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提出了一种基于学习的自校正控制算法 ,算法中包含一个自适应模型和多个固定模型 ,每一个模型都有一个相对应的控制器 .在每一采样时刻 ,将当前时段内具有最小预测误差的模型对应的控制器的输出作为控制输入 .在该算法中 ,自适应模型和自适应控制器的作用是确保闭环系统的稳定性和输出跟踪误差的渐近收敛性 ,而固定模型和固定控制器的作用是当被控对象的参数发生变化时 ,在自适应模型的参数估计收敛之前 ,暂时担当控制器的角色 ,以改善闭环系统的暂态响应 .证明了闭环系统的稳定性和输出跟踪误差的渐近收敛性 .仿真结果表明算法的有效性
A self-tuning learning control algorithm based on learning is proposed, which includes an adaptive model and multiple fixed models, each of which has a corresponding controller. At each sampling moment, a minimum prediction The output of the controller corresponding to the error model is used as the control input, in which the role of the adaptive model and the adaptive controller is to ensure the stability of the closed-loop system and the asymptotic convergence of the output tracking error, while the fixed model and fixed The role of the controller is to temporarily assume the role of controller before the parameter estimation of the adaptive model converges when the parameters of the controlled object change to improve the transient response of the closed-loop system. The stability of the closed-loop system and the output Asymptotic convergence of tracking error.The simulation results show the effectiveness of the algorithm