提出了一种基于FSQP算法的约束非线性系统最优控制方法。
FSQP algorithm based method for constrained nonlinear system optimal control problem is developed.
根据逆最优控制方法，针对非线性系统，提出了利用动态神经网络产生混沌的一种新方法。
Propose a new approach to generate chaos via dynamic neural networks according to inverse optimal control for nonlinear systems.
二是采用带干扰的线性模型，在线性二次型最优控制和线性系统状态观测器的基础上，设计了一种无速度测量非线性鲁棒控制律。
Using the linear model with disturbances, the other law is designed to be robust based on the linear quadratic optimal control and the state observer of linear system.
通过适当的非线性坐标变换，将其简化为线性系统，并对此系统进行最优控制，再通过非线性状态反馈实现对原系统的的主动控制。
The optimal control scheme is applied to the simple system and dries out an active control to the original system through a nonlinear state feedback.
针对一般形式的非线性系统，研究了有终端约束的稳定预测控制策略-有限时域滚动控制相对传统最优控制的次优性问题。
In this paper, considering nonlinear systems, the suboptimality of receding horizon control with Terminal Constraints is presented and compared with traditional optimal control.
针对一般形式的非线性系统，研究了有终端约束的稳定预测控制策略-有限时域滚动控制相对传统最优控制的次优性问题。
In this paper, considering nonlinear systems, the suboptimality of receding horizon control with Terminal Constraints is presented and compared with traditional optimal control.
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