PMID- 35009880
OWN - NLM
STAT- MEDLINE
DCOM- 20220112
LR  - 20220114
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 1
DP  - 2022 Jan 3
TI  - Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model 
      of an Overhead Traveling Crane.
LID - 10.3390/s22010339 [doi]
LID - 339
AB  - This paper proposes a multi-gene genetic programming (MGGP) approach to 
      identifying the dynamic prediction model for an overhead crane. The proposed 
      method does not rely on expert knowledge of the system and therefore does not 
      require a compromise between accuracy and complex, time-consuming modeling of 
      nonlinear dynamics. MGGP is a multi-objective optimization problem, and both the 
      mean square error (MSE) over the entire prediction horizon as well as the 
      function complexity are minimized. In order to minimize the MSE an initial 
      estimate of the gene weights is obtained by using the least squares approach, 
      after which the Levenberg-Marquardt algorithm is used to find the local optimum 
      for a k-step ahead predictor. The method was tested on both a simulation model 
      obtained from the Euler-Lagrange equation with friction and the experimental 
      stand. The simulation and the experimental stand were trained with varying 
      control inputs, rope lengths and payload masses. The resulting predictor model 
      was then validated on a testing set, and the results show the effectiveness of 
      the proposed method.
FAU - Kusznir, Tom
AU  - Kusznir T
AUID- ORCID: 0000-0002-6353-0311
AD  - Department of Manufacturing Systems, Faculty of Mechanical Engineering and 
      Robotics, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 
      Krakow, Poland.
FAU - Smoczek, Jaroslaw
AU  - Smoczek J
AD  - Department of Manufacturing Systems, Faculty of Mechanical Engineering and 
      Robotics, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 
      Krakow, Poland.
LA  - eng
PT  - Journal Article
DEP - 20220103
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - *Algorithms
MH  - Computer Simulation
MH  - Least-Squares Analysis
MH  - *Nonlinear Dynamics
PMC - PMC8749516
OTO - NOTNLM
OT  - crane
OT  - genetic programming
OT  - nonlinear identification
COIS- The authors declare no conflict of interest.
EDAT- 2022/01/12 06:00
MHDA- 2022/01/13 06:00
PMCR- 2022/01/03
CRDT- 2022/01/11 01:08
PHST- 2021/11/29 00:00 [received]
PHST- 2021/12/19 00:00 [revised]
PHST- 2021/12/30 00:00 [accepted]
PHST- 2022/01/11 01:08 [entrez]
PHST- 2022/01/12 06:00 [pubmed]
PHST- 2022/01/13 06:00 [medline]
PHST- 2022/01/03 00:00 [pmc-release]
AID - s22010339 [pii]
AID - sensors-22-00339 [pii]
AID - 10.3390/s22010339 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Jan 3;22(1):339. doi: 10.3390/s22010339.