Effective nonlinear predictive and CTC-PID control of rigid manipulators

• Autorzy: Tatjewski Piotr

Identyfikatory

DOI

10.14313/JAMRIS/2‐2024/8

• Języki publikacji: EN

Abstrakty

EN

Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. Model-based predictive control (MPC) algorithms with nonlinear state-space models and most recent disturbance attenuation technique are proposed. This technique makes controller design and online calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison, a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is investigation of influence of sampling period and of computational delay time on performance of the algorithms, which is practically important when using model-based algorithms with fast sampling.

Słowa kluczowe

EN

manipulator control; nonlinear control; model predictive control; CTC-PID control; fast sampling; delay

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2024
• Tom: Vol. 18, No. 2
• Strony: 1--16
• Opis fizyczny: Bibliogr. 37 poz., rys.

Twórcy

autor

Tatjewski Piotr

• Warsaw University of Technology, Nowowiejska 15/19, Warsaw, Poland

• https://orcid.org/0000-0002-2596-8937

Bibliografia

• [1] M.W. Spong. An historical perspective on the control of robotic manipulators. Annual Reviews of Control, Robotics, and Autonomous Systems, 5:1-31, 2022.
• [2] M.F. Khan, R. Islam, and J. Iqbal. Control strategies for robotic manipulators. In Proceedingsof the 2012 International Conference of Robotics and Artificial Intelligence, Rawalpindi, Pakistan, 2012.
• [3] F.L. Lewis, D.M. Dawson, and C.T. Abdallah. Robot Manipulator Control Theory and Practice. CRC Press, Boca Raton, 2003.
• [4] R. Kelly, V. Santibánez, and A. Loría. Control of Robot Manipulators in Joint Space. Springer, London, 2005.
• [5] E.F. Camacho and C. Bordons. Model Predictive Control. Springer Verlag, London, 1999.
• [6] J.M. Maciejowski. Predictive Control. Prentice Hall, Harlow, England, 2002.
• [7] T. L. Blevins, G. K. McMillan, W. K. Wojsznis, and M. W. Brown. Advanced Control Unleashed. The ISA Society, Research Triangle Park, NC, 2003.
• [8] S.J. Qin and T.A. Badgwell. A survey of industrial model predictive control technology. Control Engineering Practice, 11:733-764, 2003.
• [9] P. Tatjewski. Advanced Control of Industrial Processes. Springer Verlag, London, 2007.
• [10] L. Wang. Model Predictive Control System Design and Implementation using MATLAB. Springer Verlag, London, 2009.
• [11] K.J. Holkar and L.M. Waghmare. An overview ofmodel predictive control. International Journal of Control and Automation, 3(4):47-63, 2010.
• [12] T. L. Blevins, W. K. Wojsznis, and M. Nixon. Advanced Control Foundation. The ISA Society, Research Triangle Park, NC, 2013.
• [13] J.B. Rawlings, D.Q. Mayne, and M.M. Diehl. Model Predictive Control: Theory, Computation, and Design 2nd Edition. Nob Hill Publishing, Santa Barbara, California, 2017.
• [14] I.L. Huang, H.H. Lou, J.P. Gong, and T.F. Edgar. Fuzzy model predictive control. IEEE Transactions on Fuzzy Systems, 8(6):665-678, 2000.
• [15] M. Ławryńczuk. Computationally Efϔicient Model Predictive Control Algorithms: A Neural Network Approach. Studies in Systems, Decision and Control, Vol. 3. Springer Verlag, Heidelberg, 2014.
• [16] M.M. Morato, J.E. Normey-Rico, and O. Sename. Model predictive control design for linear parameter varying systems: A survey. Annual Reviews in Control, 49:64-80, 2020.
• [17] M. Ławryńczuk and P. Tatjewski. Offset-free state-space nonlinear predictive control for Wiener systems. Information Sciences, 511:127-151, 2020.
• [18] T. Rybus, K. Seweryn, and J.Z. Sasiadek. Application of predictive control for manipulator mounted on a satellite. Archives of Control Sciences, 28(1):105-118, 2018.
• [19] S. Kleff, A. Meduri, R. Budhiraja, N. Mansard, and L. Righetti. High-frequency nonlinear model predictive control of a manipulator. In Proceedings f the IEEE International Conference on Robotics and Automation, Xi’an, China, 2021.
• [20] P. Bumroongsri and S. Kheawhom. Interpolation-based off-line MPC for LPV systems. In Proceedings of the 10th IFAC International Symposium on Dynamics and Control of Process Systems, Mumbai, India, 2013.
• [21] P.S.G. Cisneros, A.Sridharan, and H. Werner. Constrained predictive control of a robotic manipulator using quasi-LPV representations. IFAC Papers Online Conference Paper Archive, 51(26):118-123, 2018.
• [22] J. Wilson, M. Charest, and R. Dubay. Non-linear model predictive control schemes with application on a 2 link vertical robot manipulator. Robotics and Computer-Integrated Manufacturing, 41:23-30, 2016.
• [23] A. Benniran. Predictive optimizing reference governor for constrained 2 dof’s robot with abrupt set-point trajectories. Journal of Applied Science, Sabratha University, 1:39-49, 2018.
• [24] A. Ferrara, G.P. Incremona, and L. Magni. A robust MPC/ISM hierarchical multi-loop control scheme for robot manipulators. In Proceedings of the 52nd Conference on Decision and Control, Florence, Italy, 2013.
• [25] G.P. Incremona, A. Ferrara, and L. Magni. MPC for robot manipulators with integral sliding modes generation. IEEE/ASME Transactions on Mechatronics, 22(3):1299-1307, 2017.
• [26] S. Bouzoualegh, E. Guechi, and Y. Zennir. Model predictive control of a three degrees of freedom manipulator robot. In Proceedings of the 3rd International Conference on Advanced Systems and Emergent Technologies, pages 84-89, Hammamet, Tunisia, 2019.
• [27] D. Nicolis, F. Allevi, and P. Rocco. Operational space model predictive sliding mode control for redundant manipulators. IEEE Transactions on Robotics, 36(4):1348-1355, 2020.
• [28] P. Tatjewski. Disturbance modeling and state estimation for offset-free predictive control with state-spaced process models. International Journal of Applied Mathematics and Computer Science, 24(2):313-323, 2014.
• [29] P. Tatjewski. Offset-free nonlinear predictive control with measured state and unknown asymptotically constant disturbances. In K. Malinowski, J. Józefczyk, and J. Światek, editors, Aktualne problemy automatyki i robotyki, pages 288-299. Academic Publisher EXIT, Warszawa, Poland, 2014.
• [30] P. Tatjewski. Offset-free nonlinear Model Predictive Control with state-space process models. Archives of Control Sciences, 27(4):595-615, 2017.
• [31] P. Tatjewski and M. Ławryńczuk. Algorithms with state estimation in linear and nonlinear model predictive control. Computers and Chemical Engineering, 143:1-19, 2000.
• [32] P. Tatjewski. Nieliniowe sterowanie predykcyjne ramion manipulatorów (Nonlinear predictive control of manipulator arms). Pomiary Automatyka Robotyka, 27(2):47-58, 2023.
• [33] L. Garcia and E. Rosero. Non-linear model-based predictive control for trajectory tracking and control effort minimization in a smartphone-based quadrotor. Journal of Automation, Mobile Robotics and Intelligent Systems, 16(4):13-18, 2022.
• [34] P. Tatjewski. Sterowanie zaawansowane procesów przemysłowych (Advanced Control of Industrial Processes), Second, revised edition (e-book, in Polish). Academic Publishing House EXIT, Warszawa, 2016.
• [35] F. Reyes and R. Kelly. Experimental evaluation of identification schemes on a direct drive robot. Robotica, 15:563-571, 1997.
• [36] M. Spong, S. Hutchinson, and M. Vidyasagar. Robot Modeling and Control. J. Wiley and Sons, 2005.
• [37] A. Bożek and L. Trybus. Tuning PID and PI-PI servo controllers by multiple pole placement. Bulletin of the Polish Academy of Sciences Technical Sciences, 70(1):1-12, 2022.