An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique. Neural networks are used to approximate u...An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique. Neural networks are used to approximate unknown time-delay functions. Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error. Based on Lyapunov-Krasoviskii functional, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number.The feasibility is investigated by an illustrative simulationexample.展开更多
模型预测控制在脉宽调制(pulse width modulation,PWM)整流器上的应用既降低了直接功率控制中的脉振又提高了动态响应速度,但是传统的模型预测功率控制(model predictive power control,MPDPC)中对未来时刻状态量的预测仅依靠模型,对模...模型预测控制在脉宽调制(pulse width modulation,PWM)整流器上的应用既降低了直接功率控制中的脉振又提高了动态响应速度,但是传统的模型预测功率控制(model predictive power control,MPDPC)中对未来时刻状态量的预测仅依靠模型,对模型参数变化较为敏感,功率预测精度受电压传感器的测量精度和网侧谐波变化的影响明显。为实现整流侧参数的实时辨识和提高整体的预测精度,以实现对功率的精准控制,文中在模型预测功率控制(model predictive power control,MPDPC)的基础上引入自适应神经网络电压观测器,提出基于自适应神经网络观测的无电压传感器PWM整流器功率预测控制(adaptive neural model predictive power control,ANMPDPC)策略。通过构建包含自适应神经网络辨识器和自适应神经网络滤波器的自适应电压观测器,实现网侧电压估计的同时滤除电压高次谐波对其的影响,并将电压观测器与功率二步预测相结合,进一步降低功率脉振,提高系统的响应速度和控制精度。仿真和实验结果表明,所提出的改进策略既实现了无电压传感器下的模型预测控制,又有效抑制了网侧谐波的高频干扰及参数变化对预测精度的影响。展开更多
文摘An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique. Neural networks are used to approximate unknown time-delay functions. Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error. Based on Lyapunov-Krasoviskii functional, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number.The feasibility is investigated by an illustrative simulationexample.
文摘模型预测控制在脉宽调制(pulse width modulation,PWM)整流器上的应用既降低了直接功率控制中的脉振又提高了动态响应速度,但是传统的模型预测功率控制(model predictive power control,MPDPC)中对未来时刻状态量的预测仅依靠模型,对模型参数变化较为敏感,功率预测精度受电压传感器的测量精度和网侧谐波变化的影响明显。为实现整流侧参数的实时辨识和提高整体的预测精度,以实现对功率的精准控制,文中在模型预测功率控制(model predictive power control,MPDPC)的基础上引入自适应神经网络电压观测器,提出基于自适应神经网络观测的无电压传感器PWM整流器功率预测控制(adaptive neural model predictive power control,ANMPDPC)策略。通过构建包含自适应神经网络辨识器和自适应神经网络滤波器的自适应电压观测器,实现网侧电压估计的同时滤除电压高次谐波对其的影响,并将电压观测器与功率二步预测相结合,进一步降低功率脉振,提高系统的响应速度和控制精度。仿真和实验结果表明,所提出的改进策略既实现了无电压传感器下的模型预测控制,又有效抑制了网侧谐波的高频干扰及参数变化对预测精度的影响。
