Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator

Loucif, Fatiha; Kechida, Sihem; Sebbagh, Abdennour

doi:10.1007/s40430-019-2074-3

Cited by 168 publications

(33 citation statements)

References 38 publications

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“…The connecting rod movement, part of the kinetic energy affected can be ignored (Liu et al, 2018;Nabavi et al, 2019). Aiming the complexity of the dynamic model, the coupling terms are not considered (Wen et al, 2017;Loucif et al, 2020;Tan et al, 2020), choosing q q 1 q 2 q 3 q 4 T θ 1 θ 2 θ 3 θ 4 T as the generalized coordinate, Eq. 1 is obtained as the simplified dynamic equation:…”

Section: Mathematical Modelmentioning

confidence: 99%

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Liu

Yun

et al. 2022

Front. Bioeng. Biotechnol.

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With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations, and random noise. Therefore, it is essential to improve the robust and accuracy of the controller. In this article, a self-tuning particle swarm optimization (PSO) fuzzy PID positioning controller is designed based on fuzzy PID control. The quantization and scaling factors in the fuzzy PID algorithm are optimized by PSO in order to achieve high robustness and high accuracy of the manipulator. First of all, a mathematical model of the manipulator is developed, and the manipulator positioning controller is designed. A PD control strategy with compensation for gravity is used for the positioning control system. Then, the PID controller parameters dynamically are minute-tuned by the fuzzy controller 1. Through a closed-loop control loop to adjust the magnitude of the quantization factors–proportionality factors online. Correction values are outputted by the modified fuzzy controller 2. A quantization factor–proportion factor online self-tuning strategy is achieved to find the optimal parameters for the controller. Finally, the control performance of the improved controller is verified by the simulation environment. The results show that the transient response speed, tracking accuracy, and follower characteristics of the system are significantly improved.

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Section: Mathematical Modelmentioning

confidence: 99%

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Liu

Yun

et al. 2022

Front. Bioeng. Biotechnol.

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“…There are many optimization studies in the literature regarding the control of robot manipulators. In these studies, many different optimization methods such as Gravitational Search Algorithm (GSA), Genetic Algorithm (GA), D Star (D*) Algorithm, Simulated Annealing (SA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Differential Evolution (DE) Algorithm, Whale Optimization Algorithm (WOA) have been studied (Beşkirli & Tefek, 2019;Demydyuk & Hoshovs'ka, 2019;Erdogmus & Toz, 2012;Loucif, Kechida, & Sebbagh, 2020;Nadir & Mohammed, 2018;Oliveira, Oliveira, Boaventura-Cunha, & Pinho, 2017;Qiang, Xuhua, Ting, Xiaoxia, & Jianpei, 2019;Raheem & Hameed, 2019;Soltanpour & Khooban, 2013;Zhou, Wang, Zhu, Wang, & Yang, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Bees Algorithm Approach to Determining SMC Controller Parameters for the Position Control of a SCARA Robot Manipulator

Ilgen¹,

Durdu²,

Gülbahçe³

et al. 2022

European Journal of Science and Technology

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In this study, position control of a SCARA robot manipulator is investigated using the sliding mode control (SMC) method based on parameter optimization using The Bees Algorithm. The modeling the SCARA manipulator is conducted in MSC Adams and the control implementation is carried out in MATLAB software. The numerical model of the SCARA manipulator is acquired by setting up a virtual prototype on MSC Adams software. In addition, the inverse kinematic equations of the SCARA manipulator are formed using Matlab/Simulink software in order to check the accuracy of the created virtual prototype. In addition, the SMC controller parameters are optimized with The Bees Algorithm to get better results. Then, the control performance of the system is examined on the virtual prototype using MSC Adams-MATLAB co-simulation. Moreover, Genetic Algorithm, another meta-heuristic method, is used for parameter optimization and the performance of The Bees Algorithm is compared with the results obtained. As a result, it has been observed that The Bees Algorithm can be used in studies related to the control of robotic systems.

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“…Robustness here refers to the ability to produce good dynamic behavior in the face of modelling errors and unmodelled dynamics of the robot manipulator [5,6]. Loucif and Kechida [7] and Elkhateeb et al [8] used a whale optimization algorithm and an artificial bee colony algorithm, respectively, to optimize the parameters of the proportion integral differential (PID) controller, improve the trajectory tracking accuracy of the robot manipulator under unmodeled dynamics, and make the controller have a certain robustness. In order to model the control process of the robot manipulator more accurately, Ardeshiri et al [9,10] proposed a fractional order fuzzy PID controller.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Proportional Integral Robust Control of an Uncertain Robotic Manipulator Based on Deep Deterministic Policy Gradient

Huang

Xiao

et al. 2021

Mathematics

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An adaptive proportional integral robust (PIR) control method based on deep deterministic policy gradient (DDPGPIR) is proposed for n-link robotic manipulator systems with model uncertainty and time-varying external disturbances. In this paper, the uncertainty of the nonlinear dynamic model, time-varying external disturbance, and friction resistance of the n-link robotic manipulator are integrated into the uncertainty of the system, and the adaptive robust term is used to compensate for the uncertainty of the system. In addition, dynamic information of the n-link robotic manipulator is used as the input of the DDPG agent to search for the optimal parameters of the proportional integral robust controller in continuous action space. To ensure the DDPG agent’s stable and efficient learning, a reward function combining a Gaussian function and the Euclidean distance is designed. Finally, taking a two-link robot as an example, the simulation experiments of DDPGPIR and other control methods are compared. The results show that DDPGPIR has better adaptive ability, robustness, and higher trajectory tracking accuracy.

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Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator

Cited by 168 publications

References 38 publications

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

The Bees Algorithm Approach to Determining SMC Controller Parameters for the Position Control of a SCARA Robot Manipulator

Adaptive Proportional Integral Robust Control of an Uncertain Robotic Manipulator Based on Deep Deterministic Policy Gradient

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