Robust Model Predictive Flight Control of Unmanned Rotorcrafts

Abstract

This paper addresses the problem of robust flight control of unmanned rotorcrafts, by proposing and experimentally evaluating a real-time robust model predictive control scheme in various challenging conditions, aiming to capture the demanding nature of the potential requirements for their efficient and safe integration in real-life operations. The control derivation process is based on state space representations applicable in most rotorcraft configurations and incorporate the effects of external disturbances. Exploiting this modeling approach, two different unmanned rotorcraft configurations are presented and experimentally utilized. The formulated control strategy consists of a receding horizon scheme, the optimization process of which employs the minimum peak performance measure, while incorporating and accounting for the modeled dynamics and input and state constraints. This strategy aims to ensure the minimum possible deviation subject to the worstcase disturbance, while robustly respecting and satisfying the physical limitations of the system, or a set of mission-related requirements, as encoded in the constraints. The proposed framework is further augmented in order to provide obstacle avoidance capabilities into a unified scheme. Multiparametric methods were utilized in order to provide an explicit solution to the controller computation optimization problem, therefore allowing for fast real-time execution and seamless integration into any digital avionics system. The efficiency of the proposed strategy is validated via several experimental case studies on the two unmanned rotorcraft platforms. The experiments set consists of: trajectory tracking subject to atmospheric disturbances, slung load operations, fast highly disturbed maneuvers, collisions handling, as well as avoidance of known obstacles.

Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

“…That is, we require the ability to compute predictive control commands at sufficiently high rates to ensure stability of these agile systems. Fast MPC solution strategies can be divided into four categories: leveraging fast online optimization techniques [26], optimizing approximate formulations [15], explicit enumeration of equivalent controllers [2], and semi-explicit approaches [7,8,28]. In this work, we consider this last class of techniques due to the reduced reliance on online optimization in a critical control loop and their scalability to available computational resources [28].…”

Distributed model predictive control for unmanned aerial vehicles

“…Few of them utilized a three dimensional space but the authors used a simple linear model or did not consider the attitude problem of the UAV ( [44], [46], [48]). In general, only few works considered the nonlinear model in a three dimensional space but still the authors, in this approaches have not considered cooperation strategies and focused only on one UAV control problem ( [41], [42], [43]and [45]).…”

Sample-Based SMPC for Tracking Control of Fixed-Wing UAV

“…The combination of guidance and classical MPC schemes for UAV applications, in particular for rotorcraft and multi-rotor systems can be found in literature, see e.g. [9], [10]. For example, in [11] a combination of a Linear Quadratic Regulator and Nonlinear MPC (NMPC) is proposed for high-level dynamics.…”