIs）. Using solutions of LMIs, a state feedback control law is proposed to stabilize the perturbed system and guarantee an upper bound of system performance, which is applicable to arbitrary time-delays.展开更多
For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a r...For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs, such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness （UUB） of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated by an illustrative simulation example.展开更多
In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as ...In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as roughness, etc）. Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.展开更多
The robust stability of uncertain neural network with time-varying delay was investigated.The norm-bounded uncertainties are included in the system matrices.The constraint on time-varying delays is removed,which means...The robust stability of uncertain neural network with time-varying delay was investigated.The norm-bounded uncertainties are included in the system matrices.The constraint on time-varying delays is removed,which means that a fast time-varying delay is admissible.Some new delay-dependent stability criteria were presented by using Lyapunov-Krasovskii functional and linear matrix inequalities(LMIs) approaches.Finally,a numerical example was given to illustrate the effectiveness and innovation nature of the developed techniques.展开更多
A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed. Firstly, neural networks are employed to approximate the nonlinearities. Next, the nonlinear dynamic system is represe...A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed. Firstly, neural networks are employed to approximate the nonlinearities. Next, the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion （LDI）. Finally, based on the LDI model, a neural network-based nonlinear filter （NNBNF） is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality （LMI） constraints. Compared with the existing nonlinear filters, NNBNF is time-invariant and numerically tractable. The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.展开更多
In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was...In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.展开更多
An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems.Both the designed observer and controller are free from time de...An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems.Both the designed observer and controller are free from time delays.Different from the existing results,this paper need not the assumption that the upper bounding functions of time-delay terms are known,and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms,so the designed controller procedure is more simplified.In addition,the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded,and the output regulation error converges to a small residual set around the origin.Two simulation examples are provided to verify the effectiveness of control scheme.展开更多
A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback c...A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.展开更多
设备资产运维精益管理系统(power production management system,PMS)SF6气体量数据不全且误差较大,无法为电网企业核算碳储量以及实现待建变电站碳规划提供基础数据。针对上述情况,研究了计及母线和断路器的变电站碳储量核算方法,并结...设备资产运维精益管理系统(power production management system,PMS)SF6气体量数据不全且误差较大,无法为电网企业核算碳储量以及实现待建变电站碳规划提供基础数据。针对上述情况,研究了计及母线和断路器的变电站碳储量核算方法,并结合宁夏电网现场实测数据,通过MIC法筛选神经网络输入参数,构建了6输入参数的GA-BP、PSO-BP、HPO-BP神经网络模型,结果表明HPO-BP神经网络模型的评估指标及预估结果相对误差(6.28%)均优于其余2种神经网络模型,可以准确核算断路器SF6气体量。针对参数不确定情况,根据PCCs法分析不同参数之间的线性关系,构建了3输入参数的HPO-BP神经网络模型,预估结果相对误差为9.72%。通过遍历输出方式,在参数不确定情况下输出多组断路器SF6气体量预估数据,利用求和累积方法获取变电站总SF6气体量,并量化为变电站碳储量,从而为电网企业实现“双碳”目标提供数据支撑。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
基金This project was supported by the National Natural Science Foundation of China (60574001)Program for New Century Excellent Talents in University (NCET-05-0485).
文摘Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model the nonlinearities. By using an appropriate uncertainty description and the linear difference inclusion technique, sufficient conditions for existence of such controller are derived based on the linear matrix inequalities （LMIs）. Using solutions of LMIs, a state feedback control law is proposed to stabilize the perturbed system and guarantee an upper bound of system performance, which is applicable to arbitrary time-delays.
基金This project was supported by the National Natural Science Foundation of China (69974028 60374015)
文摘For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs, such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness （UUB） of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated by an illustrative simulation example.
基金This project was supported by the National Natural Science Foundation of China (60572038)
文摘In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself （including fuzziness, randomness, incompleteness as well as roughness, etc）. Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.
文摘The robust stability of uncertain neural network with time-varying delay was investigated.The norm-bounded uncertainties are included in the system matrices.The constraint on time-varying delays is removed,which means that a fast time-varying delay is admissible.Some new delay-dependent stability criteria were presented by using Lyapunov-Krasovskii functional and linear matrix inequalities(LMIs) approaches.Finally,a numerical example was given to illustrate the effectiveness and innovation nature of the developed techniques.
基金the National Natural Science Foundation of China (60574001)Program for New CenturyExcellent Talents in University (NCET-05-0485) and PIRTJiangnan
文摘A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed. Firstly, neural networks are employed to approximate the nonlinearities. Next, the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion （LDI）. Finally, based on the LDI model, a neural network-based nonlinear filter （NNBNF） is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality （LMI） constraints. Compared with the existing nonlinear filters, NNBNF is time-invariant and numerically tractable. The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.
基金Projects(70572090, 70373017) supported by the National Natural Science Foundation of China
文摘In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.
基金supported by the National Natural Science Foundation of China (60804021)the Fundamental Research Funds for the Central Universities (JY10000970001)
文摘An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems.Both the designed observer and controller are free from time delays.Different from the existing results,this paper need not the assumption that the upper bounding functions of time-delay terms are known,and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms,so the designed controller procedure is more simplified.In addition,the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded,and the output regulation error converges to a small residual set around the origin.Two simulation examples are provided to verify the effectiveness of control scheme.
基金Project(61433004)suppouted by the National Natural Science Foundation of China
文摘A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.
文摘设备资产运维精益管理系统(power production management system,PMS)SF6气体量数据不全且误差较大,无法为电网企业核算碳储量以及实现待建变电站碳规划提供基础数据。针对上述情况,研究了计及母线和断路器的变电站碳储量核算方法,并结合宁夏电网现场实测数据,通过MIC法筛选神经网络输入参数,构建了6输入参数的GA-BP、PSO-BP、HPO-BP神经网络模型,结果表明HPO-BP神经网络模型的评估指标及预估结果相对误差(6.28%)均优于其余2种神经网络模型,可以准确核算断路器SF6气体量。针对参数不确定情况,根据PCCs法分析不同参数之间的线性关系,构建了3输入参数的HPO-BP神经网络模型,预估结果相对误差为9.72%。通过遍历输出方式,在参数不确定情况下输出多组断路器SF6气体量预估数据,利用求和累积方法获取变电站总SF6气体量,并量化为变电站碳储量,从而为电网企业实现“双碳”目标提供数据支撑。
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.
