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以线性离散系统为研究对象,以瞬时最优化控制和智能算法中的迭代学习控制为基础,以系统响应期望值与实际值之差为反馈信号,以离散系统的二次型性能泛函为目标函数,提出了迭代学习型瞬时最优控制算法.该方法以瞬时最优化控制算法初始化控制信号,并采用迭代学习控制在线实时修正控制信号以提高主动控制的效果.针对迭代学习型瞬时最优化控制算法迭代的特性,采用范数方法给出了该算法收敛的充分条件.数值算例表明,迭代学习型瞬时最优控制算法较离散瞬时最优控制算法有较明显的优势.同时,基于改进遗传算法,对主动控制器位置优化进行了讨论.数值分析结果表明:部分楼层设置主动控制器且安装位置经过优化后,其控制效果可接近甚至优于全楼层设置主动控制器时的控制效果.
Taking the linear discrete system as the research object and the iterative learning control in the instantaneous optimal control and intelligent algorithm as the basis, the difference between the expected value and the actual value of the system response is taken as the feedback signal, and the quadratic function functional of the discrete system is taken as the objective function , An iterative learning instantaneous optimal control algorithm is proposed.The method initializes the control signal with instantaneous optimization control algorithm and uses iterative learning to control the online real-time correction control signal to improve the active control effect.For the iterative learning instantaneous optimization control algorithm The sufficient condition for the convergence of this algorithm is given by using the norm method.The numerical examples show that the iterative learning instantaneous optimal control algorithm has more obvious advantages than the discrete instantaneous optimal control algorithm.At the same time, based on the improved genetic algorithm , The location of the active controller is discussed.Numerical analysis shows that the control effect can be close to or even better than that of the active controller with all floors when the active controller is installed on some floors and the installation location is optimized.