Combining both kinds of fuzzy logic forms including fuzzy T-S model and adaptive fuzzy logic systems, this paper presents an observer-based tracking control scheme for a class of nonlinear systems.
针对一类非线性系统，把模糊t - S模型和自适应模糊逻辑系统两种模糊逻辑方式结合起来，提出了一种基于观测器的跟踪控制方案。
A plan of model reference adaptive tracking control for nonlinear systems is introduced based on neural network dynamic inversion (NNDI).
基于神经网络动态逆方法，给出了一种非线性模型参考自适应跟踪控制方案。
A robust adaptive tracking control method is presented based on the fuzzy T-S model.
提出了一种基于T - S模糊模型和自适应神经网络的跟踪控制方法。
Combining fuzzy Takagi-Sugeno (T-S) model with adaptive fuzzy logic systems, we present a tracking control scheme for a class of complex nonlinear systems.
针对一类复杂非线性系统，把模糊t - S模型和自适应模糊逻辑系统结合起来，提出了一种跟踪控制方案。
Combining both kinds of fuzzy logic forms including fuzzy T-S model and adaptive fuzzy logic systems, this paper presents an observer-based tracking control scheme for a class of nonlinear systems.
针对一类非线性系统，把模糊t - S模型和自适应模糊逻辑系统这两种模糊逻辑方式结合起来，提出了一种自适应控制方案。
Combining fuzzy Takagi-Sugeno (T-S) model with adaptive fuzzy logic systems, we present a tracking control scheme for a class of complex nonlinear systems.
针对一类非线性系统，把模糊t - S模型和自适应模糊逻辑系统这两种模糊逻辑方式结合起来，提出了一种自适应控制方案。
Combining both kinds of fuzzy logic forms including fuzzy T-S model and adaptive fuzzy logic systems, this paper presents an observer-based tracking control scheme for a class of nonlinear systems.
针对一类复杂非线性系统，把模糊t - S模型和自适应模糊逻辑系统结合起来，提出了一种跟踪控制方案。
Combining fuzzy Takagi-Sugeno (T-S) model with adaptive fuzzy logic systems, we present a tracking control scheme for a class of complex nonlinear systems.
针对一类非线性系统，把模糊t S模型和自适应模糊逻辑系统两类模糊逻辑方式结合起来，提出了一种基于观测器的控制方案。
The model inversion is under the hover condition. And the adaptive control law based on the neural network is designed to guarantee the boundedness of tracking error and control signals.
其中，模型逆基于悬停状态，基于神经网络的自适应控制律能够确保跟踪误差和控制信号的有界。
The model inversion is under the hover condition. And the adaptive control law based on the neural network is designed to guarantee the boundedness of tracking error and control signals.
其中，模型逆基于悬停状态，基于神经网络的自适应控制律能够确保跟踪误差和控制信号的有界。
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