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首页> 外文会议>2011 IEEE International Conference on Information and Automation >Immune evolution algorithm for iterative learning controller
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Immune evolution algorithm for iterative learning controller

机译：迭代学习控制器的免疫进化算法

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In this paper, immune evolution algorithm (IEA) by imitating the defending process of an immune system and the mutating ideas of biology evolutionary is investigated to optimize the input of iterative learning controller. In the IEA, a self-adaptive mutation operator is constructed to decide the mutation step size of every antibody by its environment and an affinity calculation process is also embedded to maintain the diversity. The method takes the objective function that is defined as the square error between reference signal and output signal in all sampling points and constraints as antigen. Through the genetic evolution, an antibody that most fits the antigen becomes the solution. The experimental results confirm that the proposed method has higher tracking accuracy and fast convergence speed. And compared with conventional iterative learning control methods, it is easy to solve the optimal input for nonlinear plant models.
机译：本文通过模仿免疫系统的防御过程和生物学进化的变异思想，研究了免疫进化算法（IEA），以优化迭代学习控制器的输入。在IEA中，构建了一个自适应突变算子，以根据其环境决定每种抗体的突变步长，并且还嵌入了一个亲和力计算过程来维持多样性。该方法采用目标函数，该目标函数定义为所有采样点中参考信号和输出信号之间的平方误差，并约束为抗原。通过遗传进化，最适合抗原的抗体成为溶液。实验结果表明，该方法具有较高的跟踪精度和较快的收敛速度。并且与传统的迭代学习控制方法相比，很容易解决非线性植物模型的最优输入问题。

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