Fast RLS Identification Leveraging the Linearized System Sparsity: Predictive Cost Adaptive Control for Quadrotors

TL;DR

This paper introduces a novel predictive control method for quadrotors that uses online RLS identification of only essential parameters, improving adaptability, speed, and stability without needing attitude setpoints.

Contribution

It leverages the sparsity of linearized quadrotor models to reduce parameter estimation complexity and enhances control performance in dynamic environments.

Findings

Faster parameter identification due to sparsity exploitation

Improved stability during transient maneuvers

Elimination of attitude setpoint requirement in control scheme

Abstract

This paper presents a centralized predictive cost adaptive control (PCAC) strategy for the position and attitude control of quadrotors. PCAC is an optimal, prediction-based control method that uses recursive least squares (RLS) to identify model parameters online, enabling adaptability in dynamic environments. Addressing challenges with black-box approaches in systems with complex couplings and fast dynamics, this study leverages the unique sparsity of quadrotor models linearized around hover points. By identifying only essential parameters related to nonlinear couplings and dynamics, this approach reduces the number of parameters to estimate, accelerates identification, and enhances stability during transients. Furthermore, the proposed control scheme removes the need for an attitude setpoint, typically required in conventional cascaded control designs.