Intelligent Control of Rigid-Link Manipulators: A Systematic Review of Recent Advances and Future Trends
(1) * M. Y. Alwardat Mail (Peter the Great St. Petersburg Polytechnic University, Russian Federation)
(2) O. E.-L. M’bolo Mail (Peter the Great St. Petersburg Polytechnic University, Russian Federation)
(3) Y. Benslimane Mail (Peter the Great St. Petersburg Polytechnic University, Russian Federation)
(4) H. M. Alwan Mail (University of Technology, Iraq)
*corresponding author

Abstract


As robotic manipulators increasingly operate in dynamic and safety-critical environments, the need for intelligent control strategies that ensure adaptability, robustness, and real-time performance has become critical. While earlier reviews have addressed aspects of this domain, they often lacked systematic rigor, overlooked emerging hybrid and learning-based approaches, or provided limited quantitative synthesis. The research contribution is a PRISMA-compliant systematic review of 80 peer-reviewed studies on intelligent control of rigid-link manipulators (RLMs) published between 2016 and 2024, offering both qualitative and structured comparative analysis. The methods reviewed include PID, sliding mode control (SMC), fuzzy logic, artificial neural networks (ANN), reinforcement learning (RL), genetic algorithms (GA), and hybrid combinations. Studies were assessed according to methodological clarity, experimental validation, reported performance metrics, and publication impact. A comparative summary of 25 representative studies-selected based on citation impact, methodological rigor, and diversity of control approaches-highlights performance trade-offs and strengths across techniques. The findings indicate a growing shift toward hybrid intelligent controllers, which demonstrate enhanced adaptability in addressing nonlinear dynamics and uncertainties. However, most studies remain simulation-based, with limited real-world validation and reproducibility. Major research gaps include the lack of standardized benchmarking, insufficient explainability, and limited generalizability across application domains. These insights support the development of deployable, interpretable, and reliable robotic controllers, particularly for industrial automation and medical robotics, where transparency and safety are paramount.


Keywords


Intelligent Control; Rigid-Link Manipulators; Hybrid Methods; Experimental Validation; Systematic Review

   

DOI

https://doi.org/10.31763/ijrcs.v5i3.2019 		      

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10.31763/ijrcs.v5i3.2019 Abstract views : 1 | PDF views : 1

   

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References


[1] D. Rawat, M. K. Gupta, and A. Sharma, “Intelligent control of robotic manipulators: A comprehensive review,” Spatial Information Research, vol. 31, no. 3, pp. 345-357, 2023, https://doi.org/10.1007/s41324-022-00500-2.

[2] V. S. D. M. Sahu, P. Samal, and C. K. Panigrahi, “Modelling and control techniques of robotic manipulators: A review,” Materials Today: Proceedings, vol. 62, pp. 3484-3490, 2022, https://doi.org/10.1016/j.matpr.2021.10.009.

[3] V. Mohan, H. Chhabra, A. Rani, and V. Singh, “An expert 2DOF fractional order fuzzy PID controller for nonlinear systems,” Neural Computing and Applications, vol. 31, no. 8, pp. 4253-4270, 2019, https://doi.org/10.1007/s00521-017-3330-z.

[4] L. Jin, S. Li, J. Yu, and J. H. Park, “Robot manipulator control using neural networks: A survey,” Neurocomputing, vol. 285, pp. 23-34, 2018, https://doi.org/10.1016/j.neucom.2018.01.002.

[5] L. Zhang, W. Qi, Y. Hu, and Y. Chen, “Disturbance-observer-based fuzzy control for a robot manipulator using an EMG-driven neuromusculoskeletal model,” Complexity, vol. 2020, no. 1, pp. 1-10, 2020, https://doi.org/10.1155/2020/8814460.

[6] Z. Dachang, H. Pengcheng, D. Baolin, and Z. Puchen, “Adaptive nonsingular terminal sliding mode control of robot manipulator based on contour error compensation,” Scientific Reports, vol. 13, no. 1, p. 20765, 2023, https://doi.org/10.1038/s41598-023-27633-0.

[7] T. Wang, Z. Ruan, Y. Wang, and C. Chen, “Control strategy of robotic manipulator based on multi-task reinforcement learning,” Complex & Intelligent Systems, vol. 11, no. 3, pp. 2413-2425, 2025, https://doi.org/10.1007/s40747-025-01816-w.

[8] S. Xu, C. Zhang, and A. Mohammadzadeh, “Type-3 fuzzy control of robotic manipulators,” Symmetry, vol. 15, no. 2, p. 483, 2023, https://doi.org/10.3390/sym15020483.

[9] M. Moran-Armenta, J. Martínez-Carranza, and J. Muñoz-Gutiérrez, “Neural networks meet PID control: Revolutionizing manipulator regulation with gravitational compensation,” IFAC Journal of Systems and Control, vol. 32, p. 10036, 2025, https://doi.org/10.1016/j.ifacsc.2025.100306.

[10] D. Han, B. Mulyana, V. Stankovic, and S. Cheng, “A survey on deep reinforcement learning algorithms for robotic manipulation,” Sensors, vol. 23, no. 7, p. 3762, 2023, https://doi.org/10.3390/s23073762.

[11] A. A. Shahid, D. Piga, F. Braghin, and L. Roveda, “Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning,” Autonomous Robots, vol. 46, no. 3, pp. 483-498, 2022, https://doi.org/10.1007/s10514-022-10034-z.

[12] V. Tinoco, M. F. Silva, F. N. Santos, R. Morais, S. A. Magalhães, and P. M. Oliveira, “A review of advanced controller methodologies for robotic manipulators,” International Journal of Dynamics and Control, vol. 13, no. 1, p. 36, 2025, https://doi.org/10.1007/s40435-024-01533-1.

[13] S. I. Abdelmaksoud, M. H. Al-Mola, G. E. M. Abro and V. S. Asirvadam, "In-Depth Review of Advanced Control Strategies and Cutting-Edge Trends in Robot Manipulators: Analyzing the Latest Developments and Techniques," IEEE Access, vol. 12, pp. 47672-47701, 2024, https://doi.org/10.1109/ACCESS.2024.3383782.

[14] V. Tinoco, M. F. Silva, F. N. Santos, R. Morais, S. A. Magalhães, and P. M. Oliveira, "A review of advanced controller methodologies for robotic manipulators," International Journal of Dynamics and Control, vol. 13, no. 1, p. 36, 2025, https://doi.org/10.1007/s40435-024-01533-1.

[15] S. Romero et al., “Trajectory planning for robotic manipulators in automated palletizing: A comprehensive review,” Robotics, vol. 14, no. 5, p. 55, 2025, https://doi.org/10.3390/robotics14050055.

[16] Y. Liu, H. Zhang, Y. Li, and X. Chen, “Self-tuning control of manipulator positioning based on fuzzy PID and PSO algorithm,” Frontiers in Bioengineering and Biotechnology, vol. 9, p. 817723, 2022, https://doi.org/10.3389/fbioe.2021.817723.

[17] M. I. Azeez, A. M. M. Abdelhaleem, S. Elnaggar, K. A. F. Moustafa, and K. R. Atia, “Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced artificial bee colony algorithm,” Scientific Reports, vol. 13, no. 1, p. 11164, 2023, https://doi.org/10.1038/s41598-023-37895-3.

[18] M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” TheBMJ, vol. 372, 2021, https://doi.org/10.1136/bmj.n71.

[19] J. A. Sterne et al., “ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions,” TheBMJ, vol. 355, 2016, https://doi.org/10.1136/bmj.i4919.

[20] J. Kern, D. Marrero, and C. Urrea, “Fuzzy control strategies development for a 3-DoF robotic manipulator in trajectory tracking,” Processes, vol. 11, no. 12, p. 3267, 2023, https://doi.org/10.3390/pr11123267.

[21] J. Han, F. Wang, and C. Sun, "Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System," Applied Sciences, vol. 13, no. 2, p. 1046, 2023, https://doi.org/10.3390/app13021046.

[22] A. Muñoz-Vázquez, F. Gaxiola, F. Martínez-Reyes, and A. Manzo-Martínez, “A fuzzy fractional-order control of robotic manipulators with PID error manifolds,” Applied Soft Computing, vol. 85, p. 105826, 2019, https://doi.org/10.1016/j.asoc.2019.105646.

[23] B. Yilmaz, E. Tatlicioglu, A. Savran, and M. Alci, “Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators,” Applied Soft Computing, vol. 105, p. 107242, 2021, https://doi.org/10.1016/j.asoc.2021.107183.

[24] A. Kumar and V. Kumar, “Evolving an interval type-2 fuzzy PID controller for the redundant robotic manipulator,” Expert Systems with Applications, vol. 71, pp. 97-106, 2017, https://doi.org/10.1016/j.eswa.2016.12.029.

[25] H. Zhang, Z. Zhao, Y. Wei, Y. Liu, and W. Wu, "A Self-Tuning Variable Universe Fuzzy PID Control Framework with Hybrid BAS-PSO-SA Optimization for Unmanned Surface Vehicles," Journal of Marine Science and Engineering, vol. 13, no. 3, p. 558, 2025, https://doi.org/10.3390/jmse13030558.

[26] S. Li, Y. Zhang and L. Jin, "Kinematic Control of Redundant Manipulators Using Neural Networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2243-2254, 2017, https://doi.org/10.1109/TNNLS.2016.2574363.

[27] D. Galvan-Perez, H. Yañez-Badillo, F. Beltran-Carbajal, I. Rivas-Cambero, and A. Favela-Contreras, R. Tapia-Olvera, “Neural adaptive robust motion-tracking control for robotic manipulator systems,” Actuators, vol. 11, no. 9, p. 255, 2022, https://doi.org/10.3390/act11090255.

[28] F. Wang, Z. Q. Chao, L. B. Huang, H. Y. Li, and C. Q. Zhang, “Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode,” Cluster Computing, vol. 22, no. 3, pp. 5799-5809, 2019, https://doi.org/10.1007/s10586-017-1538-4.

[29] K. Guo, H. Zhang, C. Wei, H. Jiang, and J. Wang, “Novel sliding mode control of the manipulator based on a nonlinear disturbance observer,” Scientific Reports, vol. 14, no. 1, p. 77125, 2024, https://doi.org/10.1038/s41598-024-77125-y.

[30] L. Zhang, Y. Wang, Y. Hou and H. Li, "Fixed-Time Sliding Mode Control for Uncertain Robot Manipulators," IEEE Access, vol. 7, pp. 149750-149763, 2019, https://doi.org/10.1109/ACCESS.2019.2946866.

[31] H. Sai, Z. Xu, S. He, E. Zhang, and L. Zhu, "Adaptive nonsingular fixed-time sliding mode control for uncertain robotic manipulators under actuator saturation," ISA transactions, vol. 123, pp. 46-60, 2022, https://doi.org/10.1016/j.isatra.2021.05.011.

[32] A. T. Vo, T. N. Truong, Q. D. Le, and H. J. Kang, "Fixed-time sliding mode-based active disturbance rejection tracking control method for robot manipulators," Machines, vol. 11, no. 2, p. 140, 2023, https://doi.org/10.3390/machines11020140.

[33] O. O. Obadina, M. A. Thaha, Z. Mohamed, and M. H. Shaheed, "Grey-box modelling and fuzzy logic control of a Leader–Follower robot manipulator system: A hybrid Grey Wolf–Whale Optimisation approach," ISA transactions, vol. 129, pp. 572-593, 2022, https://doi.org/10.1016/j.isatra.2022.02.023.

[34] A. Kumar and V. Kumar, "Hybridized ABC-GA optimized fractional order fuzzy pre-compensated FOPID control design for 2-DOF robot manipulator," AEU-international journal of electronics and communications, vol. 79, pp. 219-233, 2017, https://doi.org/10.1016/j.aeue.2017.06.008.

[35] A. Kukker and R. Sharma, “Stochastic genetic algorithm-assisted fuzzy Q-learning for robotic manipulators,” Arabian Journal for Science and Engineering, vol. 46, no. 10, pp. 9527-9539, 2021, https://doi.org/10.1007/s13369-021-05379-z.

[36] M. H. Barhaghtalab, M. A. Sepestanaki, S. Mobayen, A. Jalilvand, A. Fekih, and V. Meigoli, "Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators," Applied Soft Computing, vol. 149, p. 110970, 2023, https://doi.org/10.1016/j.asoc.2023.110970.

[37] Q. Wu, D. Xu, B. Chen and H. Wu, "Integral Fuzzy Sliding Mode Impedance Control of an Upper Extremity Rehabilitation Robot Using Time Delay Estimation," IEEE Access, vol. 7, pp. 156513-156525, 2019, https://doi.org/10.1109/ACCESS.2019.2949197.

[38] Q. Liu, D. Li, S. S. Ge, R. Ji, Z. Ouyang, and K. P. Tee, "Adaptive bias RBF neural network control for a robotic manipulator," Neurocomputing, vol. 447, pp. 213-223, 2021, https://doi.org/10.1016/j.neucom.2021.03.033.

[39] J. Zhai and G. Xu, "A Novel Non-Singular Terminal Sliding Mode Trajectory Tracking Control for Robotic Manipulators," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 1, pp. 391-395, 2021, https://doi.org/10.1109/TCSII.2020.2999937.

[40] J. Kumar, V. Kumar, and K. P. S. Rana, "Fractional-order self-tuned fuzzy PID controller for three-link robotic manipulator system," Neural Computing and Applications, vol. 32, no. 11, pp. 7235-7257, 2020, https://doi.org/10.1007/s00521-019-04215-8.

[41] H. I. Abdulameer and M. J. Mohamed, "Fractional Order Fuzzy PID Controller Design for 2-Link Rigid Robot Manipulator," International Journal of Intelligent Engineering & Systems, vol. 15, no. 3, 2022, https://www.researchgate.net/profile/Mohamed-Mohamed-228.

[42] A. A. Shahid, L. Roveda, D. Piga and F. Braghin, "Learning Continuous Control Actions for Robotic Grasping with Reinforcement Learning," 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4066-4072, 2020, https://doi.org/10.1109/SMC42975.2020.9282951.

[43] R. Li, T. Han, and X. Song, “Stock price index forecasting using a multiscale modelling strategy based on frequency components analysis and intelligent optimization,” Applied Soft Computing, vol. 124, p. 109089, 2022, https://doi.org/10.1016/j.asoc.2022.109089.

[44] K. Ali, S. Ullah, A. Mehmood, H. Mostafa, M. Marey and J. Iqbal, "Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation," IEEE Access, vol. 10, pp. 3378-3389, 2022, https://doi.org/10.1109/ACCESS.2021.3139041.

[45] Y. Sun, X. Liang, and Y. Wan, “Tracking control of robot manipulator with friction compensation using time-delay control and an adaptive fuzzy logic system,” Actuators, vol. 12, no. 5, p. 184, 2023, https://doi.org/10.3390/act12050184.

[46] L. Wu, H. Huang, M. Wang, K. A. Alattas, A. Mohammadzadeh and E. Ghaderpour, "Optimal Control of Non-Holonomic Robotic Systems Based on Type-3 Fuzzy Model," IEEE Access, vol. 11, pp. 124430-124440, 2023, https://doi.org/10.1109/ACCESS.2023.3330244.

[47] M. I. Azeez and K. R. Atia, "Modeling of PID controlled 3DOF robotic manipulator using Lyapunov function for enhancing trajectory tracking and robustness exploiting Golden Jackal algorithm," ISA transactions, vol. 145, pp. 190-204, 2024, https://doi.org/10.1016/j.isatra.2023.11.033.

[48] J. Choi, J. Baek, W. Lee, Y. S. Lee and S. Han, "Adaptive Model-Free Control With Nonsingular Terminal Sliding-Mode for Application to Robot Manipulators," IEEE Access, vol. 8, pp. 169897-169907, 2020, https://doi.org/10.1109/ACCESS.2020.3022523.

[49] N. T. M. Nguyet and D. X. Ba, “A neural flexible PID controller for task-space control of robotic manipulators,” Frontiers in Robotics and AI, vol. 9, p. 975850, 2023, https://doi.org/10.3389/frobt.2022.975850.

[50] Z. Li, Y. Zhou, M. Zhu, and Q. Wu, "Adaptive Fuzzy Integral Sliding Mode Cooperative Control Based on Time-Delay Estimation for Free-Floating Close-Chain Manipulators," Sensors, vol. 24, no. 12, p. 3718, 2024, https://doi.org/10.3390/s24123718.

[51] D. He, H. Wang, Y. Tian, and X. Ma, “Model-free finite-time robust control using fractional-order ultra-local model and prescribed performance sliding surface for upper-limb rehabilitation exoskeleton,” ISA Transactions, vol. 147, pp. 511-526, 2024, https://doi.org/10.1016/j.isatra.2024.02.002.

[52] P. Yue, B. Xu, and M. Zhang, “An improved nonlinear robust control approach for robotic manipulators with PSO-based global optimization strategy,” Scientific Reports, vol. 14, no. 1, p. 21447, 2024, https://doi.org/10.1038/s41598-024-72156-x.

[53] A. T. Vo and H. -J. Kang, "A Novel Fault-Tolerant Control Method for Robot Manipulators Based on Non-Singular Fast Terminal Sliding Mode Control and Disturbance Observer," IEEE Access, vol. 8, pp. 109388-109400, 2020, https://doi.org/10.1109/ACCESS.2020.3001391.

[54] T. Li, G. Zhang, T. Zhang, and J. Pan, “Adaptive neural network tracking control of robotic manipulators based on disturbance observer,” Processes, vol. 12, no. 3, p. 499, 2024, https://doi.org/10.3390/pr12030499.

[55] J. F. Villa-Tiburcio, J. A. Estrada-Torres, R. Hernández-Alvarado, J. R. Montes-Martínez, D. Bringas-Posadas, and E. A. Franco-Urquiza, “ANN enhanced hybrid force/position controller of robot manipulators for fiber placement,” Robotics, vol. 13, no. 7, p. 105, 2024, https://doi.org/10.3390/robotics13070105.

[56] M. J. Mohamed, B. K. Oleiwi, A. T. Azar, and A. R. Mahlous, “Hybrid controller with neural network PID/FOPID operations for two-link rigid robot manipulator based on the zebra optimization algorithm,” Frontiers in Robotics and AI, vol. 11, p. 1386968, 2024, https://doi.org/10.3389/frobt.2024.1386968.

[57] P. Nowak and Z. Pandilov, “Energy-efficient and fault-tolerant control of a six-axis robot based on AI models,” Energies, vol. 18, no. 1, p. 20, 2025, https://doi.org/10.3390/en18010020.

[58] C. Liu, J. Gao, Y. Bi, X. Shi, and D. Tian, "A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin Synchro-Control," Sensors, vol. 20, no. 12, p. 3515, 2020, https://doi.org/10.3390/s20123515.

[59] P. Yang, S. Zhang, X. Yu and W. He, "Reinforcement-Learning-Based Finite Time Fault Tolerant Control for a Manipulator With Actuator Faults," IEEE Transactions on Cybernetics, vol. 55, no. 6, pp. 2621-2632, 2025, https://doi.org/10.1109/TCYB.2025.3557681.

[60] B. Zhao, Y. Wu, C. Wu, and R. Sun, “Deep reinforcement learning trajectory planning for robotic manipulator based on simulation-efficient training,” Scientific Reports, vol. 15, no. 1, p. 93175, 2025, https://doi.org/10.1038/s41598-025-93175-2.

[61] Y. Zhang and L. Zhao, "Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and dynamic surface control method," PloS one, vol. 20, no. 2, p. e0318601, 2025, https://doi.org/10.1371/journal.pone.0318601.

[62] Y. Han et al., "Deep Reinforcement Learning for Robot Collision Avoidance With Self-State-Attention and Sensor Fusion," IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6886-6893, 2022, https://doi.org/10.1109/LRA.2022.3178791.

[63] N. Tran Minh Nguyet and D. X. Ba, "A New Task-Space Neural Nonlinear Control Approach for Robotic Manipulators Under Joint Constraints," IEEE Access, vol. 13, pp. 91945-91956, 2025, https://doi.org/10.1109/ACCESS.2025.3571933.

[64] M. I. Azeez, A. M. M. Abdelhaleem, S. Elnaggar, K. A. Moustafa, and K. R. Atia, "Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output," Scientific reports, vol. 13, no. 1, p. 12518, 2023, https://doi.org/10.1038/s41598-023-38855-7.

[65] L. Zheng et al., "Analytical Inverse Kinematics for a Prismatic-Revolute Hybrid Joints Radiography Robot Mounted on the Ambulance," IEEE/ASME Transactions on Mechatronics, vol. 30, no. 2, pp. 1366-1376, 2025, https://doi.org/10.1109/TMECH.2024.3442782.

[66] D. D. Zheng, X. Li, X. Ren, and J. Na, “Intelligent control for robotic manipulator with adaptive learning rate and variable prescribed performance boundaries,” Journal of the Franklin Institute, vol. 360, no. 11, pp. 7037-7062, 2023, https://doi.org/10.1016/j.jfranklin.2023.05.002.

[67] M. J. Mohamed, B. K. Oleiwi, L. H. Abood, A. T. Azar, and I. A. Hameed, "Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator," Fractal and Fractional, vol. 7, no. 9, p. 693, 2023, https://doi.org/10.3390/fractalfract7090693.

[68] A. Elmogy, N. Alhemaly, H. El-Ghaish, and W. Elawady, “An enhanced neuro-adaptive PID sliding mode control for robot manipulators: Promoting sustainable automation,” Neural Computing and Applications, vol. 37, pp. 6877-6898, 2025, https://doi.org/10.1007/s00521-025-10980-6.

[69] Q. Zhou, J. Wu, B. Li, S. Li, B. Feng, J. Liu, and Y. Bi, "Adaptive Robot Motion Planning for Smart Manufacturing Based on Digital Twin and Bayesian Optimization-Enhanced Reinforcement Learning," Journal of Manufacturing Science and Engineering, vol. 147, no. 5, p. 051009, 2025, https://doi.org/10.1115/1.4067616.

[70] S. Kim and J. Choi, "Explaining the Decisions of Deep Policy Networks for Robotic Manipulations," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2663-2669, 2021, https://doi.org/10.1109/IROS51168.2021.9636594.

[71] K. Merckaert, B. Convens, M. M. Nicotra, and B. Vanderborght, “Real-time constraint-based planning and control of robotic manipulators for safe human–robot collaboration,” Robotics and Computer-Integrated Manufacturing, vol. 87, p. 102711, 2024, https://doi.org/10.1016/j.rcim.2023.102711.

[72] N. M. A. Ghani, A. Othman, A. A. A. Hashim, and A. N. K. Nasir, “Comparative analysis of PID and fuzzy logic controllers for position control in double-link robotic manipulators,” Journal of Intelligent Systems and Control, vol. 2, no. 4, pp. 183-196, 2023, https://doi.org/10.56578/jisc020401.

[73] A. S. Polydoros and L. Nalpantidis, “Survey of model-based reinforcement learning: Applications on robotics,” Journal of Intelligent & Robotic Systems, vol. 86, no. 2, pp. 153-173, 2017, https://doi.org/10.1007/s10846-017-0468-y.

[74] H. Su, A. Danioni, R. M. Mira, M. Ungari, X. Zhou, J. Li, Y. Hu, G. Ferrigno, and E. De Momi, “Experimental validation of manipulability optimization control of a 7-DoF serial manipulator for robot-assisted surgery,” The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 17, no. 1, pp. 1-17, 2021, https://doi.org/10.1002/rcs.2193.

[75] C. Van Pham and Y. N. Wang, "Robust adaptive trajectory tracking sliding mode control based on neural networks for cleaning and detecting robot manipulators," Journal of Intelligent & Robotic Systems, vol. 79, no. 1, pp. 101-114, 2015, https://doi.org/10.1007/s10846-014-0162-2.

[76] M. B. Aremu, K. S. Bin Gaufan, N. M. Alyazidi and S. El-Ferik, "A Control Perspective on Position Tracking of a Two-Link Robotic Manipulator: Addressing Perturbations with FOPID and SMC," 2025 IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD), pp. 958-963, 2025, https://doi.org/10.1109/SSD64182.2025.10990020.

[77] A. Eltayeb, G. Ahmed, I. H. Imran, N. M. Alyazidi, and A. Abubaker, "Comparative Analysis: Fractional PID vs. PID Controllers for Robotic Arm Using Genetic Algorithm Optimization," Automation, vol. 5, no. 3, pp. 230-245, 2024, https://doi.org/10.3390/automation5030014.

[78] H. -C. Huang and C. -C. Chuang, "Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time Machine Learning Control of Articulated Robotic Manipulators," IEEE Access, vol. 8, pp. 192481-192492, 2020, https://doi.org/10.1109/ACCESS.2020.3032715.

[79] J. Keighobadi, M. M. Fateh, and B. Xu, "Adaptive fuzzy voltage-based backstepping tracking control for uncertain robotic manipulators subject to partial state constraints and input delay," Nonlinear Dynamics, vol. 100, no. 3, pp. 2609-2634, 2020, https://doi.org/10.1007/s11071-020-05674-8.

[80] N. Rokbani, B. Neji, M. Slim, S. Mirjalili, and R. Ghandour, “A multi-objective modified PSO for inverse kinematics of a 5-DOF robotic arm,” Applied Sciences, vol. 12, no. 14, p. 7091, 2022, https://doi.org/10.3390/app12147091.

[81] R. Liu, F. Nageotte, P. Zanne, M. de Mathelin, and B. Dresp-Langley, "Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review," Robotics, vol. 10, no. 1, p. 22, 2021, https://doi.org/10.3390/robotics10010022.

[82] H. Nguyen and H. La, "Review of Deep Reinforcement Learning for Robot Manipulation," 2019 Third IEEE International Conference on Robotic Computing (IRC), pp. 590-595, 2019, https://doi.org/10.1109/IRC.2019.00120.

[83] Y. Tao, M. Li, X. Cao and P. Lu, "Mobile Robot Collision Avoidance Based on Deep Reinforcement Learning With Motion Constraints," IEEE Transactions on Intelligent Vehicles, vol. 10, no. 3, pp. 2163-2173, 2025, https://doi.org/10.1109/TIV.2024.3444854.

[84] Y. Zhang and H. Wang, “Redundancy-based motion planning with task constraints for robot manipulators,” Sensors, vol. 25, no. 6, p. 1900, 2025, https://doi.org/10.3390/s25061900.

[85] Y. Tong, J. Liu, H. Zhou, Z. Ju and X. Zhang, "Adaptive Tracking Control of Robotic Manipulators With Unknown Kinematics and Uncertain Dynamics," IEEE Transactions on Automation Science and Engineering, vol. 21, no. 4, pp. 5252-5269, 2024, https://doi.org/10.1109/TASE.2023.3309964.

[86] X. Lei, Z. Zhang, and P. Dong, “Dynamic path planning of unknown environment based on deep reinforcement learning,” Journal of Robotics, vol. 2018, p. 5781591, 2018, https://doi.org/10.1155/2018/5781591.

[87] Z. Anjum, H. Zhou, S. Ahmed, and Y. Guo, "Fixed time sliding mode control for disturbed robotic manipulator," Journal of Vibration and Control, vol. 30, no. 7-8, pp. 1580-1593, 2024, https://doi.org/10.1177/10775463231165094.

[88] R. H. M. Aly, K. Hussien Rahouma and A. I. Hussein, "Design and Optimization of PID Controller based on Metaheuristic algorithms for Hybrid Robots," 2023 20th Learning and Technology Conference (L&T), pp. 85-90, 2023, https://doi.org/10.1109/LT58159.2023.10092348.

[89] M. Y. Alwardat, H. Alwan, “Geometric Jacobians derivation and kinematic singularity analysis for 6-dof robotic manipulator,” International Journal of Advanced Research in Computer Science, vol. 16, no. 1, 2025, https://doi.org/10.26483/ijarcs.v16i1.7178.

[90] M. Y. Alwardat and H. M. Alwan, "Optimized Robot Control: A Comparative Study of PID, Fuzzy Logic, and Hybrid FLC-PID Techniques," Mechanical Engineering, vol. 32, no. 2, pp. 182-195, 2025, https://vestnikmai.ru/eng/publications.php?ID=185004&eng=Y&mobile=Y&PAGEN_2=3.

[91] A. Fomin, A. Antonov, V. Glazunov, and Y. Rodionov, "Inverse and Forward Kinematic Analysis of a 6-DOF Parallel Manipulator Utilizing a Circular Guide," Robotics, vol. 10, no. 1, p. 31, 2021, https://doi.org/10.3390/robotics10010031.

[92] M. Y. Alwardat, and H. M. Alwan, “Redundancy-based obstacle avoidance with virtual force fields for high-DOF robotic arm,” International Journal of Advanced Research in Computer Science, vol. 16, no. 3, pp. 15-21, 2025, https://doi.org/10.26483/ijarcs.v16i3.7221.

[93] B. Li, X. Li, H. Gao and F. -Y. Wang, "Advances in Flexible Robotic Manipulator Systems—Part I: Overview and Dynamics Modeling Methods," IEEE/ASME Transactions on Mechatronics, vol. 29, no. 2, pp. 1100-1110, 2024, https://doi.org/10.1109/TMECH.2024.3359067.

[94] A. Ollero, M. Tognon, A. Suarez, D. Lee and A. Franchi, "Past, Present, and Future of Aerial Robotic Manipulators," IEEE Transactions on Robotics, vol. 38, no. 1, pp. 626-645, 2022, https://doi.org/10.1109/TRO.2021.3084395.


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International Journal of Robotics and Control Systems
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