A mixed control method combining fuzzy model-based control and neural network control is presented for a class of uncertain nonlinear system with multiple time delays.
一类多时滞不确定非线性系统，基于模型的模糊控制和神经网络控制相结合的混合控制方法。
Model predictive control (MPC), also known as receding horizon control (RHC), is a class of model-based control theories that use linear or nonlinear process models to forecast system behavior.
模型预测控制(MPC)，也称为滚动时域控制(RHC)，是一种基于模型的控制理论，采用线性或非线性模型预测系统的活动。
As a model-based control algorithm, the performance of controller will be hard to be improved only by re-tuning the parameters of controller if there is serious mismatch between model and plant.
作为基于模型的优化控制算法，如果模型预测控制算法的预测模型与实际对象的失配程度很严重，则仅靠整定控制器参数将难以改善控制器性能。
A model based control scheme employing computed-torque control has been presented, and its applications in three different path planning methods have also been discussed.
设计了一基于模型的计算力矩控制算法，并深入探讨了其应用于三种不同轨迹规划方法下的控制性能。
A model based control scheme employing computed-torque control has been presented, and its applications in three different path planning methods have also been discussed.
设计了一基于模型的计算力矩控制算法，并深入探讨了其应用于三种不同轨迹规划方法下的控制性能。
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