An Online Trajectory Tracking Control of a Double Flexible Joint Manipulator Robot by Considering the Parametric and Non-Parametric Uncertainty
Subject Areas : optimization and simulation
Alireza
Pezhman
1
(Department of Mechanical Engineering,
Lahijan Branch,Islamic Azad University, Lahijan, Iran)
Javad
Rezapour
2
(Department of Mechanical Engineering,
Lahijan Branch,Islamic Azad University, Lahijan, Iran)
Mohammadjavad
Mahmoodabadi
3
(Department of Mechanical Engineering,
Sirjan University of Technology, Sirjan, Iran)
Keywords: Double Flexible Joint Manipulator, Gradient Descent Method, sliding mode controller, Uncertainy,
Abstract :
Accurate trajectory tracking and control of the Double Flexible Joint Manipulator lead to design a controller with complex features. In this paper, we study two significant strategies based on improving the structure of the hybrid controller and training the controller parameters for an online estimation of time-varying parametric uncertainities. For this purpose, combination of feedback linearization with an adaptive sliding mode control by considering update mechanism is utilized to stabilize the DFJM system. The update mechanism is obtained based on gradient descend method and chain rule of the derivation. Following, in order to eliminate the tedious trial-and-error process of determining the control coefficients, an evolutionary algorithm (NSGA-II) is used to extract the optimal parameters by minimizing the tracking error and control input. In the second step, an online estimation of the designed parameters were proposed based on three intelligent methods; weighting function, Adaptive Neural Network Function Fitting (ANNF), and adaptive Neuro-fuzzy inference system (ANFIS-PSO). The proposed controller reliability finally was examined in condition of the mass and the length of the robot arm was changed and sudden disturbances were imposed at the moment of equilibrium position, simultanously. The results of the tracking error and control input of the trained proposed controller demonstrated minimal energy consumption and shorter stability time in condition that the control parameters are constant and training are not considered.
[1] Eva, G., Dmitri, S., Tracking Controller for Flexible Joint Robots, Journal of Electrical Engineering, Vol. 53, No. 7-8, 2002, pp. 184-190.
[2] Kolhe, J. P., Shaheed, M. D., Chandar, T. S., and Talole, S. E., Robust Control of Robot Manipulators Based On Uncertainty and Disturbance Estimation, International Journal of Robust And Nonlinear Control, Vol. 23, No. 1, 2013, pp. 104-122.
[3] Marutheswar, G. V., Chandrakala, C., Control of Rotary Flexible Link with Linear Quadratic Regulator, International Journal of Engineering Research and Applications, Vol. 3, No. 1, 2013, pp. 1699-1703.
[4] Mbede, J. B., Mvogo Ahanda, J. J., Exponential Tracking Control Using Backstepping Approach for Voltage-Based Control of a Flexible Joint Electrically Driven Robot, Journal of Robotics, No. 1, 2014, pp. 1-10.
[5] Haouari, F., Nourdine, B., Boucherit, M. S., and Tadjine, M., A Coefficient Diagram Method Controller with Back Stepping Methodology for Robotic Manipulators, Journal of Electrical Engineering, Vol. 66, No. 5, 2015, pp. 270–276.
[6] Liu, Z. G., Huang, J. M., A New Adaptive Tracking Control Approach for Uncertain Flexible Joint Robot System, International Journal of Automation and Computing ,Vol. 12, No. 5, 2015, pp. 559–566.
[7] Qingxuan, J., Xiaodong, Z., Hanxu, S., and Ming, C., Active Control of Space Flexible-Joint/Flexible-Link Manipulator, IEEE Conference on Robotics, Automation and Mechatronics, Beiging, China, 2008, pp. 1-7.
[8] Ahmadi, S., Fateh, M. M., Control of Flexible Joint Robot Manipulators by Compensating Flexibilty, Iranian Journal of Fuzzy Systems, Vol. 15, No. 4, 2018, pp. 57-71.
[9] Ettefagh, M. H., Naraghi M., and Mahzoon, M., Experimental Control of a Flexible Link by the Method of Controlled Lagrangian, Journal of Theoretical and Applied Vibration and Acoustics, Vol. 4, No. 1, 2018, pp. 81-98.
[10] Pezhman, A., Masoumnezhad, M., and Nouraifar, V., Optimize the Motion Model of Overhead Crane with Accurate Estimate the Equation of Variables State by using Unscented Kalman Filter, 3rd International Conference on Research in Science and Technology, Berlin, Germany, 2016, pp. 1-9.
[11] Lih-Chang, L., King, Y., Feedback Linearization And Linear Stabilization Control of Flexible Robots, Journal of the Chinese Institute of Engineers, Vol. 13, No. 1, 1990, pp. 11-24.
[12] Spong, M. W., Modeling and Control of Robot Manipulators with Elastic Joints, Journal of Dynamic Systems Measurement and Control, Vol. 109, No. 1, 1987, pp. 310-319.
[13] Moberg, S., Hanssen, S., On Feedback Linearization for Robust Tracking Control of Flexible Joint Robots, Proceedings of the 17th World Congress The International Federation of Automatic Control, Seoul, South Korea, 2008, pp. 12218-12223.
[14] Alizadeh, S., Korayem, M. H., and Nekoo, S. R., Design and Implementation of the Output Feedback Linearization Control Method to Determine the DLCC of 6R manipulator, Int J Advanced Design and Manufacturing Technology, Vol. 8, No. 3, 2015, pp. 59-66.
[15] Korayem, M. H., Firouzy, S., Dynamic Load Carrying Capacity of Mobile-Base Flexible link Manipulators: Feedback Linearization Control Approach, International Journal of Robotics, Vol. 3, No. 1, 2013, pp. 34-48.
[16] Cambera, J. C., Chocoteco, J. A., and Feliu, V., Feedback Linearizing Controller for a Flexible Single-Link Link under Gravity and Joint Friction, First Iberian Robotics Conference, Advances in Intelligent Systems and Computing, Springer, Vol. 253, 2014, pp. 169-184.
[17] Jaun, C. C., Vicente, F. B., Input-State Feedback Linearization Control of a Single-Link Flexible Robot Link Moving Under Gravity and Joint Friction, Robotics and Autonomous Systems, Vol. 88, 2017, pp. 24–36.
[18] Yeungs, K. S., Chens, Y. P., Regulation of a One-Link Flexible Robot Link Using Sliding-Mode Technique, International Journal of Control, Vol. 49, No. 6, 1989, pp. 1965-1978.
[19] Hisseine, D., Lohmann B., Robust Control for a Flexible-Link Manipulator Using Sliding Mode Techniques and Nonlinear Control Design Methods, Proceedings of IEEE the International Conference on Robotics & Automation Seoul, South Korea, 2001, pp. 21-26.
[20] Piltan, F., Sulaiman, N., Rashidi, M., Tajpeikar, Z., and Ferdosali, P., Design and Implementation of Sliding Mode Algorithm: Applied to Robot Manipulator-A Review, International Journal of Robotics and Automation,Vol. 2, No. 5, 2011, pp. 265-282.
[21] Etxebarria, V., Sanz, A., and Lizarraga, I., Control of a Lightweight Flexible Robotic Link Using Sliding Modes, International Journal of Advanced Robotic Systems, Vol. 2, No. 2, 2005, pp. 103- 110.
[22] Pezhman, A., Rezapour J., and Mahmoodabadi, M. J., An Optimal Hybrid Adaptive Controller Based On the Multi-Objective Evolutionary Algorithm for an Under-Actuated Nonlinear Ball and Wheel System, Journal of Mechanical Science and Technology, Vol. 34, No. 4, 2020, pp. 1723-1734.
[23] Korayem, M. H., Jalali, M., and Tourajizadeh, H., Dynamic Load Carrying Capacity of Spatial Cable Suspended Robot: Sliding Mode Control Approach, Int J Advanced Design and Manufacturing Technology, Vol. 5, No. 3, 2012, pp. 73-81.
[24] Korayem, M. H., Noroozi, M., and Daeinabi, Kh., Sliding Mode Control of AFM in Contact Mode during Manipulation of Nano-Particle, Int J Advanced Design and Manufacturing Technology, Vol. 6, No. 4, 2013, pp. 83-89.
[25] Yang, X., Zhong Z., Dynamics, and Terminal Sliding Mode Control of Two-Link Flexible Manipulators with Non-collocated Feedback, 3rd IFAC International Conference on Intelligent Control and Automation Science, Beijing, China, 2013, pp. 218-223.
[26] Korayem, M. H., Taherifar, M., and Tourajizadeh, H., Compensating the Flexibility Uncertainties of a Cable Suspended Robot using SMC approach, Robotica, 2014, pp. 1 – 21.
[27] Abdul-Adheem, W. R., Kasim I. I., Improved Sliding Mode Nonlinear Extended State Observer based Active Disturbance Rejection Control for Uncertain Systems with Unknown Total Disturbance, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 12, 2016, pp. 80-93.
[28] Peza-Solís, J. F., Silva-Navarro, G., and Castro-Linares, N. R., Trajectory Tracking Control in a Single Flexible-Link Robot using Finite Differences and Sliding Modes, Journal of Applied Research and Technology, Vol. 13, No.1, 2015, pp. 70-78.
[29] Has, Z., Marzuki, M., and Mardiyah, N. A., Two-Link Flexible Manipulator Control Using Sliding Mode Control Based Linear Matrix Inequality, International Conference on Electrical Engineering Computer Science and Informatics, Vol. 190, 2017, pp. 1-9.
[30] Akbari, A. A., Amini, S., Attitude Control of Unmanned Aerial Vehicle Based on Sliding Mode Technique with Parameter Estimation, Int J Advanced Design and Manufacturing Technology, Vol. 10, No. 2, 2017, pp. 49-59.
[31] Rezapour, J., Afzali, P., Rollover Avoidance in Sport Utility Vehicles: a Multi-Criteria Viewpoint, Automotive Science and Engineering Iran University of Science & Technology, Vol. 10, No. 3, 2020, pp. 3357-3368.
[32] Rezapour, J., Sharifi, M., and Nariman-zadeh, N., Application of Fuzzy Sliding Mode Control to Robotic Manipulator Using Multiobjective Genetic Algorithm, International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey, 2011.
[33] Park, C. W., Robust Stable Fuzzy Control via Fuzzy Modeling and Feedback Linearization with Its Applications to Controlling Uncertain Single-link Flexible Joint Manipulators, Journal of Intelligent and Robotic Systems, Vol. 39, 2004, pp. 137-141.
[34] Ahmad, M. A., Raja Ismail, R. M. T., Ramli, M. S., Zawawi, M. A., and Suid, M.H., Vibration Control Strategy for Flexible Joint Manipulator: A Fuzzy Logic Control Approach, IEEE Symposium on Industrial Electronics and Applications (ISIEA), Penang, Malaysia, 2010, pp. 1-6.
[35] Naderi, D., Ganjefar, S., and Mosadeghzad, M., Optimization Controller and Observer Design of a Reconfigurable Mobile Robot Using Genetic Algorithm and Neural Network, Int J Advanced Design and Manufacturing Technology, Vol. 3, No. 2, 2010, pp 1-16.
[36] Kandroodi, M. R., Mansouri, M., Aliyari Shoorehdeli, M., and Teshnehlab, M., Control of Flexible Joint Manipulator via Reduced Rule-Based Fuzzy Control with Experimental Validation, ISRN Artificial Intelligence, 2012, pp. 1-8.
[37] Ashraf Ahmad, M., Tumari, Z. M., and Kasruddin Nasir, A. N., Composite Fuzzy Logic Control Approach to a Flexible Joint Manipulator, International Journal of Advanced Robotic Systems, Vol. 10, No. 12013, 2013, pp. 1-9.
[38] Bassera, H., Karamib, H., Shamshirbandc, S., Akiba, S., Amirmojahedia, M., Ahmadd, R., Jahangirzadeha, A., and Javidniae, H., Hybrid ANFIS–PSO Approach for Predicting Optimum Parameters of a Protective Spur Dike, Applied Soft Computing, Vol. 30, 2015, pp. 642–649.
[39] Ghazali, M. R., Ibrahim, Z., Saealal, M. S., and Suid, M. H., Single Input Fuzzy Logic Controller for Flexible Joint Manipulator, International Journal of Innovative Computing, Information and Control, Vol. 1, No. 12, 2016, pp. 181-191.
[40] Mahmoodabadi, M. J., Taherkhorsandi, M., Optimal Robust Design of Sliding-mode Control Based on Multi-Objective Particle Swarm Optimization for Chaotic Uncertain Problems, Int J Advanced Design and Manufacturing Technology, Vol. 10, No. 3, 2017, pp. 115-126.
[41] Hagan, M. T., Menhaj, M., Training Feed-Forward Networks with the Marquardt Algorithm, IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1999, pp. 989–993.
[42] Marquardt, D., An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAMJournal on Applied Mathematics, Vol. 11, No. 2, 1963, pp. 431–441.
[43] Jang, J., Yh-Shing, R., Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm, Proceedings of the 9th National Conference on Artificial Intelligence, Vol. 2, No. 14–19, 1991, pp. 762–767.
[44] Andalib Sahnehsaraei, M., Mahmoodabadi, M. J., Taherkhorsandi, M., Castillo-Villar, K. K., and Mortazavi Yazdi, S. M., A Hybrid Global Optimization Algorithm: Particle Swarm Optimization in Association with a Genetic Algorithm, Complex System Modelling and Control Through Intelligent Soft Computations, Springer, 2015, pp. 45-86.