Controls Research

Owned by Anthony Luo

Last updated: 2021-10-12

4 min read

1 Research round 1 (oct 7, 2021)
- 1.1 Summary
- 1.2 Sources (Controls)
- 1.3 Sources: Sensor Fusion
- 1.4 Sources (Simulation)
2 PDF’s (Is this allowed?)

Research round 1 (oct 7, 2021)

Mostly just trying to find good sources on where to start to look for hexacopter controls. Will highlight good informative sources, may link to new sources.

Reach out to Rodolfo Pellizzoni or Steven Smith after. Probably reach out to Steven Smith, Rodolfo Pellizzoni is more focused on embedded timing/architecture/rm/etc. Steven Smith seems (seems!) to have more direct research interests with robot motion planning and dynamic vehicle routing!

Summary

Two main approaches to hexacopter control: CCA (Classical Control Allocation) & WCA (Weighted Control Allocation).
- Control allocation deals with mapping the virtual control vector = [T L M N] T consisting of Thrust Roll Pitch & Yaw to individual motor speeds.
Most proposed implementations at this point use PID’s ?
- https://onlinelibrary-wiley-com.proxy.lib.uwaterloo.ca/doi/epdf/10.1002/acs.2955
- n-rotor drones are “nonlinear-underactuated systems”
Online or offline learning?
- Have a drone that can dynamically learn (single node model?
Moltirotor basics
To Do
- Global vs Local Reference Frame
- How to make controls layout agnostic (doesn’t matter whether drone is hex, quad, octa, etc.)
- Choose whether RC inputs control rate of change in axis or the angle
  - Controlling angle means that the drone will re-stabilize when sticks are released (stabilize mode) - better but harder to implement
  - Controlling rate means that the drone will not re-stabilizie after sticks are released - undesirable but easier to implement

Sources (Controls)

https://www.hindawi.com/journals/mpe/2020/9465153/
Describes & compares two methods of hexacopter control
- See Hexacopter Flight Performance Comparison with CCA vs WCA Control Allocation
- https://www.scopus.com/record/display.uri?eid=2-s2.0-85092198779&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=b7fa53d441962cc42fa7833639cbe68c&sot=b&sdt=b&sl=34&s=TITLE-ABS-KEY%28Hexacopter+Controls%29&relpos=26&citeCnt=0&searchTerm=
PID tuning of hexacopters
- https://ocul-wtl.primo.exlibrisgroup.com/permalink/01OCUL_WTL/3b6rcr/cdi_gale_infotracacademiconefile_A568601537
- https://onlinelibrary-wiley-com.proxy.lib.uwaterloo.ca/doi/epdf/10.1002/acs.2955
- Most proposed implementations use PID’s?
- N-rotor drones are “nonlinear-underactuated systems” what does thaht even MEAN
  - hard to use PID control directly?
- Other proposed artificial intelligence and dynamic properties? → single neuron PID.
  - → self tuning PID’s (basically single neuron).
  - online tuning of PID ?
  - → agaussion potential function network PID or FPGN NN ?
  - → gradient descent based methodology for online adjustments.
- This paper proposes an adaptive neural PID
  - can be used in any multilple-input-multiple-output (MIMO) nonlinear system.
  - exact model of the system does not need to be known.
  - design is straightforward (totally hahahahahahahaha) and rooted in the topology of adynamic NN (adaptable)
  - can be adjusted online (which we won’t be doing either? unless comms has something spicy).
- Then gets into really fancy math that I don’t quite understand.
Navigation and Autonomous Control of a Hexacopter in Indoor Environments
- https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=4359940&fileOId=4359943
- Uses ArduCopter 3DR Hexa B
- model-based approach using Matlab Simulink.
- → MAINLY FOCUSES ON HOW DATA FROM AN IMU AIDS CV AND GPS DENIED ENVIRONMENT
  - this is prettty important for us
- they use a simulated model (which we also want to do).
- Tells us Sigma point Kalman filter outperforms Extended Kalman Filter.
  - nonlinear kalman filters in general seem more popular.
- Generally use PID and back-stepping control.
Backstepping control for hexacopter UAV’s
- https://ocul-wtl.primo.exlibrisgroup.com/permalink/01OCUL_WTL/3b6rcr/cdi_doaj_primary_oai_doaj_org_article_94b8abf479f4432ba1b94ef54a58040d
Mathematical Modelling & control of a hexacopter
- https://ocul-wtl.primo.exlibrisgroup.com/discovery/fulldisplay?docid=cdi_ieee_primary_6564793&context=PC&vid=01OCUL_WTL:WTL_DEFAULT&lang=en&search_scope=OCULDiscoveryNetwork&adaptor=Primo Central&tab=OCULDiscoveryNetwork&query=any,contains,hexacopter control&offset=10
Automatic Hexacopter tuning
- https://ocul-wtl.primo.exlibrisgroup.com/discovery/fulldisplay?docid=cdi_ieee_primary_8107127&context=PC&vid=01OCUL_WTL:WTL_DEFAULT&lang=en&search_scope=OCULDiscoveryNetwork&adaptor=Primo Central&tab=OCULDiscoveryNetwork&query=any,contains,hexacopter control&offset=10
Implementation of mathematical model for hexacopter systems
- https://ocul-wtl.primo.exlibrisgroup.com/discovery/fulldisplay?docid=cdi_ieee_primary_9011842&context=PC&vid=01OCUL_WTL:WTL_DEFAULT&lang=en&search_scope=OCULDiscoveryNetwork&adaptor=Primo Central&tab=OCULDiscoveryNetwork&query=any,contains,hexacopter control&offset=0
Navigation and Autonomous Control of a Hexacopter in Indoor Environments
- https://lup.lub.lu.se/luur/download?func=downloadFile&recordOId=4359940&fileOId=4359943
- tells us a bit about the maths behind modelling → might be useful if we decide to matlab it?
- also tells us about some estimation & control theory. → might be quite nice actually.
Dynamic modeling and Control of a HexaRotor using Linear and Nonlinear Methods
- https://www.ijais.org/research/volume9/number5/moussid-2015-ijais-451411.pdf
- also quite good maths
- introduced simple PID control for the hexarotor
- also introduced attittude controlers & backstepping controllers & sliding-mode control
Dynamic Analysis of a Hexacopter controlled via LQR-PI
- https://www.researchgate.net/publication/260829387_Dynamic_Analysis_of_a_Hexacopter_Controlled_via_LQR-PI
- no actual clue what this does but PI control vs PID is interestinggggg.
- also brings in a mathematical model to simulate.
  - has been verified in simulink/matlab.
Real time tuning of hexacopter controls
- https://link.springer.com/article/10.1007%2Fs10846-021-01441-y
A really FANCY ML way of getting controls (probably too fancy)
- https://ocul-wtl.primo.exlibrisgroup.com/discovery/openurl?institution=01OCUL_WTL&vid=01OCUL_WTL:WTL_DEFAULT&%3Fsid=Elsevier:Scopus&_service_type=getFullTxt&issn=21682216&isbn=&volume=51&issue=4&spage=2525&epage=2535&pages=2525-2535&artnum=8721510&date=2021&id=doi:10.1109%2FTSMC.2019.2915950&title=IEEE Transactions on Systems, Man, and Cybernetics: Systems&atitle=Stable Adaptive Controller Based on Generalized Regression Neural Networks and Sliding Mode Control for a Class of Nonlinear Time-Varying Systems&aufirst=A.J.&auinit=A.J.&auinit1=A&aulast=Al-Mahasneh
Hexacopter Trajectory planning (not very relevant to us but maybe more for PM)
- https://www.scopus.com/record/display.uri?eid=2-s2.0-85092492931&origin=resultslist&sort=plf-f&src=s&sid=b7fa53d441962cc42fa7833639cbe68c&sot=b&sdt=b&sl=34&s=TITLE-ABS-KEY%28Hexacopter+Controls%29&relpos=13&citeCnt=2&searchTerm=
Set of articles regarding the programming of an Attitude Manager for a drone
- https://reefwing.medium.com/how-to-write-your-own-flight-controller-software-part-4-8d4c9ce4319
Paper detailing use of Simulink for PID tuning and control
- https://www.theseus.fi/bitstream/handle/10024/333992/Usman_Muhammad.pdf?sequence=2&isAllowed=y

Sources: Sensor Fusion

Sensor Fusion - Required to compute roll/pitch/yaw angle based on the accelerometer and gyro values of the IMU
- Madgwick
- Mahoney
- FIR

Sources (Simulation)

Matlab and simulink Hexacopter simulation?
- https://www.irjet.net/archives/V4/i3/IRJET-V4I3426.pdf

PDF’s (Is this allowed?)