Disturbance Rejection in MIMO Systems With Emotional-Learning-Based Controller: Application to Variable Rotor-Speed Helicopters
提出一种帕累托最优的扰动估计器设计方法，用于情感学习控制的多输入多输出系统，在实验室直升机上验证了其对动态风扰动的抑制效果优于传统设计。
This manuscript proposes a Pareto-optimal approach toward designing an estimator for rejecting disturbances with an emotional-learning-based control (ELBC) administered MIMO system. Contrary to the conventional high-gain-based ELBC design, this approach employs a disturbance estimate augmented stimulus which is not prone to excitation of mechanical resonance. Furthermore, in contrast to the existing SISO-based methods of estimator design, this approach considers key attributes, such as disturbance rejection, noise sensitivity, control signal chattering, and loop interaction associated with multiple loops of an MIMO system in an integrated manner. The parameters of the estimator are realized by minimizing a performance index that is representative of the weighted sensitivity and complementary sensitivity functions of the MIMO system. The approach is illustrated with four popular disturbance estimators. Experimental results on a laboratory-scale helicopter under dynamic wind gusts demonstrate its efficacy with respect to nominal ELBC design.