Improving Disturbance Estimation and Suppression via Learning among Systems with Mismatched Dynamics

TL;DR

The paper addresses robustness gaps in disturbance rejection for systems with mismatched dynamics by integrating Iterative Learning Control (ILC) with Disturbance Observer (DOB). It introduces learning filters that transfer information across dynamically different linearized UAVs, with a theorem guaranteeing reduced tracking error $||e_j||$ relative to $||e'_j||$ under bounded modeling uncertainty $||\Delta_{j-1}||$. The approach is validated through simulations and experiments on three quadrotors near hover, showing that learning substantially improves trajectory tracking and disturbance estimation beyond DOB alone, across multiple disturbance profiles. The work demonstrates practical benefits for safety-critical, repetitive tasks in disturbance-prone environments and outlines avenues for future extensions to more aggressive trajectories and multi-system data fusion.

Abstract

Iterative learning control (ILC) is a method for reducing system tracking or estimation errors over multiple iterations by using information from past iterations. The disturbance observer (DOB) is used to estimate and mitigate disturbances within the system, while the system is being affected by them. ILC enhances system performance by introducing a feedforward signal in each iteration. However, its effectiveness may diminish if the conditions change during the iterations. On the other hand, although DOB effectively mitigates the effects of new disturbances, it cannot entirely eliminate them as it operates reactively. Therefore, neither ILC nor DOB alone can ensure sufficient robustness in challenging scenarios. This study focuses on the simultaneous utilization of ILC and DOB to enhance system robustness. The proposed methodology specifically targets dynamically different linearized systems performing repetitive tasks. The systems share similar forms but differ in dynamics (e.g. sizes, masses, and controllers). Consequently, the design of learning filters must account for these differences in dynamics. To validate the approach, the study establishes a theoretical framework for designing learning filters in conjunction with DOB. The validity of the framework is then confirmed through numerical studies and experimental tests conducted on unmanned aerial vehicles (UAVs). Although UAVs are nonlinear systems, the study employs a linearized controller as they operate in proximity to the hover condition. A video introduction of this paper is available via this link: https://zh.engr.tamu.edu/wp-content/uploads/sites/310/2024/02/ILCDOB_v3f.mp4.

Figures (10)

• Figure 1: System block diagram with (a) a basic DOB framework (b) an iterative learning framework along with DOB
• Figure 2: Learning relationship among systems
• Figure 3: Bode plots for $G_{r,j}$ and $G_{d,j}$
• Figure 4: Bode plots for $T_{e_{1,j}}$, $T_{e_{2,j}}$, and $\Omega_j$
• Figure 5: Bode plots for $L_{1,j}$ and $L_{2,j}$