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本文根据系统极点的鲁棒性与特征向量的关系,构造了一个以配置鲁棒特征结构为目标,以具有线性二次型最优控制为约束条件的最优化问题。解此最优化问题,即可得到具有鲁棒特征结构的二次型最优状态反绩。本算法无需首先确定二次型性能指标中的加权阵Q,R.由此算法确定的状态反馈阵所构成的闭环系统的极点,将位于设计者所指定的希望极点附近,并具有较强的鲁棒性。
In this paper, according to the relationship between the robustness of the system pole and the eigenvector, an optimization problem is proposed, which is based on the configuration of robust features and linear quadratic optimal constraints. Solving this optimization problem, we can obtain the inverse of the quadratic optimal state with robust feature structure. The algorithm does not need to first determine the quadratic performance of the weighting matrix Q, R. The state feedback loop formed by the algorithm determines the pole of the closed-loop system will be located near the desired pole specified by the designer and has a strong Robustness.