This article bestows the linear quadratic Gaussian (LQG)/Loop Transfer Recovery (LTR) optimal controller design for a perturbed linear system having insufficient information about systems states through a multiobjective optimization approach. A Kalman filter observer is required to estimate the unknown states at the output from the noisy data. However, the main downside of the LQG controller's is that its robustness cannot be guaranteed because it consists of linear quadratic regulator (LQR) and Kalman observer, and due to observer incorporation within the LQR framework results in loss of robustness which is undesirable. Therefore, it is necessary to recover the robustness by tuning the controller which further plays havoc with system performance and control effort for certain plants. The present work addresses the investigation of the trade‐off between multiobjective indexes (formulated on the basis of robustness, optimal control, and performances) through three multiobjective optimization algorithms as NSGA‐II, multiobjective simulated annealing and multiobjective particle swarm optimization. The tuned parameters meet the competitive multiobjective performance indexes that are verified through simulation results. The Pareto front with multiple solutions helps to design a robust controller depending on the weightage given to the respective performance indexes. Simulation results reveal that the proposed multiobjective control strategy helps in recovering the characteristics of LQG/LTR.

中文翻译：

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线性二次高斯/环路传递恢复设计的多目标优化方法

本文通过多目标优化方法为线性系统的状态信息不足的扰动线性系统提供了线性二次高斯（LQG）/回路转移恢复（LTR）最优控制器设计。需要卡尔曼滤波器观察器从噪声数据中估计输出处的未知状态。但是，LQG控制器的主要缺点是不能保证其鲁棒性，因为它由线性二次调节器（LQR）和卡尔曼观测器组成，并且由于将观测器合并到LQR框架中会导致鲁棒性下降，这是不希望的。因此，有必要通过调整控制器来恢复鲁棒性，这对于系统性能和某些工厂的控制工作会进一步造成破坏。本工作通过三种多目标优化算法（如NSGA-II），多目标模拟退火和多目标粒子群优化，研究了多目标指标（基于鲁棒性，最优控制和性能而制定）之间的取舍问题。调整后的参数符合竞争性多目标性能指标，该指标已通过仿真结果验证。具有多种解决方案的Pareto前端可根据赋予各个性能指标的权重，帮助设计一款功能强大的控制器。仿真结果表明，所提出的多目标控制策略有助于恢复LQG / LTR的特性。和性能）通过三种多目标优化算法（如NSGA-II），多目标模拟退火算法和多目标粒子群算法进行优化。调整后的参数符合竞争性多目标性能指标，该指标已通过仿真结果验证。具有多种解决方案的Pareto前端可根据赋予各个性能指标的权重来帮助设计强大的控制器。仿真结果表明，所提出的多目标控制策略有助于恢复LQG / LTR的特性。和性能）通过三种多目标优化算法（如NSGA-II），多目标模拟退火算法和多目标粒子群算法进行优化。调整后的参数符合竞争性多目标性能指标，该指标已通过仿真结果验证。具有多种解决方案的Pareto前端可根据赋予各个性能指标的权重来帮助设计强大的控制器。仿真结果表明，所提出的多目标控制策略有助于恢复LQG / LTR的特性。具有多种解决方案的Pareto前端可根据赋予各个性能指标的权重，帮助设计一款功能强大的控制器。仿真结果表明，所提出的多目标控制策略有助于恢复LQG / LTR的特性。具有多种解决方案的Pareto前端可根据赋予各个性能指标的权重，帮助设计一款功能强大的控制器。仿真结果表明，提出的多目标控制策略有助于恢复LQG / LTR的特性。