Citation: Giernacki, W. Minimum
Energy Control of Quadrotor UAV:
Synthesis and Performance Analysis
of Control System with
Neurobiologically Inspired
Intelligent Controller (BELBIC).
Energies 2022, 15, 7566. https://
doi.org/10.3390/en15207566
Academic Editor: Francisco
Manzano Agugliaro
Received: 9 September 2022
Accepted: 9 October 2022
Published: 13 October 2022
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Article
Minimum Energy Control of Quadrotor UAV: Synthesis
and Performance Analysis of Control System with
Neurobiologically Inspired Intelligent Controller (BELBIC)
Wojciech Giernacki
Faculty of Automatic Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence,
Poznan University of Technology, ul. Piotrowo 3a, 60-965 Poznan, Poland; wojciech.giernacki@put.poznan.pl
Abstract:
There is a strong trend in the development of control systems for multi-rotor unmanned
aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight
time. The aim of this article is to shed light on the problem of shaping control signals in terms of
energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional
learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from
the feedback loop of the reference signal tracking system, shows a high learning ability to develop
an appropriate control action with low computational complexity. This extends the capabilities of
commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient
manner. The problem of controller tuning is treated here as a problem of optimization of the cost
function expressing control signal effort and maximum precision flight. The article introduces several
techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters.
The performance of the system is comprehensively analyzed based on results of the experiments
conducted for the quadrotor model.
Keywords:
UAV; quadrotor; optimization; minimum energy control; brain emotional learning;
BELBIC
1. Introduction
1.1. Background
In recent years, there has been a growing interest in unmanned aerial vehicles
(UAVs) [
1
,
2
]. Among the various types of UAVs, multi-rotor robots are particularly interest-
ing due to their small size, good flight properties (including the possibility of hovering and
flying stably at very low speeds), and relatively low cost [
3
]. In each of the diverse missions
(transportation, agricultural, industrial, photogrammetry, reconnaissance, surveillance,
etc.), UAV features such as maximum flight time and smooth, non-overshooted flight tra-
jectories are in demand. These properties determine the safety of control of this inherently
unstable and underactuated plant. The appropriate selection of controllers and their proper
tuning are of prime importance since they allow the optimal use of highly limited energy
resources to generate the appropriate thrust and torques of the particular propulsion units
of the UAV.
Nowadays, numerous types of controllers are used in multidimensional UAV control
systems [
4
]. In addition to a number of advanced solutions in which the control system is
able to autonomously control the UAV with rapidly changing, time-varying aerodynamic
characteristics during flight (briefly characterized in [
5
]), techniques based on model predic-
tive control (MPC) [
6
], fuzzy control [
7
], sliding mode control (SMC) [
8
], and adaptive fault-
tolerant control [
9
] are widely used. In addition to these techniques, many new ones have
appeared [
10
–
13
] which are related to advanced intelligent control of nonlinear systems
and may be easily adaptable to UAVs. However, the most common commercially avail-
able multi-rotor UAVs use solutions based on classical fixed-value feedback controllers of
Energies 2022, 15, 7566. https://doi.org/10.3390/en15207566 https://www.mdpi.com/journal/energies
