Improving INDI for Input Nonaffine Systems via Learning-Based Nonlinear Control Allocation
This addresses computational challenges in control allocation for input nonaffine systems, but appears incremental as it builds on existing INDI and ICA frameworks.
The paper tackled the problem of applying standard incremental nonlinear dynamic inversion (INDI) with incremental control allocation (ICA) to input nonaffine systems, which relies on an untenable linear approximation, and proposed a supervised learning-based approach to avoid solving nonlinear programming problems online. The result showed that the learning-based method provides an effective and computationally tractable alternative, as validated in numerical experiments.
This paper first demonstrates that applying standard incremental nonlinear dynamic inversion (INDI) with incremental control allocation (ICA) to input nonaffine systems relies on an untenable linear approximation of the actuator model. It then shows that avoiding this issue, while retaining the static control allocation paradigm, generally requires solving a nonlinear programming (NLP) problem. To address the associated online computational challenges, the paper subsequently presents a supervised learning-based approach. Numerical experiments on an example problem validate the identified limitations of standard INDI + ICA for input nonaffine systems, while also demonstrating that the proposed learning-based method provides an effective and computationally tractable alternative.