Isoperimetric Constraint Inference for Discrete-Time Nonlinear Systems Based on Inverse Optimal Control
研究了从最优状态和控制轨迹中推断未知等周约束的问题，利用庞特里亚金原理建立恢复方程，在可验证条件下保证精确推断，并通过仿真验证有效性。
In this article, the problem of inferring unknown isoperimetric constraints is considered given optimal state and control trajectories that solve the optimal control problem with isoperimetric constraints. By exploiting Pontryagin's principle, the recovery equations for unknown isoperimetric constraints are established. Under verifiable dimensionality condition and matrix rank condition, the proposed method is guaranteed to infer the unknown isoperimetric constraints exactly. Furthermore, the proposed method is extended to multiple trajectory setting. Finally, the effectiveness of the proposed method is illustrated by two simulation examples with various settings.