Servo Integrated Nonlinear Model Predictive Control for Overactuated Tiltable-Quadrotors

TL;DR

This study proposes a control approach for tiltable-quadrotors based on nonlinear model predictive control (NMPC), which directly uses rotor thrust and servo angle as control inputs, where their limited working ranges are considered input constraints.

Abstract

Utilizing a servo to tilt each rotor transforms quadrotors from underactuated to overactuated systems, allowing for independent control of both attitude and position, which provides advantages for aerial manipulation. However, this enhancement also introduces model nonlinearity, sluggish servo response, and limited operational range into the system, posing challenges to dynamic control. In this study, we propose a control approach for tiltable-quadrotors based on nonlinear model predictive control (NMPC). Unlike conventional cascade methods, our approach preserves the full dynamics without simplification. It directly uses rotor thrust and servo angle as control inputs, where their limited working ranges are considered input constraints. Notably, we incorporate a first-order servo model within the NMPC framework. Simulation reveals that integrating the servo dynamics is not only an enhancement to control performance but also a critical factor for optimization convergence. To evaluate the effectiveness of our approach, we fabricate a tiltable-quadrotor and deploy the algorithm onboard at 100 Hz. Extensive real-world experiments demonstrate rapid, robust, and smooth pose-tracking performance.

Figures (10)

• Figure 1: Our self-made tiltable-quadrotor is tracking a pose trajectory based on the proposed NMPC method. It demonstrates the capability for independent control in position and attitude to track different Lemniscate curves.
• Figure 2: Comparative analysis of the previous and proposed workflows. Unlike the previous cascade structure, the proposed method directly integrates constraints and allocation within the NMPC optimization. Furthermore, servos are explicitly modeled. We directly use the servo angle and thrust as commands, requiring no extra integrator as in other research bicego_nonlinear_2020.
• Figure 3: Diagram of a tiltable-quadrotor with the ENU (X East, Y North, Z Up) inertial frame and the FLU (X Forward, Y Left, Z Up) body frame.
• Figure 4: Comparative analysis of NMPC considering different models in an ideal simulation, where the control targets are described in the main text. The drastic command oscillation in (a) leads to optimization errors in noisy simulations such as Gazebo, and incorporating a servo model in (b) significantly mitigates this phenomenon. From (b) and (c), adding a thrust model merely results in a smoother tracking performance. The result of only considering the thrust is similar to (a).
• Figure 5: (a) Snapshot of our self-build tiltable-quadrotor. (b) An onboard computer VIM4, a 4-in-1 electronic speed controller (ESC), and a self-designed flight control unit "Spinal" are centrally mounted on the robot's body. (c) The feasible servo angle is structurally limited to within $\pm \pi/2$.