Autonomous navigation and control of magnetic microcarriers using potential field algorithm and adaptive non-linear PID DOI Creative Commons
Mohamed Sallam, Mohamed Α. Shamseldin, Fanny Ficuciello

et al.

Frontiers in Robotics and AI, Journal Year: 2024, Volume and Issue: 11

Published: Aug. 13, 2024

Microparticles are increasingly employed as drug carriers inside the human body. To avoid collision with environment, they reach their destination following a predefined trajectory. However, due to various disturbances, tracking control of microparticles is still challenge. In this work, we propose use an Adaptive Nonlinear PID (A-NPID) controller for trajectory microparticles. A-NPID allows gains be continuously adjusted satisfy performance requirements at different operating conditions. An in-vitro study conducted verify proposed where microparticle 100 μ m diameter put navigate through open fluidic reservoir virtual obstacles. Firstly, collision-free generated using path-planning algorithm. Secondly, dynamic model, when moving under influence external forces, derived, and design law. The successfully allowed particle autonomously reference in presence varying environmental Moreover, could its targeted position minimal steady-state error 4 id="m2">μ m. A degradation was observed only used absence adaptive terms. results have been verified by simulation experimentally.

Language: Английский

Research on Real-Time Motion Control Strategy of Robotic Arm Based on Deep Learning DOI
Hui Gao

Lecture notes on data engineering and communications technologies, Journal Year: 2025, Volume and Issue: unknown, P. 573 - 584

Published: Jan. 1, 2025

Language: Английский

Citations

0
Adaptive Cruise Control of the Autonomous Vehicle Based on Sliding Mode Controller Using Arduino and Ultrasonic Sensor DOI Open Access
Rachid Alika, El Mehdi Mellouli, El Houssaine Tissir

et al.

Journal of Robotics and Control (JRC), Journal Year: 2024, Volume and Issue: 5(1), P. 301 - 311

Published: Feb. 6, 2024

This article will focus on adaptive cruise control in autonomous automobiles. The inputs are the safety distance which determines thanks to conditions set depending value, measured distance, longitudinal speed of automobile itself, output is desired acceleration. objective follow vehicles front with safety, according by ultrasonic sensor, and maintain a between greater than we have determined. For this, used super twisting sliding mode controller (STSMC) non-singular terminal (NTSMC) based neural network applied system. able approximate exponential reaching law term parameter NTSMC compensate for uncertainties perturbations. An system prototype was produced tested using an sensor measure two automobiles, Arduino board as microcontroller implement our program, four DCs motors actuators move or stop host vehicle. processed code Simulink Matlab, efficiency robustness these controllers excellent, demonstrated low velocity error value. can be enhanced improving STSMC controllers, chosen their robustness.

Language: Английский

Citations

2
Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization DOI Open Access
Mohamed Α. Shamseldin

Journal of Robotics and Control (JRC), Journal Year: 2023, Volume and Issue: 4(5), P. 643 - 649

Published: Sept. 16, 2023

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

Language: Английский

Citations

5
Data-Driven Inverse Kinematics Approximation of a Delta Robot with Stepper Motors DOI Creative Commons
Anni Zhao, Arash Toudeshki, Reza Ehsani

et al.

Robotics, Journal Year: 2023, Volume and Issue: 12(5), P. 135 - 135

Published: Sept. 30, 2023

The Delta robot is a parallel that over-actuated and has highly nonlinear dynamic model, which poses significant challenge to its control design. inverse kinematics maps the motor angles position of end effector extremely important for design robot. It been experimentally shown geometry-based not accurate enough capture dynamics due manufacturing component errors, measurement joint flexibility, backlash, friction, etc. To address this issue, we propose neural network model approximate with stepper motors. trained randomly sampled experimental data implemented on hardware in an open-loop trajectory tracking. Extensive results show achieves excellent performance terms tracking under different operation conditions, outperforms model. A critical numerical observation indicates networks specific fall short anticipated lack data. Conversely, random rich are quite robust uncertainties compared kinematics.

Language: Английский

Citations

4
Autonomous navigation and control of magnetic microcarriers using potential field algorithm and adaptive non-linear PID DOI Creative Commons
Mohamed Sallam, Mohamed Α. Shamseldin, Fanny Ficuciello

et al.

Frontiers in Robotics and AI, Journal Year: 2024, Volume and Issue: 11

Published: Aug. 13, 2024

Microparticles are increasingly employed as drug carriers inside the human body. To avoid collision with environment, they reach their destination following a predefined trajectory. However, due to various disturbances, tracking control of microparticles is still challenge. In this work, we propose use an Adaptive Nonlinear PID (A-NPID) controller for trajectory microparticles. A-NPID allows gains be continuously adjusted satisfy performance requirements at different operating conditions. An in-vitro study conducted verify proposed where microparticle 100 μ m diameter put navigate through open fluidic reservoir virtual obstacles. Firstly, collision-free generated using path-planning algorithm. Secondly, dynamic model, when moving under influence external forces, derived, and design law. The successfully allowed particle autonomously reference in presence varying environmental Moreover, could its targeted position minimal steady-state error 4 id="m2">μ m. A degradation was observed only used absence adaptive terms. results have been verified by simulation experimentally.

Language: Английский

Citations

0