因负载变化、磁路机构的偏移以及元器件的老化等因素导致的双LCL型感应电能传输(inductively power transfer,IPT)系统出现输出电压变化及系统频率漂移等不确定性问题会加剧系统性能的恶化,为此,针对双LCL型IPT系统在负载摄动和干扰下...因负载变化、磁路机构的偏移以及元器件的老化等因素导致的双LCL型感应电能传输(inductively power transfer,IPT)系统出现输出电压变化及系统频率漂移等不确定性问题会加剧系统性能的恶化,为此,针对双LCL型IPT系统在负载摄动和干扰下的稳压输出,设计闭环系统的鲁棒控制器。首先采用广义状态空间平均法建立系统频域模型,将实际被控对象的模型转化为广义系统,借助上线性分式变换分离系统的标称部分和不确定部分,得到广义被控对象模型。然后,基于该模型设计H_∞鲁棒控制器,并利用结构奇异值的频率响应校验闭环系统的鲁棒稳定性及鲁棒性能。仿真和实验结果表明该控制器可以较好地保证双LCL型IPT系统的标称性能和鲁棒性能,验证了该模型和控制方法的有效性和可行性。展开更多
An extension of L_1 adaptive control is proposed for the unmatched uncertain nonlinear system with the nonlinear reference system that defines the performance specifications. The control law adapts fast and tracks the...An extension of L_1 adaptive control is proposed for the unmatched uncertain nonlinear system with the nonlinear reference system that defines the performance specifications. The control law adapts fast and tracks the reference system with the guaranteed robustness and transient performance in the presence of unmatched uncertainties. The interval analysis is used to build the quasi-linear parameter-varying model of unmatched nonlinear system, and the robust stability of the proposed controller is addressed by sum of squares programming. The transient performance analysis shows that within the limit of hardware a large adaption gain can improve the asymptotic tracking performance. Simulation results are provided to demonstrate the theoretical findings of the proposed controller.展开更多
This paper focuses on the existence, uniqueness and global robust stability of equilibrium point for complex-valued recurrent neural networks with multiple time-delays and under parameter uncertainties with respect to...This paper focuses on the existence, uniqueness and global robust stability of equilibrium point for complex-valued recurrent neural networks with multiple time-delays and under parameter uncertainties with respect to two activation functions. Two sufficient conditions for robust stability of the considered neural networks are presented and established in two new time-independent relationships between the network parameters of the neural system, Finally, three illustrative examples are given to demonstrate the theoretical results.展开更多
文摘因负载变化、磁路机构的偏移以及元器件的老化等因素导致的双LCL型感应电能传输(inductively power transfer,IPT)系统出现输出电压变化及系统频率漂移等不确定性问题会加剧系统性能的恶化,为此,针对双LCL型IPT系统在负载摄动和干扰下的稳压输出,设计闭环系统的鲁棒控制器。首先采用广义状态空间平均法建立系统频域模型,将实际被控对象的模型转化为广义系统,借助上线性分式变换分离系统的标称部分和不确定部分,得到广义被控对象模型。然后,基于该模型设计H_∞鲁棒控制器,并利用结构奇异值的频率响应校验闭环系统的鲁棒稳定性及鲁棒性能。仿真和实验结果表明该控制器可以较好地保证双LCL型IPT系统的标称性能和鲁棒性能,验证了该模型和控制方法的有效性和可行性。
文摘An extension of L_1 adaptive control is proposed for the unmatched uncertain nonlinear system with the nonlinear reference system that defines the performance specifications. The control law adapts fast and tracks the reference system with the guaranteed robustness and transient performance in the presence of unmatched uncertainties. The interval analysis is used to build the quasi-linear parameter-varying model of unmatched nonlinear system, and the robust stability of the proposed controller is addressed by sum of squares programming. The transient performance analysis shows that within the limit of hardware a large adaption gain can improve the asymptotic tracking performance. Simulation results are provided to demonstrate the theoretical findings of the proposed controller.
基金This publication was made possible by NPRP Grant ≠NPRP 4-1162-1-181 from the Qatar National Research Fund （a member of Qatar Foundation）. The statements made herein are solely the responsibility of the authors. This work was also supported by Natural Science Foundation of China （Grant No. 61374078）.
文摘This paper focuses on the existence, uniqueness and global robust stability of equilibrium point for complex-valued recurrent neural networks with multiple time-delays and under parameter uncertainties with respect to two activation functions. Two sufficient conditions for robust stability of the considered neural networks are presented and established in two new time-independent relationships between the network parameters of the neural system, Finally, three illustrative examples are given to demonstrate the theoretical results.
