For systems with input nonlinearities, a two-step control scheme is adopted.
对存在输入非线性的系统，采用两步法预测控制策略。
Fuzzy Logic is a powerful processing methodology that easily accommodates imprecise input data and system nonlinearities for rapid development of robust control systems.
模糊逻辑是处理非精确输入与系统非线性的强有力处理方法，便于迅速开发鲁棒性控制系统。
A stable adaptive control approach using dynamic neural networks has been developed for a class of multi input multi output MIMO sampled data nonlinear systems with unknown dynamic nonlinearities.
研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法。
The more extensive unmodeled dynamics introduced is input-to-state practical stable (ISpS), and unknown nonlinearities satisfy triangular bound conditions.
系统中含有的未建模动态是更广泛的输入到状态实践稳定，非线性不确定性满足三角界条件。
The more extensive unmodeled dynamics introduced is input-to-state practical stable (ISpS), and unknown nonlinearities satisfy triangular bound conditions.
系统中含有的未建模动态是更广泛的输入到状态实践稳定，非线性不确定性满足三角界条件。
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