Predictive Control over LAWN: Joint Trajectory Design and Resource Allocation

TL;DR

This paper presents a joint trajectory design and resource allocation framework for low-altitude wireless networks supporting real-time control of mobile AGVs via drones, optimizing control accuracy and communication reliability.

Contribution

It introduces a novel optimization approach combining MPC, outage probability analysis, and quadratic programming for joint control and communication design in LAWN systems.

Findings

Proposed algorithm achieves better control accuracy than baselines.

Closed-form outage probability expression under FBL transmission.

Validated effectiveness through simulations and AirSim experiments.

Abstract

Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over a LAWN system, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory tracking, while we analyze the communication reliability using the outage probability. Subsequently, we formulate an optimization problem to jointly determine control policy, transmit power allocation, and drone trajectory by accounting for the maximum travel distance and control input constraints. To address the resultant non-convex optimization problem, we first derive the closed-form expression of the outage probability under FBL transmission. Based on this, we reformulate the original problem as a quadratic programming (QP) problem, followed by developing an alternating optimization (AO) framework. Specifically, we employ the projected gradient descent (PGD) method and the successive convex approximation (SCA) technique to achieve computationally efficient sub-optimal solutions. Furthermore, we thoroughly analyze the convergence and computational complexity of the proposed algorithm. Extensive simulations and AirSim-based experiments are conducted to validate the superiority of our proposed approach compared to the baseline schemes in terms of control performance.