As control engineering methods are applied to increasingly complex systems, data-driven approaches for system identification appear as a promising alternative to physics-based modeling. While many of these approaches rely on the availability of state measurements, the states of a complex system are often not directly measurable. It may then be necessary to jointly estimate the dynamics and a latent state, making it considerably more challenging to design controllers with performance guarantees. This paper proposes a novel method for the computation of an optimal input trajectory for unknown nonlinear systems with latent states. Probabilistic performance guarantees are derived for the resulting input trajectory, and an approach to validate the performance of arbitrary control laws is presented. The effectiveness of the proposed method is demonstrated in a numerical simulation.

翻译：随着控制工程方法应用于日益复杂的系统，基于数据驱动的系统辨识方法成为物理建模的有前景替代方案。尽管此类方法多依赖于状态测量的可用性，但复杂系统的状态通常无法直接测量。此时需联合估计系统动力学与隐状态，这使得设计具有性能保障的控制器面临更大挑战。本文提出一种针对隐状态未知非线性系统最优输入轨迹计算的新方法。推导了所得输入轨迹的概率性能保证，并提出了任意控制律性能验证方案。通过数值仿真验证了所提方法的有效性。