The design of the flight controller that exploits the advantages of the nonlinear dynamic inversion, adaptive fuzzy system and slide model control is discussed.
论述了综合运用非线性动态逆、自适应模糊系统和滑模控制的优点进行飞行控制律设计的方法。
Proposed an adaptive network fuzzy inference system control strategy based on hierarchy structure of gait planning, which do not require detailed kinematics or dynamic biped models.
提出一种基于步态规划分级结构的自适应网络模糊推理系统控制策略，该方法不需要确定双足机器人运动学和动力学模型。
An adaptive fuzzy integral type sliding mode control method based on GA was proposed to control single-axis motion control system with nonlinear dynamic friction.
提出一种基于GA的自适应模糊积分型滑模控制策略，并将其用于具有非线性动态摩擦力的单轴运动控制系统中。
Adaptive fuzzy controller was combined with PID controller to compose parallel control system, which was aimed at reducing static error and suppressing dynamic disturbance of the whole system.
其中直接自适应模糊控制器还与PID控制器一起组成并行控制系统来抑制系统静态误差和动态干扰。
For general nonlinear system, a control law design method which integrated the nonlinear dynamic inverse theory, adaptive fuzzy system and slide model control is developed.
对一般非线性系统，推导了一种综合运用非线性动态逆、自适应模糊逻辑系统和滑动模态控制进行控制律设计的方法。
A novel indirect adaptive controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed for affine nonlinear system.
针对仿射非线性系统，提出了一种新型的基于动态递归模糊神经网络(DRFNN)的间接自适应控制器。
Simulation results show that the induction motor vector control system with adaptive neuro-fuzzy inference system can improve the static and dynamic performance of the motor and has good robust.
仿真实验结果表明，具有自适应神经网络的模糊推理系统控制的异步电机矢量控制系统不仅动态和稳态性能都得到提高，而且具有较强的鲁棒性。
The analysis of adaptive fuzzy control and PID parameter change on the impact of system performance based on the dynamic process of PID tuning parameters.
在分析自适应模糊控制及PID参数变化对系统性能影响的基础上，提出在动态过程中对PID参数进行整定。
The analysis of adaptive fuzzy control and PID parameter change on the impact of system performance based on the dynamic process of PID tuning parameters.
在分析自适应模糊控制及PID参数变化对系统性能影响的基础上，提出在动态过程中对PID参数进行整定。
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