Optimization of trajectory tracking control of 3-DOF translational robot use PSO method based on inverse dynamics control for surgery application

Bach, Ngoc Pham Van; Quan, Hai; Trung, Thanh Bui

doi:10.21595/jve.2021.21997

Cited by 4 publications

(6 citation statements)

References 21 publications

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“…It has advantages such as strong global optimization ability and fast convergence speed (Xiao et al , 2022). The particles update their positions in real-time by tracking two positions, their individual best position P i and the population best position P g (Bach et al , 2021). In each iteration, the particles update their velocities and positions based on individual best value and global best value, as shown in equations (3) and (4): where ω is the inertia weight; P id is the individual best value, P gd is the population best value, X id is the particle position, V id is the particle velocity, c 1 and c 2 are positive constants as learning factors, r 1 and r 2 are random numbers distributed between 0 and 1.…”

Section: Parameters and Methodsmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Xiao,

Fu,

Wang

et al. 2024

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Purpose Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque. Design/methodology/approach The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied. Findings The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved. Originality/value PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

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Section: Parameters and Methodsmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Xiao,

Fu,

Wang

et al. 2024

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“…The higher and lower values of the deep NN model's initial weights and bias, as given in equation (12)…”

Section: 𝑦(𝑡 + 1) ≅ 𝑦 ̂(𝑡 + 1)mentioning

confidence: 99%

“…When compared to serial robots, parallel robots have clear advantages, such as their high speed and rigidity [5]- [9]. Delta robots are currently widely employed in the packaging business, the medical and pharmaceutical sectors, and surgery [10]- [12]. They are one of the most successful industrial parallel robots where it has high speed with satisfied accuracy.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

Shamseldin

2023

Journal of Robotics and Control

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This paper presents a new technique to design an inverse dynamic model for a delta robot experimental setup to obtain an accurate trajectory. The input/output data were collected using an NI DAQ card where the input is the random angles profile for the three-axis and the output is the corresponding measured torques. The inverse dynamic model was developed based on the deep neural network (NN) and the new COVID-19 optimization to find the optimal initial weights and bias values of the NN model. Due to the system uncertainty and nonlinearity, the inverse dynamic model is not enough to track accurately the preselected profile. So, the PD compensator is used to absorb the error deviation of the end effector. The experimental results show that the proposed inverse dynamic deep NN with PD compensator achieves good performance and high tracking accuracy. The suggested control was examined using two different methods. The spiral path is the first, with a root mean square error of 0.00258 m, while the parabola path is the second, with a root mean square error of 0.00152 m.

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“…The benefit of the sliding mode technique is that it offers good tracking performance throughout a range of position command operating points [16], [11]. On the other side, chattering phenomena can make it difficult to increase control accuracy and, in rare situations, it can lead to system instability [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

A New Self-Tuning Nonlinear PID Motion Control for One-Axis Servomechanism with Uncertainty Consideration

Shamseldin

Abdelghany

2023

Journal of Robotics and Control

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This paper introduces a new study for one-axis servomechanism with consideration the parameter variation and system uncertainty. Also, a new approach for high-performance self-tuning nonlinear PID control was developed to track a preselected profile with high accuracy. Moreover, a comparison study between the proposed control technique and the well-known controllers (PID and Nonlinear PID). The optimal control parameters were determined based on the COVID-19 optimization technique. The parameters of the servomechanism system changed randomly at a preselected range through the online simulation. The change of these parameters acts as the nonlinearity resources (friction, backlash, environmental effects) and system uncertainty. A comparative study between the linear and nonlinear models had been accomplished and investigated. The results show that the proposed controller can track several operating points with high accuracy, low rise time, and small overshoot.

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Optimization of trajectory tracking control of 3-DOF translational robot use PSO method based on inverse dynamics control for surgery application

Cited by 4 publications

References 21 publications

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

A New Self-Tuning Nonlinear PID Motion Control for One-Axis Servomechanism with Uncertainty Consideration

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