Nonlinear Model Predictive Control of a Hydraulic Forestry Crane

Jouko Kalmari

Nonlinear Model Predictive Control of a Hydraulic Forestry Crane

Julkaisun otsikon käännös: Hydraulisen metsäkonenosturin malliprediktiivinen säätö

Jouko Kalmari

Sähkötekniikan ja automaation laitos

Tutkimustuotos: Doctoral Thesis › Collection of Articles

Abstrakti

Metsänhoito on koneellistunut merkittävästi viimeisten vuosikymmenien aikana. Pohjoismaissa laajasti käytössä olevat hakkuukoneet kaatavat puut, karsivat ja katkovat ne tehokkaasti. Kiinnostus myös muiden metsänhoidollisten toimien koneellistamiseen on herännyt. Koneellistamisella voitaisiin lisätä tuottavuutta ja vähentää työvoiman tarvetta. Automaation lisääminen metsäkoneisiin voisi olla seuraava johdonmukainen askel. Metsäkoneet on yleensä varustettu hydraulisella nosturilla, jonka päähän varsinainen metsänhoitoon suunniteltu työkalu on kytketty. Jos automaattisia tai puoliautomaattisia metsäkoneita kehitetään, on nosturin säätö oleellista. Koska puomin päässä oleva työkalu pääsee yleensä heilumaan vapaasti, täytyy myös sen liike ottaa huomioon. Työn tavoitteena on esittää ratkaisu siihen, miten puomia voidaan ohjata samanaikaisesti työkalun heiluntaa vaimentaen. Tämä mahdollistaa automaatioasteen ja tuottavuuden lisäämisen. Päähypoteesi on, että metsäkoneen puomia voidaan ohjata käyttämällä epälineaarista malliprediktiivista säädintä (NMPC). Tämä säädin seuraa etukäteen määrättyä polkua ja vaimentaa aktiivisesti työkalun heilahteluja. Toinen hypoteesi on, että työkalun heilahtelut voidaan estimoida inertiaalisten mittausten avulla. Kolmas hypoteesi on, että varhaisperkaus on metsänhoitotoimenpide, jossa voidaan hyödyntää automaattista puomia yhdessä konenäön kanssa. Epälineaarinen malliprediktiivinen säädin on säätömenetelmä, joka kykenee ohjaamaan dynamiikaltaan epälineaarisia järjestelmiä, joissa on tila- tai ohjausrajoituksia. Ohjaustrajektori ratkaistaan jokaisella ajanhetkellä optimointimenetelmien avulla. Järjestelmän estimointi ja säätö perustuvat samoihin dynaamisiin malleihin. Kuormantuntevan hydrauliikan takia hydraulisen järjestelmän malli on suhteellisen yksinkertainen. Kehitetyn menetelmän suorituskykyä arvioitiin kokeiden avulla. Kokeissa käytettiin kolmea erilaista tavoitepolkua ja erilaisia puomin nopeuksia. Tulokset osoittavat, että esitetty menetelmä pystyy ohjaamaan puomin päätä kohtuullisella tarkkuudella ja samaan aikaan vaimentamaan työkalun heilahtelun. Myös kehitetyt estimaattorit toimivat hyvin.

Julkaisun otsikon käännös	Hydraulisen metsäkonenosturin malliprediktiivinen säätö
Alkuperäiskieli	Englanti
Pätevyys	Tohtorintutkinto
Myöntävä instituutio	Aalto-yliopisto
Valvoja/neuvonantaja	Visala, Arto, Vastuuprofessori Backman, Juha, Ohjaaja
Kustantaja	Aalto University
Painoksen ISBN	978-952-60-6324-9
Sähköinen ISBN	978-952-60-6325-6
Tila	Julkaistu - 2015
OKM-julkaisutyyppi	G5 Tohtorinväitöskirja (artikkeli)

Tutkimusalat

heiluntaa vähentävä säätö
metsäkoneet
polun seuranta
tilan estimointi
hydrauliset järjestelmät

Pääsy asiakirjaan

http://urn.fi/URN:ISBN:978-952-60-6325-6Lisenssi: Määrittelemätön

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@phdthesis{67739671cdc549318870bc1dc139e692,

title = "Nonlinear Model Predictive Control of a Hydraulic Forestry Crane",

abstract = "In forestry, the level of mechanization has increased significantly in recent decades. Modern forest harvesters, used widely in the Nordic countries, are capable of efficiently felling, delimbing and bucking trees. There is also interest in mechanizing other silvicultural tasks to increase the productivity and decrease the demand for labor. Increasing the level of automation in forest machines could be the next logical step. Forest machines are usually equipped with some sort of hydraulic crane. A variety of different silvicultural tasks can be performed using different purpose-built tools that are attached to the tip of the boom of the forest machine. Therefore, control of the boom is essential if automatic or semiautomatic forest machines are to be developed. The tool is usually attached to the boom so that it can sway relatively freely, therefore the motion of the tool should also be taken into account when designing automatic control. The main objective of this thesis is to propose a solution to the problem of controlling the hydraulic crane while simultaneously damping the swaying of the tool. This makes it possible to increase the level of automation and productivity. The main hypothesis is that the hydraulic forestry crane can be controlled using nonlinear model predictive control (NMPC), a predefined path can be followed, and the unwanted oscillations of the tool can be damped. The second hypothesis is that the swaying of the tool can be estimated using inertial measurements. The third hypothesis is that early cleaning is a forestry operation where automatic forestry crane can be used together with machine perception. NMPC is a control method designed for systems having nonlinear dynamics and constraints in the states and controls. In NMPC, the optimal control trajectory is solved at each control interval using some optimization algorithm. The estimation and control of the system are based on the same dynamic models. The model of the hydraulic cylinders is relatively simple as a load sensing hydraulic system was used. Experiments using real hardware were conducted in order to test the performance of the NMPC. Three different target paths and different boom tip velocities were tested. The results show that the proposed method is able to control the tip of the boom with reasonable accuracy while simultaneously damping the oscillations of the tool. In addition, the developed estimators seem to function quite well.",

keywords = "anti-sway control, forest machines, path tracking, state estimation, hydraulic systems, heiluntaa v{\"a}hent{\"a}v{\"a} s{\"a}{\"a}t{\"o}, mets{\"a}koneet, polun seuranta, tilan estimointi, hydrauliset j{\"a}rjestelm{\"a}t, anti-sway control, forest machines, path tracking, state estimation, hydraulic systems",

author = "Jouko Kalmari",

year = "2015",

language = "English",

isbn = "978-952-60-6324-9",

series = "Aalto University publication series DOCTORAL DISSERTATIONS",

publisher = "Aalto University",

number = "111",

address = "Finland",

school = "Aalto University",

}

TY - THES

T1 - Nonlinear Model Predictive Control of a Hydraulic Forestry Crane

AU - Kalmari, Jouko

PY - 2015

Y1 - 2015

N2 - In forestry, the level of mechanization has increased significantly in recent decades. Modern forest harvesters, used widely in the Nordic countries, are capable of efficiently felling, delimbing and bucking trees. There is also interest in mechanizing other silvicultural tasks to increase the productivity and decrease the demand for labor. Increasing the level of automation in forest machines could be the next logical step. Forest machines are usually equipped with some sort of hydraulic crane. A variety of different silvicultural tasks can be performed using different purpose-built tools that are attached to the tip of the boom of the forest machine. Therefore, control of the boom is essential if automatic or semiautomatic forest machines are to be developed. The tool is usually attached to the boom so that it can sway relatively freely, therefore the motion of the tool should also be taken into account when designing automatic control. The main objective of this thesis is to propose a solution to the problem of controlling the hydraulic crane while simultaneously damping the swaying of the tool. This makes it possible to increase the level of automation and productivity. The main hypothesis is that the hydraulic forestry crane can be controlled using nonlinear model predictive control (NMPC), a predefined path can be followed, and the unwanted oscillations of the tool can be damped. The second hypothesis is that the swaying of the tool can be estimated using inertial measurements. The third hypothesis is that early cleaning is a forestry operation where automatic forestry crane can be used together with machine perception. NMPC is a control method designed for systems having nonlinear dynamics and constraints in the states and controls. In NMPC, the optimal control trajectory is solved at each control interval using some optimization algorithm. The estimation and control of the system are based on the same dynamic models. The model of the hydraulic cylinders is relatively simple as a load sensing hydraulic system was used. Experiments using real hardware were conducted in order to test the performance of the NMPC. Three different target paths and different boom tip velocities were tested. The results show that the proposed method is able to control the tip of the boom with reasonable accuracy while simultaneously damping the oscillations of the tool. In addition, the developed estimators seem to function quite well.

AB - In forestry, the level of mechanization has increased significantly in recent decades. Modern forest harvesters, used widely in the Nordic countries, are capable of efficiently felling, delimbing and bucking trees. There is also interest in mechanizing other silvicultural tasks to increase the productivity and decrease the demand for labor. Increasing the level of automation in forest machines could be the next logical step. Forest machines are usually equipped with some sort of hydraulic crane. A variety of different silvicultural tasks can be performed using different purpose-built tools that are attached to the tip of the boom of the forest machine. Therefore, control of the boom is essential if automatic or semiautomatic forest machines are to be developed. The tool is usually attached to the boom so that it can sway relatively freely, therefore the motion of the tool should also be taken into account when designing automatic control. The main objective of this thesis is to propose a solution to the problem of controlling the hydraulic crane while simultaneously damping the swaying of the tool. This makes it possible to increase the level of automation and productivity. The main hypothesis is that the hydraulic forestry crane can be controlled using nonlinear model predictive control (NMPC), a predefined path can be followed, and the unwanted oscillations of the tool can be damped. The second hypothesis is that the swaying of the tool can be estimated using inertial measurements. The third hypothesis is that early cleaning is a forestry operation where automatic forestry crane can be used together with machine perception. NMPC is a control method designed for systems having nonlinear dynamics and constraints in the states and controls. In NMPC, the optimal control trajectory is solved at each control interval using some optimization algorithm. The estimation and control of the system are based on the same dynamic models. The model of the hydraulic cylinders is relatively simple as a load sensing hydraulic system was used. Experiments using real hardware were conducted in order to test the performance of the NMPC. Three different target paths and different boom tip velocities were tested. The results show that the proposed method is able to control the tip of the boom with reasonable accuracy while simultaneously damping the oscillations of the tool. In addition, the developed estimators seem to function quite well.

KW - anti-sway control

KW - forest machines

KW - path tracking

KW - state estimation

KW - hydraulic systems

KW - heiluntaa vähentävä säätö

KW - metsäkoneet

KW - polun seuranta

KW - tilan estimointi

KW - hydrauliset järjestelmät

KW - anti-sway control

KW - forest machines

KW - path tracking

KW - state estimation

KW - hydraulic systems

M3 - Doctoral Thesis

SN - 978-952-60-6324-9

T3 - Aalto University publication series DOCTORAL DISSERTATIONS

PB - Aalto University

ER -

Nonlinear Model Predictive Control of a Hydraulic Forestry Crane

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