论文标题
利用二阶信息来调整反向最佳控制器
Leveraging second-order information for tuning of inverse optimal controllers
论文作者
论文摘要
我们利用二阶信息来调整一类离散时间非线性输入式系统的逆最佳控制器。为此，我们选择代表调谐旋钮的输入惩罚矩阵，以产生闭环动力学的Lyapunov功能的Hessian。这吸引了以高速收敛速度和对逆最佳稳定控制器的调整而闻名的二阶方法之间的联系，以实现闭环轨迹朝向稳态的快速衰减。特别是，我们确保二次收敛，这是通过持续输入惩罚矩阵无法实现的壮举。为了平衡权衡取舍，我们建议对Hessian进行实际实施，并在代表电压源控制电源逆变器的相耦合振荡器网络上进行数值验证。
We leverage second-order information for tuning of inverse optimal controllers for a class of discrete-time nonlinear input-affine systems. For this, we select the input penalty matrix, representing a tuning knob, to yield the Hessian of the Lyapunov function of the closed-loop dynamics. This draws a link between second-order methods known for their high speed of convergence and the tuning of inverse optimal stabilizing controllers to achieve a fast decay of the closed-loop trajectories towards a steady state. In particular, we ensure quadratic convergence, a feat that is otherwise not achieved with a constant input penalty matrix. To balance trade-offs, we suggest a practical implementation of the Hessian and validate this numerically on a network of phase-coupled oscillators that represent voltage source controlled power inverters.
