结合在线辨识与滚动时域优化的自适应非线性控制
Adaptive Nonlinear Control with Online Identification and Receding-Horizon Optimization
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中文总结 AI 辅助
该研究提出结合滚动时域iLQR等技术的自适应非线性最优控制方案，通过AMIGO框架分三阶段计算，经多种非线性系统验证，为复杂控制场景提供有效方法。
中文摘要 AI 辅助
本文提出一种自适应非线性最优控制方案，将滚动时域iLQR、状态估计、在线参数辨识与执行器约束相结合。AMIGO（自适应基于模型的智能制导与调度）将计算划分为三个时间阶段：辨识、规划与闭环控制。非线性过渡通过四阶龙格-库塔法（RK4）评估，模型参数通过列文伯格-马夸尔特法（LM）优化。该方法通过范德波尔振子、四旋翼飞行器及自主月球着陆器下降任务进行验证。
英文摘要
An adaptive nonlinear optimal-control scheme is developed by combining receding-horizon iLQR, state estimation, online parameter identification, and actuator constraints. AMIGO (Adaptive Model-based Intelligent Guidance and Orchestration) organizes the computation into three Time Phases: identification, planning, and closed-loop control. A supervisory adaptive loop monitors predictive consistency during closed-loop operation and can repeat identification and planning when persistent parameter mismatch is detected. The nonlinear transition is evaluated by the fourth-order Runge-Kutta method (RK4), and model parameters are refined by the Levenberg-Marquardt method (LM). The method is illustrated by a Van der Pol oscillator, a quadcopter, and an autonomous lunar-lander descent.
