非线性预测性成本自适应控制：使用多项式、傅里叶和三次样条可观测量的伪线性输入输出模型
Nonlinear Predictive Cost Adaptive Control of Pseudo-Linear Input-Output Models Using Polynomial, Fourier, and Cubic Spline Observables
AI总结：
本文提出了一种基于多项式、傅里叶和三次样条可观测量的非线性预测成本自适应控制方法，用于伪线性输入输出模型的自适应非线性模型预测控制。
摘要：
对具有高不确定性的非线性系统进行控制在实际中具有相关性，在理论上具有挑战性。本文对一种完全依赖在线系统辨识的自适应非线性模型预测控制（MPC）技术进行了数值研究。具体而言，本文考虑了预测成本自适应控制（PCAC），它是广义预测控制的扩展。非线性PCAC（NPCAC）使用递推最小二乘（RLS）与信息遗忘子空间（SIFt）来辨识离散时间、伪线性、输入输出模型，该模型与迭代MPC结合用于非线性滚动优化。NPCAC的性能通过多项式、傅里叶和三次样条基函数进行说明。
英文摘要：
Control of nonlinear (NL) systems with high levels of uncertainty is practically relevant and theoretically challenging. This paper presents a numerical investigation of an adaptive NL model predictive control (MPC) technique that relies entirely on online system identification without prior modeling, training, or data collection. In particular, the paper extends predictive cost adaptive control (PCAC) for linear systems, which is an extension of generalized predictive control, to NL systems. NL PCAC (NPCAC) uses recursive least squares (RLS) with subspace of information forgetting (SIFt) to identify a discrete-time, pseudo-linear, input-output model, which is used with iterative MPC for NL receding-horizon optimization. The performance of NPCAC is illustrated using polynomial, Fourier, and cubic-spline basis functions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
