发表机构
Kyoto University（京都大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对非线性采样数据系统，开发基于雅可比冻结仿射预测器的泰勒 informed 间接自适应预测控制框架，通过有限泰勒展开结合RLS在线辨识系数，经数值模拟验证高阶模型可提升跟踪精度，其MATLAB实现公开以利可重复性。
AI 中文摘要
本文针对非线性采样数据系统，开发了一种基于泰勒（Taylor）信息的间接自适应预测控制框架，采用雅可比冻结仿射预测器。通过有限泰勒展开近似采样非线性动力学，递归最小二乘（RLS）在线辨识其多项式系数。在每个采样时刻，于当前工作点评估辨识映射的雅可比并在预测时域内冻结，从而为模型预测控制生成仿射预测器。与通用非线性特征字典不同，所实现的多项式字典是一种受向前欧拉/泰勒结构启发的简化字典，精确的联合奇对称性消除了总次数为偶数的单项式，额外的向前欧拉启发式修剪则构成了有意的模型降阶。对一个不稳定非线性基准进行的数值模拟比较了不同泰勒阶数的表现，结果表明，当工作点远离展开点时，高阶模型可提高跟踪精度，同时保持相当的控制量。完整的MATLAB实现已公开，以促进可重复性。
英文摘要
This paper develops a Taylor-informed indirect adaptive predictive control framework for nonlinear sampled-data systems using Jacobian-frozen affine predictors. A finite Taylor expansion approximates the sampled nonlinear dynamics, and recursive least squares (RLS) identifies its polynomial coefficients online. At each sampling instant, the Jacobian of the identified map is evaluated at the current operating point and frozen over the prediction horizon, yielding an affine predictor for model predictive control. In contrast to generic nonlinear feature dictionaries, the implemented polynomial dictionary is a forward-Euler/Taylor-structure-informed reduced dictionary. Exact joint-odd symmetry eliminates even-total-degree monomials, whereas additional forward-Euler-informed pruning constitutes a deliberate model reduction. Numerical simulations on an unstable nonlinear benchmark compare different Taylor degrees. The results show that higher-order models improve tracking accuracy as the operating point moves farther from the expansion point while maintaining comparable control effort. The complete MATLAB implementation is publicly available to facilitate reproducibility.
Comments6 pages, 3 figures
