# Centralized model predictive control with distributed adaptation

arXiv: https://arxiv.org/abs/2004.02358 · HTML: https://mlsanity.com/paper/2004.02358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Algorithmic design and implementation considerations of deep MPC](https://mlsanity.com/paper/2511.17233/) (2025)

## Abstract (arXiv)

A centralized model predictive controller (MPC), which is unaware of local uncertainties, for an affine discrete time nonlinear system is presented. The local uncertainties are assumed to be matched, bounded and structured. In order to encounter disturbances and to improve performance, an adaptive control mechanism is employed locally. The proposed approach ensures input-to-state stability of closed-loop states and convergence to the equilibrium point. Moreover, uncertainties are learnt in terms of the given feature basis by using adaptive control mechanism. In addition, hard constraints on state and control are satisfied.