论文标题
通过线性测量模型对仿射非线性系统的输出反馈自适应最佳控制
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model
论文作者
论文摘要
在复杂和不确定的环境中，现实世界中的控制应用需要适应性，以处理模型的不确定性和鲁棒性，以防止干扰。本文介绍了一个在线，输出反馈，仅批评，基于模型的增强型学习体系结构，该体系结构同时学习和实现了最佳控制器，同时在学习阶段保持稳定性。使用乘数矩阵，搜索观察者收益的便捷方法与控制器一起设计，该控制器从模拟体验中学习，以确保闭环系统的稳定性和融合到原始邻里。轨迹的局部统一最终界限是使用基于Lyapunov的分析建立的，并通过模拟结果在轻度的激发条件下证明。
Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances. This paper presents an online, output-feedback, critic-only, model-based reinforcement learning architecture that simultaneously learns and implements an optimal controller while maintaining stability during the learning phase. Using multiplier matrices, a convenient way to search for observer gains is designed along with a controller that learns from simulated experience to ensure stability and convergence of trajectories of the closed-loop system to a neighborhood of the origin. Local uniform ultimate boundedness of the trajectories is established using a Lyapunov-based analysis and demonstrated through simulation results, under mild excitation conditions.
