The mechanical arm control based on harmony search genetic algorithm

He, Zhengjie; Pan, Bo; Liu, Zongze; Tang, Xinfeng

doi:10.1007/s10586-017-1053-7

Cited by 7 publications

(8 citation statements)

References 19 publications

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“…The nature-inspired algorithms have been widely studied and developed, which can be generally classified as evolutionary, swarm intelligence, and physics-based algorithms. Evolutionary algorithms are represented by the Genetic Algorithm (GA) [22], differential evolution algorithm [23], etc., while the swarm intelligence algorithms are represented by the Particle Swarm Optimization (PSO) algorithm [24], Grey Wolf Optimization (GWO) algorithm [25], etc. Water wave optimization [26] and heat transfer search [27] are included in the physics-based algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Evolutionary algorithms GA [22], differential evolution algorithm [23] Parameter optimization of mechanical arm [22], crane systems controlling [23], etc.…”

Section: Examples Applications (Not Limited To These Applications) Pr...mentioning

confidence: 99%

“…Some metaheuristic algorithms used to adjust the PID parameters include Adaptive Fuzzy Logic Controller (AFLC) [28], GA [22], Natural Annealing Particle Swarm Optimization (NAPSO) [24], adaptive particle swarm optimization (APSO) [29], etc. When using an algorithm to tune PID parameters, a physical model of the controlled object needs to be created.…”

Section: Obedience To Physical Lawsmentioning

confidence: 99%

“…According to the properties of the power spectral density (PSD) function, if the input signal meets the white noise condition, the self-power spectral density matrix of the response signal can be obtained as Equation (22). complex conjugate; n is the modal order of the system; and   r  and   r L are the rth modal shape and modal participation factor, respectively.…”

Section: Xmentioning

confidence: 99%

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Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Liu,

Lu,

Yonezawa

et al. 2023

Mathematics

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Dual-ball-screw feed drive systems (DBSFDSs) are designed for most high-end manufacturing equipment. However, the mismatch between the dynamic characteristic parameters (e.g., stiffness and inertia) and the P-PI cascade control method reduces the accuracy of the DBSFDSs owing to the structural characteristic changes in the motion. Moreover, the parameters of the P-PI cascade controller of the DBSFDSs are always the same even though the two axes have different dynamic characteristics, and it is difficult to tune two-axis parameters simultaneously. A new application of the combination of the grey wolf optimization (GWO) algorithm and the P-PI cascade controller is presented to solve these problems and enhance the motion performance of DBSFDSs. The novelty is that the flexible coupling model and dynamic stiffness obtained from the motor current can better represent the two-axis coupling dynamic characteristics, and the GWO algorithm is used to adjust the P-PI controller parameters to address variations in the positions of the moving parts and reflect characteristic differences between the two axes. Comparison of simulation and experimental results validated the superiority of the proposed controller over existing ones in practical applications, showing a decrease in the tracking error of the tool center and non-synchronization error of over 34% and 39%, respectively.

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Section: Related Workmentioning

confidence: 99%

“…Evolutionary algorithms GA [22], differential evolution algorithm [23] Parameter optimization of mechanical arm [22], crane systems controlling [23], etc.…”

Section: Examples Applications (Not Limited To These Applications) Pr...mentioning

confidence: 99%

Section: Obedience To Physical Lawsmentioning

confidence: 99%

Section: Xmentioning

confidence: 99%

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Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Liu,

Lu,

Yonezawa

et al. 2023

Mathematics

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show abstract

“…Hence, it would be interesting to solve a similar problem by considering the variable-length vectors in the harmony memory and perform statistical analysis of the solution for identical problems using both fixed and variable-length vectors. In another research, He et al [127] proposed the harmony search genetic method to tune the parameters of the PID controller of the mechanical arm controller. e mechanical arm is a nonlinear control system with a number of multivariables and has a complicated structure and uncertain parameters.…”

Section: 23mentioning

confidence: 99%

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

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In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

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Modeling and drilling parameters optimization on burr height using harmony search algorithm in low-frequency vibration-assisted drilling

Shao-min

Zhang

Shao

et al. 2018

Int J Adv Manuf Technol

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The mechanical arm control based on harmony search genetic algorithm

Cited by 7 publications

References 19 publications

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Modeling and drilling parameters optimization on burr height using harmony search algorithm in low-frequency vibration-assisted drilling

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