Fault-Tolerant MPC Control for Trajectory Tracking

TL;DR

This paper proposes a fault-tolerant MPC control strategy that actively identifies faults and adapts the model in real-time to maintain trajectory tracking performance.

Contribution

It introduces a novel approach combining set-based fault identification with model adaptation using CCGs and SVD decomposition.

Findings

Effective fault isolation demonstrated in simulations

Improved trajectory tracking under faults

Real-time model adaptation enhances robustness

Abstract

An MPC controller uses a model of the dynamical system to plan an optimal control strategy for a finite horizon, which makes its performance intrinsically tied to the quality of the model. When faults occur, the compromised model will degrade the performance of the MPC with this impact being dependent on the designed cost function. In this paper, we aim to devise a strategy that combines active fault identification while driving the system towards the desired trajectory. The explored approaches make use of an exact formulation of the problem in terms of set-based propagation resorting to Constrained Convex Generators (CCGs) and a suboptimal version that resorts to the SVD decomposition to achieve the active fault isolation in order to adapt the model in runtime.