Precise luffing of payload in mobile cranes is a crucial part of material handling and safety in industrial operations. Explosive ordnance disposal uses a bomb disposal robot to safely disable explosive devices at a safe distance. This robot is a mobile jib crane type with a gripper as an end effector instead of the typical suspension cable with a hook. The common challenge of this crane type is the arm/jib movement sensitivity with respect to tip-over stability of the crane body. This is directly influenced by the payload and constrained by the degree of freedom. This paper presents a control strategy of the joint for luffing such that the back wheels remain in contact with the surface ground and avoid bucking at any given instance of weight change in the payload. Fuzzy logic was applied to control the motor torque and luffing angle of the arm in response to the load and gripper opening size to maintain the tip-over stability margin at the highest value possible. The response curves at different configurations of the two input signals were also analyzed based on the rules set to determine its precision with respect to the expected response curve.

1. [1] Home Land Security, “IED Attack Improvised Explosive Devise Fact Sheet,” 2022.
2. [2] A. H. Fernando, L. A. G. Lim, A. A. Bandala, R. R. Vicerra, and E. D. Dadios, “Design of a Fuzzy Control Crane Type Robot Arm for EOD Application,” 2021 IEEE 13th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines, 2021. https://doi.org/10.1109/HNICEM54116.2021.9732026
3. [3] P. Hyla, “The crane control system: A survey,” 2012 17th Int. Conf. on Methods and Models in Automation and Robotics (MMAR), 2012. https://doi.org/10.1109/MMAR.2012.6347867
4. [4] R. A. R. Bedruz, J. Martin, Z. Maningo, A. H. Fernando, A. A. Bandala, R. R. P. Vicerra, and E. P. Dadios, “Dynamic Peloton Formation Configuration Algorithm of Swarm Robots for Aerodynamic Effects Optimization,” 2019 7th Int. Conf. on Robot Intelligence Technology and Applications (RiTA), Daejeon, Korea, pp. 264-267, 2019. https://doi.org/10.1109/RITAPP.2019.8932871
5. [5] A. Aksjonov, V. Vodovozov, and E. Petlenkov, “Self-scaling laboratory crane fuzzy logic control with anti-swing regulation,” 2016 57th Int. Scientific Conf. on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga, Latvia, 2016. https://doi.org/10.1109/RTUCON.2016.7763088
6. [6] M. Guillermo et al., “Graph Query Language (GQL)-Structured Algorithms for Geospatial Intelligence on Public Transportation,” 2022 13th Int. Conf. on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, pp. 439-444, 2022. https://doi.org/10.1109/ICTC55196.2022.9952570
7. [7] R. Rahmani, H. Karimi, R. Yusof, and M. F. Othman, “A precise fuzzy controller developed for overhead crane,” 2015 10th Asian Control Conf. (ASCC), Kota Kinabalu, Malaysia, 2015. https://doi.org/10.1109/ASCC.2015.7244551
8. [8] C. Chen and J. Luo, “Hybrid optimal control of robot motor based on improved genetic algorithm and fuzzy logic,” 2017 Int. Conf. on Robotics and Automation Sciences (ICRAS), Hong Kong, China, pp. 53-57, 2017. https://doi.org/10.1109/ICRAS.2017.8071916
9. [9] Z. Nagy, E. Páll, and Z. Lendek, “Unknown input observer for a robot arm using TS fuzzy descriptor models,” 2017 IEEE Conf. on Control Technology and Applications (CCTA), Maui, HI, USA, pp. 939-944, 2017. https://doi.org/10.1109/CCTA.2017.8062580
10. [10] J. C. Chan, H. L. T. Dy, A. H. Fernando, R. L. Tiu, and P. J. G. Viernes, “Design, Fabrication, and Testing of a PLC Training Module Using Siemens S7-300 PLC,” DLSU Engineering e-Journal, Vol.1, No.1, pp. 43-54, 2007.
11. [11] A. H. Fernando, I. A. V. Marfori, and A. B. Maglaya, “A comparative study between artificial neural network and linear regression for optimizing a hinged blade cross axis turbine,” 2015 Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Cebu, Philippines, 2015. https://doi.org/10.1109/HNICEM.2015.7393225
12. [12] S. Lauguico et al., “Machine Vision-Based Prediction of Lettuce Phytomorphological Descriptors using Deep Learning Networks,” 2020 IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines, 2020. https://doi.org/10.1109/HNICEM51456.2020.9400103
13. [13] V. Meshcheryakov and I. Denisov, “Automation of the jib crane operation using adaptive neuro-fuzzy interference system,” Mechanisms and Machines (Dynamics), 2016.
14. [14] G. Madhumitha, R. Srividhya, J. Johnson, and D. Annamalai, “Physical modeling and control of self-balancing platform on a cart,” 2016 Int. Conf. on Robotics: Current Trends and Future Challenges (RCTFC), Thanjavur, India, 2016. https://doi.org/10.1109/RCTFC.2016.7893410
15. [15] P. S. Equipment, “CALIBER® T5,” pp. 5-6, 2021.
16. [16] D. Fujioka, “Tip-over stability analysis for mobile boom cranes with single and double-pendulum payload,” Georgia Institute of Technology, 2010.
17. [17] M. A. Zawawi, J. Bidin, M. Z. M. Tumari, and M. S. Saealal, “Investigation of Classical and Fuzzy Controller Robustness for Gantry Crane System Incorporating Payload,” 2011 3rd Int. Conf. on Computational Intelligence, Modelling & Simulation, Langkawi, Malaysia, pp. 141-146, 2011. https://doi.org/10.1109/CIMSim.2011.33
18. [18] N. P. Nguyen, T. N. Phan, and Q. H. Ngo, “Autonomous Offshore Container Crane System Using a Fuzzy-PD Logic Controller,” 2016 16th Int. Conf. on Control, Automation and Systems (ICCAS), Gyeongju, Korea, pp. 1093-1098, 2016. https://doi.org/10.1109/ICCAS.2016.7832447
19. [19] E. A. H. Fernando et al., “Design of a fuzzy logic controller for a vent fan and growlight in a tomato growth chamber,” 2017 IEEE 9th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Manila, Philippines, 2017. https://doi.org/10.1109/HNICEM.2017.8269526
20. [20] D. D. Ligutan, L. J. S. Cruz, M. C. D. P. Del Rosario, J. N. S. Kudhal, A. C. Abad, and E. P. Dadios, “Design and implementation of a fuzzy logic-based joint controller on a 6-DOF robot arm with machine vision feedback,” 2017 Computing Conf., London, UK, pp. 249-257, 2017. https://doi.org/10.1109/SAI.2017.8252111
21. [21] T Hoang and N. Tran-Thi, “Robust Anti—Swing Control of 3D Crane System using GA—Fuzzy,” 2017 Int. Conf. on Cyber Incident Response, Coordination, Containment & Control, 2017. https://doi.org/10.1109/CYBERINCIDENT.2017.8054638
22. [22] V. Meshcheryakov and I. Denisov, “Automation of the jib crane operations using adaptive neuro-fuzzy inference system,” 2016 Dynamics of Systems, Mechanisms and Machines (Dynamics), Omsk, Russia, 2016. https://doi.org/10.1109/Dynamics.2016.7819048
23. [23] B. Lu and J. Pu, “Sliding mode control based on fuzzy switching gain for the robot arm,” 2017 Chinese Automation Congress (CAC), 2017. https://doi.org/10.1109/CAC.2017.8243178
24. [24] S. Hernandez-Mendez, A. Marin-Hernandez, E. R. Palacios-Hernandez, and K. L. Luna-Gallegos, “A switching position/force controller for two independent finger gripper over ROS,” 2017 Int. Conf. on Electronics, Communications and Computers (CONIELECOMP), Cholula, Mexico, 2017. https://doi.org/10.1109/CONIELECOMP.2017.7891813
25. [25] S. Hernandez-Mendez, C. Maldonado-Mendez, A. Marin-Hernandez, H. V. Rios-Figueroa, H. Vazquez-Leal, and E. R. Palacios-Hernandez, “Design and implementation of a robotic arm using ROS and MoveIt!,” 2017 IEEE Int. Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico, 2017. https://doi.org/10.1109/ROPEC.2017.8261666
26. [26] T. C. Lin, J. L. Yeh, T. C. Hung, and C. H. Kuo, “Direct adaptive fuzzy sliding mode PI tracking control of a three-dimensional overhead crane,” 2015 IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), Istanbul, Turkey, 2015. https://doi.org/10.1109/FUZZ-IEEE.2015.7337856