An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wid...An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.展开更多
在交通碳达峰和碳中和的背景下,高精度、细粒度、可实施性强的机动车能耗实时预测方法成为交通减碳关键组成之一。针对传统基于回归的车辆能耗模型普适性较差的问题,提出了一种基于径向基函数神经网络(radial basis function neural net...在交通碳达峰和碳中和的背景下,高精度、细粒度、可实施性强的机动车能耗实时预测方法成为交通减碳关键组成之一。针对传统基于回归的车辆能耗模型普适性较差的问题,提出了一种基于径向基函数神经网络(radial basis function neural network,RBFNN)的车辆能耗预测模型。首先分析车辆能耗影响因素并基于Min-Max标准化方法对影响因素矩阵进行归一化处理,然后基于灰狼算法(grey wolf optimization,GWO)优化RBFNN算法隐藏层中心点、高斯函数的宽度和隐含层与输出层连接的权值的训练,最后从横向模型对比和实车实测数据进行模型预测准确度分析。测试结果表明:RBFNN算法预测准确度较传统回归模型提高约12%,整体准确度达到90%以上,能够很好地对城市机动车能耗进行预测。展开更多
针对变体飞机非线性模型的不确定性问题,提出了一种基于径向基神经网络(radial basis function neural networks,RBFNN)的高精度自适应反步控制方法。首先,在变体飞机静态和动态气动参数分析的基础上,运用传统反步法设计了非线性控制律...针对变体飞机非线性模型的不确定性问题,提出了一种基于径向基神经网络(radial basis function neural networks,RBFNN)的高精度自适应反步控制方法。首先,在变体飞机静态和动态气动参数分析的基础上,运用传统反步法设计了非线性控制律,并引入径向基神经网络在线逼近系统的不确定项,提高系统鲁棒性;并设计鲁棒项消除径向基神经网络带来的逼近误差。其次,通过对虚拟控制变量进行求导项设计微分跟踪器,解决了传统反步法中存在的“微分膨胀”问题。通过Lyapunov稳定性分析,证明该方法能保证闭环系统跟踪误差最终收敛且一致有界。最后,基于Matlab/Simulink搭建了变体飞机的数字仿真模型,并与常规反步法进行了对比分析,仿真结果表明该方法具有控制精度高、鲁棒性强的特点。展开更多
基金supported by the China Postdoctoral Science Foundation (200904501035 201003548)+3 种基金the National Natural Science Foundation of China (60835001907160289101600460804017)
文摘An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.
文摘在交通碳达峰和碳中和的背景下,高精度、细粒度、可实施性强的机动车能耗实时预测方法成为交通减碳关键组成之一。针对传统基于回归的车辆能耗模型普适性较差的问题,提出了一种基于径向基函数神经网络(radial basis function neural network,RBFNN)的车辆能耗预测模型。首先分析车辆能耗影响因素并基于Min-Max标准化方法对影响因素矩阵进行归一化处理,然后基于灰狼算法(grey wolf optimization,GWO)优化RBFNN算法隐藏层中心点、高斯函数的宽度和隐含层与输出层连接的权值的训练,最后从横向模型对比和实车实测数据进行模型预测准确度分析。测试结果表明:RBFNN算法预测准确度较传统回归模型提高约12%,整体准确度达到90%以上,能够很好地对城市机动车能耗进行预测。
文摘针对变体飞机非线性模型的不确定性问题,提出了一种基于径向基神经网络(radial basis function neural networks,RBFNN)的高精度自适应反步控制方法。首先,在变体飞机静态和动态气动参数分析的基础上,运用传统反步法设计了非线性控制律,并引入径向基神经网络在线逼近系统的不确定项,提高系统鲁棒性;并设计鲁棒项消除径向基神经网络带来的逼近误差。其次,通过对虚拟控制变量进行求导项设计微分跟踪器,解决了传统反步法中存在的“微分膨胀”问题。通过Lyapunov稳定性分析,证明该方法能保证闭环系统跟踪误差最终收敛且一致有界。最后,基于Matlab/Simulink搭建了变体飞机的数字仿真模型,并与常规反步法进行了对比分析,仿真结果表明该方法具有控制精度高、鲁棒性强的特点。
