Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

Mohamed Shamseldin

Abstract


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

Keywords


Parallel Robot; Delta Robot; Data Acquisition Tracking Control; System Identification.

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References


Y. L. Kuo and P. Y. Huang, “Experimental and simulation studies of motion control of a Delta robot using a model-based approach,” Int. J. Adv. Robot. Syst., vol. 14, no. 6, pp. 1–14, 2017, doi: 10.1177/1729881417738738.

A. Gholami, T. Homayouni, R. Ehsani, and J. Q. Sun, “Inverse kinematic control of a delta robot using neural networks in real-time,” Robotics, vol. 10, no. 4, 2021, doi: 10.3390/robotics10040115.

T. H. Kim, Y. Kim, T. Kwak, and M. Kanno, “Metaheuristic Identification for an Analytic Dynamic Model of a Delta Robot with Experimental Verification,” Actuators, vol. 11, no. 12, pp. 1–20, 2022, doi: 10.3390/act11120352.

P. C. Pham and Y. L. Kuo, “Robust Adaptive Finite-Time Synergetic Tracking Control of Delta Robot Based on Radial Basis Function Neural Networks,” Appl. Sci., vol. 12, no. 21, 2022, doi: 10.3390/app122110861.

F. B. Setiawan, P. M. Siva, L. H. Pratomo, and S. Riyadi, “Design and implementation of smart forklift for automatic guided vehicle using raspberry pi 4,” J. Robot. Control, vol. 2, no. 6, pp. 508–514, 2021, doi: 10.18196/jrc.26130.

J. M. Guirguis, S. Hammad, and S. A. Maged, “Path Tracking Control Based on an Adaptive MPC to Changing Vehicle Dynamics,” Int. J. Mech. Eng. Robot. Res., vol. 11, no. 7, pp. 535–541, 2022, doi: 10.18178/ijmerr.11.7.535-541.

M. R. Islam1, M. R. T. Hossain2, and S. C. Banik, “Synchronizing of stabilizing platform mounted on a two-wheeled robot,” J. Robot. Control, vol. 2, no. 6, pp. 552–558, 2021, doi: 10.18196/jrc.26136.

S. R. Utama, A. Firdausi, and G. P. N. Hakim, “Control and Monitoring Automatic Floodgate Based on NodeMCU and IOT with Fuzzy Logic Testing,” J. Robot. Control, vol. 3, no. 1, pp. 14–17, 2022, doi: 10.18196/jrc.v3i1.11199.

M. Rahmani, H. Komijani, and M. H. Rahman, “New Sliding Mode Control of 2-DOF Robot Manipulator Based on Extended Grey Wolf Optimizer,” Int. J. Control. Autom. Syst., vol. 18, no. 6, pp. 1572–1580, 2020, doi: 10.1007/s12555-019-0154-x.

V. Verma, A. Gupta, M. K. Gupta, and P. Chauhan, “Performance estimation of computed torque control for surgical robot application,” J. Mech. Eng. Sci., vol. 14, no. 3, pp. 7017–7028, 2020, doi: 10.15282/jmes.14.3.2020.04.0549.

S. I. Han, “Fractional-order Sliding Mode Constraint Control for Manipulator Systems Using Grey Wolf and Whale Optimization Algorithms,” Int. J. Control. Autom. Syst., vol. 19, no. 2, pp. 676–686, 2021, doi: 10.1007/s12555-020-0138-x.

N. P. Van Bach, Q. D. Hai, and T. B. Trung, "Optimization of trajectory tracking control of 3-DOF translational robot use PSO method based on inverse dynamics control for surgery application," Journal of Vibroengineering, vol. 23, no. 7, pp. 1591-1601, 2021.

M. A. Mossa, H. Khouidmi, and A. Ma’arif, “Robust Flux and Speed State Observer Design for Sensorless Control of a Double Star Induction Motor,” J. Robot. Control, vol. 3, no. 4, pp. 464–475, 2022, doi: 10.18196/jrc.v3i4.15667.

Y. Zahraoui, M. Akherraz, C. Fahassa, and S. Elbadaoui, “Induction Motor DTC Performance Improvement by Reducing Flux and Torque Ripples in Low Speed,” J. Robot. Control, vol. 3, no. 1, pp. 93–100, 2022, doi: 10.18196/jrc.v3i1.12550.

A. Latif, A. Z. Arfianto, H. A. Widodo, R. Rahim, and E. T. Helmy, “Motor DC PID system regulator for mini conveyor drive based-on matlab,” J. Robot. Control, vol. 1, no. 6, pp. 185–190, 2020, doi: 10.18196/jrc.1636.

Y. Irawan, Muhardi, R. Ordila, and R. Diandra, “Automatic floor cleaning robot using arduino and ultrasonic sensor,” J. Robot. Control, vol. 2, no. 4, pp. 240–243, 2021, doi: 10.18196/jrc.2485.

A. W. L. Yao and H. C. Chen, “An Intelligent Color Image Recognition and Mobile Control System for Robotic Arm,” Int. J. Robot. Control Syst., vol. 2, no. 1, pp. 97–104, 2022, doi: 10.31763/ijrcs.v2i1.557.

M. Abdullah-Al-Noman, A. N. Eva, T. B. Yeahyea, and R. Khan, “Computer Vision-based Robotic Arm for Object Color, Shape, and Size Detection,” J. Robot. Control, vol. 3, no. 2, pp. 180–186, 2022, doi: 10.18196/jrc.v3i2.13906.

M. Marsono, Y. Yoto, A. Suyetno, and R. Nurmalasari, “Design and programming of 5 axis manipulator robot with grblgru open source software on preparing vocational students’ robotic skills,” J. Robot. Control, vol. 2, no. 6, pp. 539–545, 2021, doi: 10.18196/jrc.26134.

D. M. Harfina, Z. Zaini, and W. J. Wulung, “Disinfectant spraying system with quadcopter type unmanned aerial vehicle technology as an effort to break the chain of the covid-19 virus,” J. Robot. Control, vol. 2, no. 6, pp. 502–507, 2021, doi: 10.18196/jrc.26129.

A. Ma’arif and N. R. Setiawan, “Control of dc motor using integral state feedback and comparison with pid: Simulation and arduino implementation,” J. Robot. Control, vol. 2, no. 5, pp. 456–461, 2021, doi: 10.18196/jrc.25122.

I. Suwarno, Y. Finayani, R. Rahim, J. Alhamid, and A. R. Al-Obaidi, “Controllability and Observability Analysis of DC Motor System and a Design of FLC-Based Speed Control Algorithm,” J. Robot. Control, vol. 3, no. 2, pp. 227–235, 2022, doi: 10.18196/jrc.v3i2.10741.

A. Ma’arif and A. Çakan, “Simulation and arduino hardware implementation of dc motor control using sliding mode controller,” J. Robot. Control, vol. 2, no. 6, pp. 582–587, 2021, doi: 10.18196/jrc.26140.

H. Chhabra, V. Mohan, A. Rani, and V. Singh, “Robust nonlinear fractional order fuzzy PD plus fuzzy I controller applied to robotic manipulator,” Neural Comput. Appl., vol. 32, no. 7, pp. 2055–2079, 2020, doi: 10.1007/s00521-019-04074-3.

M. A. S. Mohamed A. Abdel Ghany, “Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization Mohamed,” Int. J. Power Electron. Drive Syst., vol. 14, no. 1, 2023.

J. Poovarasan, R. Kayalvizhi, and R. K. Pongiannan, “Design of Fractional Order PID controller for a CSTR process,” Int. Ref. J. Eng. Sci., vol. 3, no. 1, pp. 8–14, 2014.

M. Omar, M. A. Ebrahim, A. M. Abdel Ghany, and F. Bendary, “Reduced size harmony search algorithm for optimization,” J. Electr. Eng., vol. 16, no. 1, pp. 395–402, 2016.

H. Abdelfattah, S. A. Kotb, M. Esmail, and M. I. Mosaad, “Adaptive Neuro-Fuzzy Self-Tuned-PID Controller for,” Int. J. Robot. Control Syst., vol. 3, no. 1, pp. 1–18, 2023.

H. Maghfiroh, M. Ahmad, A. Ramelan, and F. Adriyanto, “Fuzzy-PID in BLDC Motor Speed Control Using MATLAB/Simulink,” J. Robot. Control, vol. 3, no. 1, pp. 8–13, 2022, doi: 10.18196/jrc.v3i1.10964.

A. Daraz, S. A. Malik, A. T. Azar, S. Aslam, T. Alkhalifah, and F. Alturise, “Optimized Fractional Order Integral-Tilt Derivative Controller for Frequency Regulation of Interconnected Diverse Renewable Energy Resources,” IEEE Access, vol. 10, pp. 43514–43527, 2022, doi: 10.1109/ACCESS.2022.3167811.

G. I. Y. Mustafa, X. Li, and H. Wang, “A New Neural Network-Based Adaptive Time-Delay Control for Nonlinear Car Active Suspension System,” Stud. Informatics Control, vol. 31, no. 4, pp. 13–24, 2022, doi: 10.24846/V31I4Y202202.

R. R. Ardeshiri, H. N. Kashani, and A. Reza-Ahrabi, “Design and simulation of self-Tuning fractional order fuzzy PID controller for robotic manipulator,” Int. J. Autom. Control, vol. 13, no. 5, pp. 595–618, 2019, doi: 10.1504/IJAAC.2019.101912.

F. T. Li, L. Ma, L. T. Mi, Y. X. Zeng, N. B. Jin, and Y. L. Gao, “Friction identification and compensation design for precision positioning,” Adv. Manuf., vol. 5, no. 2, pp. 120–129, 2017, doi: 10.1007/s40436-017-0171-z.

E. Yuliza, H. Habil, R. A. Salam, M. M. Munir, M. Abdullah, and Khairurrijal, “Development of a Simple Single-Axis Motion Table System for Testing Tilt Sensors,” Procedia Eng., vol. 170, pp. 378–383, 2017, doi: 10.1016/j.proeng.2017.03.061.

A. G. M. A. G. Mohamed A. Shamseldin, Abdel Halim M. Bassiuny, “Design variable structure fuzzy control based on deep neural network model for servomechanism drive system,” Int. J. Power Electron. Drive Syst., vol. 13, no. 4, 2022.

P. Zhao and Y. Shi, “Robust control of the A-axis with friction variation and parameters uncertainty in five-axis CNC machine tools,” Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., vol. 228, no. 14, pp. 2545–2556, 2014, doi: 10.1177/0954406213519759.

R. Kristiyono and Wiyono, “Autotuning fuzzy PID controller for speed control of BLDC motor,” J. Robot. Control, vol. 2, no. 5, pp. 400–407, 2021, doi: 10.18196/jrc.25114.

M. Chang and G. Guo, “Sinusoidal Servocompensator Implementations with Real-Time Requirements and Applications,” IEEE Trans. Control Syst. Technol., vol. 25, no. 2, pp. 645–652, 2017, doi: 10.1109/TCST.2016.2558476.

K. Vanchinathan and N. Selvaganesan, "Adaptive fractional order PID controller tuning for brushless DC motor using artificial bee colony algorithm," Results in Control and Optimization, vol. 4, p. 100032, 2021.

E. Cajueiro, R. Kalid, and L. Schnitman, “Using NARX model with wavelet network to inferring the polished rod position,” Int. J. Math. Comput. Simul., vol. 6, no. 1, pp. 66–73, 2012.

P. Zhao, J. Huang, and Y. Shi, “Nonlinear dynamics of the milling head drive mechanism in five-axis CNC machine tools,” Int. J. Adv. Manuf. Technol., vol. 91, no. 9–12, pp. 3195–3210, 2017, doi: 10.1007/s00170-017-9989-6.

F. Abdessemed, “SVM-based control system for a robot manipulator,” Int. J. Adv. Robot. Syst., vol. 9, no. 6, p. 247, 2012.

B. Feng, D. Zhang, J. Yang, and S. Guo, “A novel time-varying friction compensation method for servomechanism,” Math. Probl. Eng., vol. 2015, 2015, doi: 10.1155/2015/269391.

M. Saad, A. H. Amhedb, and M. Al Sharqawi, “Real time DC motor position control using PID controller in LabVIEW,” J. Robot. Control, vol. 2, no. 5, pp. 342–348, 2021, doi: 10.18196/jrc.25104.

D. Handaya and R. Fauziah, “Proportional-integral-derivative and linear quadratic regulator control of direct current motor position using multi-turn based on LabView,” J. Robot. Control, vol. 2, no. 4, pp. 332–336, 2021, doi: 10.18196/jrc.24102.

F. A. Azad, S. Rahimi, M. R. Hairi Yazdi, and M. T. Masouleh, "Design and Evaluation of Adaptive and Sliding Mode Control for a 3-DOF Delta Parallel Robot," 2020 28th Iranian Conference on Electrical Engineering (ICEE), pp. 1-7, 2020, doi: 10.1109/ICEE50131.2020.9261040.

H. L. Tran and T. V. Dang, "An Ultra Fast Semantic Segmentation Model for AMR’s Path Planning," Journal of Robotics and Control (JRC), vol. 4, no. 3, pp. 424-430, doi: 10.18196/jrc.v4i3.18758.

A. A. Abed, A. Al-Ibadi, and I. A. Abed, “Vision-Based Soft Mobile Robot Inspired by Silkworm Body and Movement Behavior,” J. Robot. Control, vol. 4, no. 3, pp. 299–307, 2023, doi: 10.18196/jrc.v4i3.16622.

H. A. Mintsa, G. E. Eny, N. Senouveau, J. P. Kenné, and R. M. A. Nzué, “An Alternative Nonlinear Lyapunov Redesign Velocity Controller for an Electrohydraulic Drive,” J. Robot. Control, vol. 4, no. 2, pp. 192–201, 2023, doi: 10.18196/jrc.v4i2.17340.

F. Z. Baghli, Y. Lakhal, and Y. A. El Kadi, “The Efficiency of an Optimized PID Controller Based on Ant Colony Algorithm (ACO-PID) for the Position Control of a Multi-articulated System,” J. Robot. Control, vol. 4, no. 3, pp. 289–298, 2023, doi: 10.18196/jrc.v4i3.17709.

P. Chotikunnan and Y. Pititheeraphab, “Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application,” J. Robot. Control, vol. 4, no. 2, pp. 217–226, 2023, doi: 10.18196/jrc.v4i2.17757.

F. Umam, M. Fuad, I. Suwarno, A. Ma’arif, and W. Caesarendra, “Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot,” J. Robot. Control, vol. 4, no. 2, pp. 227–242, 2023, doi: 10.18196/jrc.v4i2.17977.

J. Díaz-Téllez, R. S. García-Ramírez, J. Pérez-Pérez, J. Estevez-Carreón, and M. A. Carreón-Rosales, "ROS-based Controller for a Two-Wheeled Self-Balancing Robot," Journal of Robotics and Control (JRC), vol. 4, no. 4, pp. 491-499, 2023.

M. H. Widianto and B. Juarto, “Smart Farming Using Robots in IoT to Increase Agriculture Yields: A Systematic Literature Review,” J. Robot. Control, vol. 4, no. 3, pp. 330–341, 2023, doi: 10.18196/jrc.v4i3.18368.

I. Zaway, R. Jallouli-khlif, B. Maalej, and N. Derbel, “A Robust Fuzzy Fractional Order PID Design Based On Multi-Objective Optimization For Rehabilitation Device Control,” J. Robot. Control, vol. 4, no. 3, pp. 388–402, 2023, doi: 10.18196/jrc.v4i3.18411.

T. A. Assegie, T. Suresh, R. Purushothaman, S. Ganesan, and N. K. Kumar, “Early Prediction of Gestational Diabetes with Parameter-Tuned K-Nearest Neighbor Classifier,” J. Robot. Control, vol. 4, no. 4, pp. 452–457, 2023, doi: 10.18196/jrc.v4i4.18412.

T. Q. Ngo, T. T. H. Le, B. M. Lam, and T. K. Pham, “Adaptive Single-Input Recurrent WCMAC-Based Supervisory Control for De-icing Robot Manipulator,” J. Robot. Control, vol. 4, no. 4, pp. 438–451, 2023, doi: 10.18196/jrc.v4i4.18464.

A. Ubaidillah, H. Sukri, U. T. Madura, J. Timur, and C. Author, “Application of Odometry and Dijkstra Algorithm as Navigation and Shortest Path Determination System of Warehouse Mobile Robot,” J. Robot. Control, vol. 4, no. 3, 2023, doi: 10.18196/jrc.v4i3.18489.

M. A. Abdelghany, A. O. Elnady, and S. O. Ibrahim, “Optimum PID Controller with Fuzzy Self-Tuning for DC Servo Motor,” J. Robot. Control, vol. 4, no. 4, 2023, doi: 10.18196/jrc.v4i4.18676.

P. Raksincharoensak, M. Nagai, and M. Shino, "Lane keeping control strategy with direct yaw moment control input by considering dynamics of electric vehicle," Vehicle System Dynamics, vol. 44, pp. 192-201, 2006, doi: 10.1080/00423110600870006.




DOI: https://doi.org/10.18196/jrc.v4i5.18865

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