Enhancing Disturbance Rejection Capability and Body Jerk Performance of a Twin-rotor Helicopter Model Using Intelligent Active Force Control
Keywords:
Twin-rotor system, 2-DOF helicopter, active force control, intelligent systems, self-tuning, iterative learning, fuzzy logic, body jerk performanceAbstract
This paper presents a study on the effectiveness of utilizing an innovative control approach based on an intelligent active force control (IAFC) strategy to stabilize a twin-rotor helicopter model and improve its ability to effectively reject external disturbances via a simulation work. A detailed mathematical model of a two-degree-of-freedom (DOF) helicopter was derived using the Euler-Lagrange method taking into account the effects of coupling and disturbances. In this developed model, a Proportional–Integral–Derivative (PID) controller was designed and combined with the proposed IAFC strategy to yield an intelligent hybrid control architecture known as a PID-IAFC scheme that can improve system performance and reject various types of applied disturbances. The intelligent algorithms used in the schemes are based on iterative learning (IL) and fuzzy logic (FL). In this work, different types of external disturbances in the form of sinusoidal waves, pulsating, and random noise disturbances were applied to the helicopter system to verify the sensitivity and durability of the proposed control schemes and consequently, a comparative study was performed to analyze the system characteristics. Notably, the efficacy of the IAFC based control unit was investigated to improve the body jerk performance in the presence of external disturbances. The acquired results reveal the effectiveness and robustness of the IAFC based controller in stabilizing the dual-rotor helicopter, rejecting the applied disturbances, and improving the body jerk performance by at least 54% for pitching and 19% for yawing motions in the presence of the pulsating disturbance, and 60% and 54%, respectively, for the random noise disturbance.References
Abdelkader, K., Kais, B., 2019. Robust H∞ Gain Neuro-Adaptive Observer Design For Nonlinear Uncertain Systems. Trans. Inst. Meas. Control 41, 2293–2309. https://doi.org/10.1177/0142331218798685
Abdelmaksoud, S.I., Mailah, M., Abdallah, A.M., 2021. Practical Real-Time Implementation of A Disturbance. Rejection Control Scheme for A Twin-Rotor Helicopter System Using Intelligent Active Force Control. IEEE. Access 9, 4886–4901. https://doi.org/10.1109/ACCESS.2020.3046728
Abdelmaksoud, S. I., Mailah, M., Abdallah, A.M., 2020. Control Strategies and Novel Techniques for Autonomous Rotorcraft Unmanned Aerial Vehicles: A Review. IEEE Access 8, 195142–195169. https://doi.org/10.1109/ACCESS.2020.3031326
Abdelmaksoud, Sherif I., Mailah, M., Abdallah, A.M., 2020. Robust Intelligent Self-Tuning Active Force Control of A Quadrotor With Improved Body Jerk Performance. IEEE Access 8, 150037–150050. https://doi.org/10.1109/ACCESS.2020.3015101
Almtireen, N., Elmoaqet, H., Ryalat, M., 2018. Linearized Modelling and Control for A Twin Rotor System. Autom. Control Comput. Sci. 52, 539–551. https://doi.org/10.3103/S0146411618060020
Arimoto, S., Kawamura, S., Miyazaki, F., 1986. Convergence, Stability and Robustness of Learning Control Schemes for Robot Manipulators, in: Proceedings of the International Symposium on Robot Manipulators on Recent Trends in Robotics: Modeling, Control and Education. Elsevier North-Holland, Inc., New York, NY, USA, pp. 307–316.
Burdess, J.S., Hewit, J.R., 1986. An Active Method for The Control of Mechanical Systems in The Presence of Unmeasurable Forcing. Mech. Mach. Theory 21, 393–400. https://doi.org/10.1016/0094-114X(86)90087-X
Chi, N.V., 2017. Adaptive Feedback Linearization Control for Twin Rotor Multiple-Input Multiple-Output System. Int. J. Control Autom. Syst. 15, 1267–1274. https://doi.org/10.1007/s12555-015-0245-2
Choudhary, S.K., 2016. Optimal Feedback Control of Twin Rotor MIMO System with A Prescribed Degree of Stability. Int. J. Intell. Unmanned Syst. 4, 226–238. https://doi.org/10.1108/IJIUS-07-2016-0005
Eager, D., Pendrill, A.-M., Reistad, N., 2016. Beyond Velocity and Acceleration: Jerk, Snap and Higher Derivatives. Eur. J. Phys. 37, 11 pages. https://doi.org/10.1088/0143-0807/37/6/065008
Faris, F., Moussaoui, A., Djamel, B., Mohammed, T., 2017. Design and Real-Time Implementation of A Decentralized Sliding Mode Controller For Twin Rotor Multi-Input Multi-Output System. Proc. Inst. Mech. Eng. Part J. Syst. Control Eng. 231, 3–13. https://doi.org/10.1177/0959651816680457
Harshath, K., Manoharan, P.S., Varatharajan, M., 2016. Model Predictive Control of TRMS, in: 2016 Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE). Presented at the 2016 Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), pp. 1–5. https://doi.org/10.1109/PESTSE.2016.7516455
Hashim, H.A., Abido, M.A., 2015. Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study. Comput. Intell. Neurosci. https://doi.org/10.1155/2015/704301
Hewit, J.R., Burdess, J.S., 1981. Fast Dynamic Decoupled Control for Robotics, Using Active Force Control. Mech. Mach. Theory 16, 535–542. https://doi.org/10.1016/0094-114X(81)90025-2
Ijaz, S., Hamayun, M.T., Yan, L., Mumtaz, M.F., 2016. Fractional Order Modeling and Control of Twin Rotor Aero Dynamical System Using Nelder Mead Optimization. J. Electr. Eng. Technol. 11, 1863–1871.
Ilyas, M., Abbas, N., UbaidUllah, M., Imtiaz, W.A., Shah, M. a. Q., Mahmood, K., 2016. Control Law Design for Twin Rotor MIMO System with Nonlinear Control Strategy. Discrete Dyn. Nat. Soc. 2016, 10 pages. https://doi.org/10.1155/2016/2952738
Lin, C.-W., Li, T.-H.S., Chen, C.-C., 2018. Feedback Linearization and Feedforward Neural Network Control with Application to Twin Rotor Mechanism. Trans. Inst. Meas. Control 40, 351–362. https://doi.org/10.1177/0142331216656758
Mailah, M., 1998. Intelligent Active Force Control of A Rigid Robot Arm Using Neural Network and Iterative Learning Algorithms. University of Dundee.
Maiti, R., Sharma, K.D., Sarkar, G., 2018. PSO Based Parameter Estimation and PID Controller Tuning for 2-DOF Nonlinear Twin Rotor MIMO System. Int. J. Autom. Control 12, 582–609. https://doi.org/10.1504/IJAAC.2018.095109
Mascaró Palliser, R., Costa-Castelló, R., Ramos, G.A., 2017. Iterative Learning Control Experimental Results in Twin-Rotor Device. Math. Probl. Eng. 2017, 12 pages. https://doi.org/10.1155/2017/6519497
Meon, M.S., Mohamed, T.L.T., Ramli, M.H.M., Mohamed, M.Z., Manan, N.F.A., 2012. Review and Current Study on New Approach Using PID Active Force Control (PIDAFC) of Twin Rotor Multi Input Multi Output System (TRMS), in: 2012 IEEE Symposium on Humanities, Science and Engineering Research. Presented at the 2012 IEEE Symposium on Humanities, Science and Engineering Research, pp. 163–167. https://doi.org/10.1109/SHUSER.2012.6268848
Omar, M., Mailah, M., Abdelmaksoud, S.I., 2017. Robust Active Force Control of A Quadcopter. J. Mek. 40, 12–22.
Pandey, S.K., Dey, J., Banerjee, S., 2018. Design of Robust Proportional–Integral–Derivative Controller for Generalized Decoupled Twin Rotor Multi-Input-Multi-Output System with Actuator Non-Linearity. Proc. Inst. Mech. Eng. Part J. Syst. Control Eng. 232, 971–982. https://doi.org/10.1177/0959651818771487
Precup, R.-E., Radac, M.-B., Roman, R.-C., Petriu, E.M., 2017. Model-Free Sliding Mode Control of Nonlinear Systems: Algorithms and Experiments. Inf. Sci. 381, 176–192. https://doi.org/10.1016/j.ins.2016.11.026
Raghavan, R., Thomas, S., 2017. Practically Implementable Model Predictive Controller for A Twin Rotor Multi-Input Multi-Output System. J. Control Autom. Electr. Syst. 28, 358–370. https://doi.org/10.1007/s40313-017-0311-5
Rakhtala, S.M., Ahmadi, M., 2017. Twisting Control Algorithm for The Yaw and Pitch Tracking of A Twin Rotor UAV. Int. J. Autom. Control 11, 143–163. https://doi.org/10.1504/IJAAC.2017.083296
Ramli, H., Kuntjoro, W., Meon, M.S., Ishak, K.M.A.K., 2013. Adaptive Active Force Control Application to Twin Rotor Mimo System. Appl. Mech. Mater. 393, 688–693. https://doi.org/10.4028/www.scientific.net/AMM.393.688
Rashad, R., Aboudonia, A., El-Badawy, A., 2016. A Novel Disturbance Observer-Based Backstepping Controller with Command Filtered Compensation for A MIMO System. J. Frankl. Inst. 16, 4039–4061. https://doi.org/10.1016/j.jfranklin.2016.07.017
Rashad, R., El-Badawy, A., Aboudonia, A., 2017. Sliding Mode Disturbance Observer-Based Control of A Twin Rotor MIMO System. ISA Trans. 69, 166–174. https://doi.org/10.1016/j.isatra.2017.04.013
Sabzehmeidani, Y., Mailah, M., Hing, T.H., Abdelmaksoud, S.I., 2021. A Novel Voice-Coil Actuated Mini Crawler for In-Pipe Application Employing Active Force Control with Iterative Learning Algorithm. IEEE Access 9, 28156–28166. https://doi.org/10.1109/ACCESS.2021.3058312
Tahmasebi, M., Mailah, M., Gohari, M., Abd Rahman, R., 2017. Vibration Suppression of Sprayer Boom Structure Using Active Torque Control and Iterative Learning. Part I: Modelling and Control Via Simulation. J. Vib. Control 24, 4689–4699. https://doi.org/10.1177/1077546317733164
Van, C.N., 2016. Designing the Adaptive Tracking Controller for Uncertain Fully Actuated Dynamical Systems with Additive Disturbances Based on Sliding Mode. J. Control Sci. Eng. 2016, 11 pages. https://doi.org/10.1155/2016/9810251
Downloads
Published
How to Cite
Issue
Section
License
Copyright of articles that appear in Jurnal Mekanikal belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.
