Adaptive Control Techniques for Improving the Performance of Robotic Systems

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Published: Sep 2, 2024
DOI: https://doi.org/10.29070/befc2332
Keywords:
adaptive control techniques, performance, robotic systems, monitor system characteristics, adjustments, external conditions, environmental demands, trajectory, disturbances, parameters, stabilize, flexible, healthcare, autonomous vehicles, industrial automation, materials required, approach, outcomes, further research, standardizing, benchmarking, hardware integration

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Authors

Ananya Sood

Student, St. Joseph’s Academy, Dehradun

Abstract

One of the best ways to boost the performance of robotic systems is through adaptive controlapproaches, which are quickly gaining popularity. Robots are now able to continuously monitor systemcharacteristics and make adjustments in response to changing external conditions and environmentaldemands thanks to these approaches. When applying these adaptive control procedures, roboticsystems improve their capacity to follow a trajectory, reject disturbances, adapt parameters, stabilize,and be flexible. Numerous robotic applications, including those for healthcare, autonomous vehicles,and industrial automation, all show benefits. The performance of robotic systems may be improved viaadaptive control, which is examined in this work along with the materials required, the approach, theoutcomes, and the potential for further research. There are also suggestions for standardizing,benchmarking, and talking about hardware integration.

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Article Details

Issue

Vol. 20 No. 1 (2023): Journal of Advances in Science and Technology (JAST)

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Articles

References

  1. Fan, D.D., Nguyen, J., Thakker, R., Alatur, N., Agha-mohammadi, A.A. and Theodorou, E.A., 2020, May. Bayesian learning-based adaptive control for safety critical systems. In 2020 IEEE international conference on robotics and automation (ICRA) (pp. 4093-4099). IEEE.
  2. Ouyang, Y., Dong, L., Xue, L. and Sun, C., 2019. Adaptive control based on neural networks for an uncertain 2-DOF helicopter system with input deadzone and output constraints. IEEE/CAA Journal of AutomaticaSinica, 6(3), pp.807-815.
  3. Huang, H., Yang, C. and Chen, C.P., 2020. Optimal robot–environment interaction under broad fuzzy neural adaptive control. IEEE Transactions on Cybernetics, 51(7), pp.3824-3835.
  4. Kebria, P.M., Khosravi, A., Nahavandi, S., Shi, P. and Alizadehsani, R., 2019. Robust adaptive control scheme for teleoperation systems with delay and uncertainties. IEEE transactions on cybernetics, 50(7), pp.3243-3253.
  5. Sun, N., Liang, D., Wu, Y., Chen, Y., Qin, Y. and Fang, Y., 2019. Adaptive control for pneumatic artificial muscle systems with parametric uncertainties and unidirectional input constraints. IEEE Transactions on Industrial Informatics, 16(2), pp.969-979.
  6. Luan, F., Na, J., Huang, Y. and Gao, G., 2019. Adaptive neural network control for robotic manipulators with guaranteed finite-time convergence. Neurocomputing, 337, pp.153-164.
  7. Koksal, N., An, H. and Fidan, B., 2020. Backstepping-based adaptive control of a quadrotor UAV with guaranteed tracking performance. ISA transactions, 105, pp.98-110.
  8. Na, J., Jing, B., Huang, Y., Gao, G. and Zhang, C., 2019. Unknown system dynamics estimator for motion control of nonlinear robotic systems. IEEE Transactions on Industrial Electronics, 67(5), pp.3850-3859.
  9. Hu, J., Bhowmick, P., Arvin, F., Lanzon, A. and Lennox, B., 2020. Cooperative control of heterogeneous connected vehicle platoons: An adaptive leader-following approach. IEEE Robotics and Automation Letters, 5(2), pp.977-984.