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Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

Published online by Cambridge University Press:  19 January 2018

Tiago P. Nascimento Open the ORCID record for Tiago P. Nascimento [Opens in a new window] ,
Carlos E. T. Dórea  and
Luiz Marcos G. Gonçalves
Tiago P. Nascimento*
Affiliation:
Embedded Systems and Robotics Lab (LaSER), Computer Systems Department, Federal University of Paraíba (UFPB), Brazil
Carlos E. T. Dórea
Affiliation:
Computer and Automation Engineering Department, Federal University of Rio Grande do Norte (UFRN), Brazil. E-mails: [email protected], [email protected]
Luiz Marcos G. Gonçalves
Affiliation:
Computer and Automation Engineering Department, Federal University of Rio Grande do Norte (UFRN), Brazil. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Model predictive control (MPC) theory has gained attention with the recent increase in the processing power of computers that are now able to perform the needed calculations for this technique. This kind of control algorithms can achieve better results in trajectory tracking control of mobile robots than classical control approaches. In this paper, we present a review of recent developments in trajectory tracking control of mobile robot systems using model predictive control theory, especially when nonholonomicity is present. Furthermore, we point out the growth of the related research starting with the boom of mobile robotics in the 90s and discuss reported field applications of the described control problem. The objective of this paper is to provide a unified and accessible presentation, placing the classical model, problem formulations and approaches into a proper context and to become a starting point for researchers who are initiating their endeavors in linear/nonlinear MPC applied to nonholonomic mobile robots. Finally, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works, including our contributions to state-of-the-art.

Keywords

Model predictive controlMobile robotsControl of robotic systemsTrajectory trackingNavigation
Type
Articles
Information
Robotica , Volume 36 , Issue 5 , May 2018 , pp. 676 - 696
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Fontes, F. A. C. C., “Discontinuous feedbacks, discontinuous optimal controls, and continuous-time model predictive control,” Int. J. Robust Nonlinear Control 13 (3–4), 191–209 (2003). https://doi.org/10.1002/rnc.813Google Scholar
2. Abbas, M. A., Milman, R. and Eklund, J. M., “Obstacle avoidance in real time with nonlinear model predictive control of autonomous vehicles,” Can. J. Electr. Comput. Eng. 40 (1), 12–22 (2017).Google Scholar
3. Aguiar, A. P., Dacic, D. B., Hespanha, J. P. and Kokotovic, P., “Path-Following or Reference-Tracking? An Answer Relaxing the Limits to Performance,” Proceedings of the IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal (2004) pp. 1–6.Google Scholar
4. Ahmad, A., Nascimento, T. P., Conceição, A. G. S., Moreira, A. P. and Lima, P., “Perception-Driven Multi-Robot Formation Control,” Proceedings of 2013 IEEE International Conference of Robotics Automation (ICRA), Karlsruhe, Germany (2013) pp. 1851–1856.Google Scholar
5. Akiba, S., Zanma, T. and Ishida, M., “Optimal Tracking Control of Two-Wheeled Mobile Robots Based on Model Predictive Control,” Proceedings of 2010 11th IEEE International Workshop on Advanced Motion Control, AMC (2010) pp. 454–459.Google Scholar
6. Allibert, G., Courtial, E., Touré, Y., “Real-time visual predictive controller for image-based trajectory tracking of a mobile robot,” IFAC Proc. Volumes 41 (2), 11,244–11,249 (2008). 17th IFAC World CongressGoogle Scholar
7. Araújo, H. X., ao, A. G. C., Oliveira, G. H., Pitanga, J., “Model Predictive control based on LMIs applied to an omni-directional mobile robot,” IFAC Proc. Vol. 44 (1), 8171–8176 (2011).Google Scholar
8. Azimi, M. M. and Koofigar, H. R., “Model Predictive Control for a Two Wheeled Self Balancing Robot,” Proceedings of the 2013 1st RSI/ISM International Conference on Robotics and Mechatronics, ICRoM (2013) pp. 152–157.Google Scholar
9. Backman, J., Oksanen, T. and Visala, A., “Nonlinear model predictive trajectory control in tractor–trailer system for parallel guidance in agricultural field operations,” IFAC Proc. Vol. 43 (26), 133–138 (2010). 3rd IFAC Conference in Modelling and Control in Agriculture, Horticulture and Post-Harvest Processing - AgricontrolGoogle Scholar
10. Backman, J., Oksanen, T. and Visala, A., “Navigation system for agricultural machines: Nonlinear model predictive path tracking,” Comput. Electron. Agric. 82, 32–43 (2012).CrossRefGoogle Scholar
11. Backman, J., Oksanen, T. and Visala, A., “Collision avoidance method with nonlinear model predictive trajectory control,” IFAC Proc. Vol. 46 (18), 35–40 (2013).Google Scholar
12. Bahadorian, M., Eaton, R., Hesketh, T. and Savkovic, B., “Robust Time-varying model predictive control with application to mobile robot unmanned path tracking,” IFAC Proc. Vol. 47 (3), 4849–4854 (2014). http://www.sciencedirect.com/science/article/pii/S1474667016423657. 19th IFAC World CongressGoogle Scholar
13. Bahadorian, M., Savkovic, B., Eaton, R. and Hesketh, T., “Toward a robust model predictive controller applied to mobile vehicle trajectory tracking control,” IFAC Proc. Vol. 44 (1), 13,552–13,557 (2011). http://www.sciencedirect.com/science/article/pii/S1474667016458015. 18th IFAC World CongressGoogle Scholar
14. Bascetta, L., Ferretti, G., Matteucci, M. and Bossi, M., “LFT-based MPC control of an autonomous vehicle,” IFAC-PapersOnLine 49 (15), 7 – 12 (2016). 9th IFAC Symposium on Intelligent Autonomous Vehicles IAV2016 Leipzig, Germany, 29 June–1 July 2016Google Scholar
15. Biegler, L. T., Efficient Solution of Dynamic Optimization and NMPC Problems. (Birkhäuser Basel, Basel, 2000) pp. 219–243.Google Scholar
16. Camacho, E. F. and Bordons, C., Model Predictive Control (Springer, London, England, 2004).Google Scholar
17. CAO, Z. C., YIN, L. J., FU, Y. L. and LIU, T. L., “Predictive control for visual servo stabilization of nonholonomic mobile robots,” Acta Autom. Sin. 39 (8), 1238–1245 (2013).Google Scholar
18. Conceição, A. S., Dórea, C. E. T. and Barreto, J. C. L., “Predictive control of an omnidirectional mobile robot with friction compensation,” Proceedings of 2010 Latin American Robotics Symposium and Intelligent Robotic Meeting, LARS (2010) pp. 30–35.Google Scholar
19. Conceição, A. S., Moreira, A. P. and Costa, P. J., “A nonlinear model predictive control strategy for trajectory tracking of a four-wheeled omnidirectional mobile robot,” Optim. Control Appl. Methods 29 (5), 335–352 (2008).Google Scholar
20. Conceição, A. S., Oliveira, H. P., Silva, A. S. E., Oliveira, D. and Moreira, A. P., “A Nonlinear Model Predictive Control of an Omni-Directional Mobile Robot,” Proceedings of 2007 IEEE International Symposium Industrial Electron (2007) pp. 2161–2166.Google Scholar
21. Deng, J. and Li, Z., “Model Predictive Control for Visual Servo Steering of Nonholonomic Mobile Robots,” Proceedings of the 2014 11th World Congress on Intelligence Control Automation, WCICA (2014) pp. 347–352.Google Scholar
22. Deng, J., Li, Z. and Su, C. Y., “Trajectory Tracking of Mobile Robots Based on Model Predictive Control Using Primal Dual Neural Network,” Proceedings of the 2014 33rd Chinese Control Conference, CCC (2014) pp. 8353–8358.Google Scholar
23. Donaire, A., Romero, J. G., Perez, T. and Ortega, R., “Smooth Stabilisation of Nonholonomic Robots Subject to Disturbances,” Proceedings of the 2015 IEEE International Conference on Robotics and Automation, ICRA (2015) pp. 4385–4390.Google Scholar
24. Elbanhawi, M., Simic, M. and Jazar, R., “Receding horizon lateral vehicle control for pure pursuit path tracking,” J. Vib. Control (2016) URL 1077546316646,906Google Scholar
25. Elnagar, A. and Hussein, A., “On optimal constrained trajectory planning in 3D environments,” Robot. Auton. Syst. 33 (4), 195–206 (2000).Google Scholar
26. van Essen, H. A. and Nijmeijer, H., “Non-Linear Model Predictive Control for Constrained Mobile Robots,” Proceedings of the 2001 European Control Conference, ECC (2001) pp. 1157–1162.Google Scholar
27. Ferreira, J. R. and Moreira, A. P., “Non-Linear Model Predictive Controller for Trajectory Tracking of an Omni-Directional Robot Using a Simplified Model,” Proceedings of the 9th Portuguese Conference Automation Control, Coimbra, Portugal (2010) pp. 57–62.Google Scholar
28. Findeisen, R. and Allgöwer, F., “An introduction to nonlinear model predictive control,” Proceedings of the 21st Benelux Meeting on Systems and Control, Veldhoven, The Netherlands (2002) 1–23.Google Scholar
29. Fontes, F. A., “A general framework to design stabilizing nonlinear model predictive controllers,” Syst. Control Lett. 42 (2), 127–143 (2001).Google Scholar
30. Forbes, M. G., Patwardhan, R. S., Hamadah, H. and Gopaluni, R. B., “Model predictive control in industry: Challenges and opportunities,” IFAC-PapersOnLine 48 (8), 531–538 (2015). http://www.sciencedirect.com/science/article/pii/S2405896315011039. 9th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2015.Google Scholar
31. Franzè, G. and Lucia, W., “An obstacle avoidance model predictive control scheme for mobile robots subject to nonholonomic constraints: A sum-of-squares approach,” J. Franklin Inst. 352 (6), 2358–2380 (2015).Google Scholar
32. Fruchard, M., Allibert, G. and Courtial, E., “Choice of the Control Horizon in an NMPC Strategy for the Full-State Control of Nonholonomic Systems,” Proceedings of the 2012 American Control Conference, ACC (2012) pp. 4149–4154.Google Scholar
33. Gao, Y., Lee, C. G. and Chong, K. T., “Receding horizon tracking control for wheeled mobile robots with time-delay,” J. Mech. Sci. Technol. 22 (12), 2403 (2008). https://doi.org/10.1007/s12206-008-0746-5Google Scholar
34. Gomez-Ortega, J. and Camacho, E., “Neural network MBPC for mobile robot path tracking,” Robot. Comput.-Integr. Manuf. 11 (4), 271–278 (1994).Google Scholar
35. Grancharova, A., Kocijan, J. and Johansen, T. A., “Explicit Stochastic Nonlinear Predictive Control Based on Gaussian Process Models,” Proceedings of the European Control Conference (2007) pp. 2340–2347.Google Scholar
36. Gu, D. and Hu, H., “Wavelet Neural Network Based Predictive Control for Mobile Robots,” Proceedings of the 2000 IEEE International Conference on Systems, Man, and Cybernetics (2000) vol. 5, pp. 3544–3549.Google Scholar
37. Gu, D. and Hu, H., “Neural predictive control for a car-like mobile robot,” Robot. Auton. Syst. 39 (2), 73–86 (2002).Google Scholar
38. Gu, D. and Hu, H., “A stabilizing receding horizon regulator for nonholonomic mobile robots,” IEEE Trans. Robot. 21 (5), 1022–1028 (2005).Google Scholar
39. Gu, D. and Hu, H., “Receding horizon tracking control of wheeled mobile robots,” IEEE Trans. Control Syst. Technol. 14 (4), 743–749 (2006).Google Scholar
40. Hedjar, R., Alsulaiman, M. and Almutib, K., “Approximated Nonlinear Predictive Control for Trajectory Tracking of a Wheeled Mobile Robot,” Proceedings of the 2011 1st International Conference on Robot, Vision and Signal Processing (2011) pp. 296–299.Google Scholar
41. HU, H. and GU, D., “Generalised Predictive Control of an Industrial Mobile Robot,” Proceedings of the IASTED International Conference on Intelligent Systems and Control, Santa Barbara, California, USA (1999) pp. 235–240.Google Scholar
42. Huang, Y. C. and Li, H. Y., “Receding Horizon Optimal Controller for Reference Trajectory Tracking in Mars Entry Guidance,” Proceedings of 2016 IEEE Chinese Guidance, Navigation Control Conference, CGNCC (2016) pp. 2442–2449.Google Scholar
43. Jayasiri, A., Gros, S., Mann, G. K. I., “Tracking Control and State Estimation of a Mobile Robot Based on NMPC and MHE,” Proceedings of the 2016 American Control Conference, ACC (2016) pp. 1999–2004.Google Scholar
44. Kaliński, K. J. and Mazur, M., “Optimal control at energy performance index of the mobile robots following dynamically created trajectories,” Mechatronics 37, 79–88 (2016).CrossRefGoogle Scholar
45. Kamel, M. A., Zhang, Y. and Yu, X., “Fault-tolerant cooperative control of multiple wheeled mobile robots under actuator faults,” IFAC-PapersOnLine 48 (21), 1152–1157 (2015) . 9th IFAC Symposium on Fault Detection, Supervision andSafety for Technical Processes SAFEPROCESS 2015Google Scholar
46. Kanjanawanishkul, K. and Zell, A., “A Model-Predictive Approach to Formation Control of Omnidirectional Mobile Robots,” Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (2008) 2771–2776.CrossRefGoogle Scholar
47. Kanjanawanushkul, K. and Zell, A., “Path Following for and Omnidirectional Mobile Robot Based on Model Predictive Control,” Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Piscataway, NJ, USA (2009) pp. 3341–3346.Google Scholar
48. Klancar, G. and Skrjanc, I., “Predictive Trajectory Tracking Control for Mobile Robots,” Proceedings of 12th International Conference the Power Electronics and Motion Control, EPE-PEMC (2006) pp. 373–378.Google Scholar
49. Kocijan, J., Murray-Smith, R., Rasmussen, C. E. and Gi-rard, A., “Gaussian Process Model Based Predictive Control,” Proceedings of 4th American Control Conference, Boston, MA (2004) pp. 2214–2218.Google Scholar
50. Kolas, S., Foss, B. and Schei, S., “State estimation is the real challenge in NMPC.” International Workshop on Assessment and Future Directions of NMPC Pavia, Italy, (Sept. 2008) pp. 5–9.Google Scholar
51. Kolmanovsky, I. and McClamroch, N. H., “Developments in nonholonomic control problems,” IEEE Control Syst. 15 (6), 20–36 (1995).Google Scholar
52. Lages, W. F. and Alves, J. A. V., “Real-time control of a mobile robot using linearized model predictive control,” IFAC Proc. Vol. 39 (16), 968–973 (2006). 4th IFAC Symposium on Mechatronic SystemsGoogle Scholar
53. Li, R., Chen, M. and Wu, Q., “Nonlinear Model Predictive Control for WMR with Input Constraint,” Proceedings of the International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (2014) pp. 1–6.Google Scholar
54. Li, Z., Deng, J., Lu, R., Xu, Y., Bai, J. and Su, C. Y., “Trajectory-tracking control of mobile robot systems incorporating neural-dynamic optimized model predictive approach,” IEEE Trans. Syst., Man, Cybern.: Syst. 46 (6), 740–749 (2016).Google Scholar
55. Li, Z., Yang, C., Su, C. Y., Deng, J. and Zhang, W., “Vision-based model predictive control for steering of a nonholonomic mobile robot,” IEEE Trans. Control Syst. Technol. 24 (2), 553–564 (2016).Google Scholar
56. Lian, C., Xu, X., Chen, H. and He, H., “Near-optimal tracking control of mobile robots via receding-horizon dual heuristic programming,” IEEE Trans. Cybern. 46 (11), 2484–2496 (2016).Google Scholar
57. Likar, B. and Kocijan, J., “Predictive control of a gas-liquid separation plant based on a Gaussian process model,” Comput. Chem. Eng. 31 (3), 142–152 (2007)Google Scholar
58. Lim, H., Kang, Y., Kim, C. and Kim, J., “Experimental verification of nonlinear model predictive tracking control for six-wheeled unmanned ground vehicles,” Int. J. Precis. Eng. Manuf. 15 (5), 831–840 (2014).CrossRefGoogle Scholar
59. Lim, H., Kang, Y., Kim, C., Kim, J. and You, B. J., “Nonlinear Model Predictive Controller Design with Obstacle Avoidance for a Mobile Robot,” Proceedings of the IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA '08 (2008) pp. 494–499.Google Scholar
60. Liu, Y., Yu, S., Gao, B. and Chen, H., “Receding Horizon Following Control of Wheeled Mobile Robots: A Case Study,” Proceedings of the 2015 IEEE International Conference on Mechatronics and Automation, ICMA (2015) pp. 2571–2576.Google Scholar
61. Lourenço, J., Lemos, J. and Marques, J., “Control of neuro-muscular blockade with gaussian process models,” Biomed. Signal Process. Control 8 (3), 244–254 (2013)Google Scholar
62. Ma, M. M., Li, S. and Liu, X.J., “Tracking Control and Stabilization of Wheeled Mobile Robots by Nonlinear Model Predictive Control,” Proceedings of the 2012 31st Chinese Control Conference (CCC) (2012) pp. 4056–4061.Google Scholar
63. Maniatopoulos, S., Panagou, D. and Kyriakopoulos, K. J., “Model Predictive Control for the Navigation of a Nonholonomic Vehicle with Field-of-View Constraints,” Proceedings of the 2013 American Control Conference (2013) pp. 3967–3972.Google Scholar
64. Manzoor, M. F., Wu, Q. and Masood, R. J., “Coordination Control of Wheeled Mobile Robot Using MPC,” Proceedings of the 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN (2015) pp. 241–246.Google Scholar
65. Martins, F. N., Celeste, W. C., Carelli, R., Sarcinelli-Filho, M., and Bastos-Filho, T.F., “An adaptive dynamic controller for autonomous mobile robot trajectory tracking,” Control Eng. Pract. 16 (11), 1354–1363 (2008).Google Scholar
66. Maurović, I., Baoti, M. and Petrović, I., “Explicit Model Predictive Control for Trajectory Tracking with Mobile Robots,” Proceedings of the 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM (2011) pp. 712–717.Google Scholar
67. Mayne, D., Rawlings, J., Rao, C. and Scokaert, P., “Constrained model predictive control: Stability and optimality,” Automatica 36 (6), 789–814 (2000).Google Scholar
68. Mehrez, M. W., Mann, G. K. I. and Gosine, R. G., “Stabilizing NMPC of Wheeled Mobile Robots Using Open-Source Real-Time Software,” Proceedings of the 2013 16th International Conference on Advanced Robotics, ICAR (2013) pp. 1–6.Google Scholar
69. Mehrez, M. W., Mann, G. K. I. and Gosine, R. G., “Comparison of Stabilizing NMPC Designs for Wheeled Mobile Robots: An Experimental Study,” Proceedings of the Moratuwa Engineering Research Conference, MERCon (2015) pp. 130–135.Google Scholar
70. Meiling, W., Zhen, W., Yi, Y. and Mengyin, F., “Model Predictive Control for UGV Trajectory Tracking Based on Dynamic Model,” Proceedings of the 2016 IEEE International Conference on Information and Automation, ICIA (2016) pp. 1676–1681.Google Scholar
71. Mohseni, F., Doustmohammadi, A. and Menhaj, M. B., “Distributed model predictive coverage control for decoupled mobile robots,” Robotica 35 (4), 922–941 (2017).Google Scholar
72. Nascimento, T. P., ao, A. G. S. C. and Moreira, A. P., “Multi-robot nonlinear model predictive formation control: Moving target and target absence,” Robot. Auton. Syst. 61 (12), 1502–1515 (2013).Google Scholar
73. Nascimento, T. P., ao, A. G. S. C. and Moreira, A. P., “Multi-robot nonlinear model predictive formation control: The obstacle avoidance problem,” Robotica 34 (3), 549–567 (2014).Google Scholar
74. Nascimento, T. P., Costa, L. F. S., Conceição, A. G. S. and Moreira, A. P., “Nonlinear model predictive formation control: An iterative weighted tuning approach,” J. Intell. & Robot. Syst. 80 (3), 441–454 (2015).Google Scholar
75. Neunert, M., de Crousaz, C., Furrer, F., Kamel, M., Farshidian, F., Siegwart, R. and Buchli, J., “Fast Nonlinear Model Predictive Control for Unified Trajectory Optimization and Tracking,” Proceedings of the 2016 IEEE International Conference on Robotics and Automation, ICRA (2016) pp. 1398–1404.Google Scholar
76. Ostafew, C. J., Schoellig, A. P. and Barfoot, T. D., “Learning-Based Nonlinear Model Predictive Control to Improve Vision-Based Mobile Robot Path-Tracking in Challenging Outdoor Environments,” Proceedings of the 2014 IEEE International Conference on Robotics and Automation, ICRA (2014) pp. 4029–4036.Google Scholar
77. Ostafew, C. J., Schoellig, A. P. and Barfoot, T. D., “Robust constrained learning-based NMPC enabling reliable mobile robot path tracking,” The Int. J. Robot. Res. 1, 1–17 (2016).Google Scholar
78. Pacheco, L., Luo, N. and Ferrer, J., “Local Model Predictive Control Experiences with Differential Driven Wheeled Mobile Robots,” Proceedings of the IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR '08 (2008) vol. 2, pp. 377–382.Google Scholar
79. Pan, Y. and Wang, J., “A Neurodynamic Optimization Approach to Nonlinear Model Predictive Control,” Proceedings of the 2010 IEEE International Conference on Systems Man and Cybernetics, SMC (2010) pp. 1597–1602.Google Scholar
80. Panathula, C. B., Fahimi, F. and Shtessel, Y., “Model Predictive Traction Control for Robots on Slippery 3D Terrains,” Proceedings of the 2012 American Control Conference, ACC (2012) pp. 4257–4262.Google Scholar
81. Piovesan, J. L. and Tanner, H. G., “Randomized Model Predictive Control for Robot Navigation,” Proceedings of the 2009 IEEE International Conference on Robotics and Automation, ICRA '09 (2009) pp. 94–99.Google Scholar
82. Pitanga, J. R., Araújo, H. X., ao, A. G. S. C. and Oliveira, G. H., “Stable model-based predictive control for wheeled mobile robots using linear matrix inequalities,” IFAC-PapersOnLine 48 (19), 33–38 (2015).Google Scholar
83. Poonawala, H. A. and Spong, M. W., “From Nonholonomy to Holonomy: Time-Optimal Velocity Control of Differential Drive Robots,” Proceedings of the 10th International Workshop on Robot Motion Control, RoMoCo (2015) pp. 97–102.Google Scholar
84. Raffo, G. V., Gomes, G. K., Normey-Rico, J. E., Kelber, C. R. and Becker, L. B., “A predictive controller for autonomous vehicle path tracking,” IEEE Trans. Intell. Transp. Syst. 10 (1), 92–102 (2009).Google Scholar
85. Ramirez, D. R., Limon, D., Gomez-Ortega, J. and Camacho, E. F., “Nonlinear MBPC for Mobile Robot Navigation using Genetic Algorithms,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3 (1999) pp. 2452–2457.Google Scholar
86. da Rocha, F. H. M. Jr., Guizilini, V. G. V. C. and Ramos, F., “Model Predictive Control of a Heavy-Duty Truck Based on Gaussian Process,” Proceedings of the XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (2016) pp. 97–102.Google Scholar
87. Rosolia, U., Bruyne, S. D. and Alleyne, A. G., “Autonomous vehicle control: A nonconvex approach for obstacle avoidance,” IEEE Trans. Control Syst. Technol. 25 (2), 469–484 (2017).Google Scholar
88. S., J.C.L.B, ao, A.G.S.C., Dórea, C. E. T., Martinez, L. and de Pieri, E. R., “Design and implementation of model-predictive control with friction compensation on an omnidirectional mobile robot,” IEEE/ASME Trans. Mechatron. 19 (2), 467–476 (2014).Google Scholar
89. Seder, M., Baotić, M. and Petrović, I., “Receding horizon control for convergent navigation of a differential drive mobile robot,” IEEE Trans. Control Syst. Technol. 25 (2), 653–660 (2017).Google Scholar
90. Shen, C., Shi, Y., and Buckham, B., “Nonlinear Model Predictive Control for Trajectory Tracking of an Auv: A Distributed Implementation,” Proceedings of the IEEE 55th Conference on Decision and Control, CDC (2016) pp. 5998–6003.Google Scholar
91. Siegwart, R. and Nourbakhsh, I. R., Intelligent Robotics and Autonomous Agents (The MIT Press, Cambridge, MA, 861 2004).Google Scholar
92. Sigaud, O., Salaün, C. and Padois, V., “On-line regression algorithms for learning mechanical models of robots: A survey,” Robot. Auton. Syst. 59 (12), 1115–1129 (2011).Google Scholar
93. Teatro, T. A. V., Eklund, J. M. and Milman, R., “Nonlinear model predictive control for omnidirectional robot motion planning and tracking with avoidance of moving obstacles,” Can. J. Electr. Comput. Eng. 37 (3), 151–156 (2014).Google Scholar
94. Thrun, S., Burgard, W. and Fox, D., Efficient Solution of Dynamic Optimization and NMPC Problems (MIT Press, Cambridge, MA, 2005).Google Scholar
95. Vougioukas, S. G., “Reactive Trajectory Tracking for Mobile Robots Based on Non Linear Model Predictive Control,” Proceedings of IEEE International Conference on Robotics and Automation (2007) pp. 3074–3079.Google Scholar
96. Wang, Z., Kinugawa, J., Wang, H. and Kazuhiro, K., “The Simulation of Nonlinear Model Predictive Control for a Human-Following Mobile Robot,” Proceedings of the IEEE International Conference on Robotics and Biomimetics, ROBIO (2015) pp. 415–422.Google Scholar
97. Wei, S., Uthaichana, K., Zefran, M. and DeCarlo, R., “Hybrid model predictive control for the stabilization of wheeled mobile robots subject to wheel slippage,” IEEE Trans. Control Syst. Technol. 21 (6), 2181–2193 (2013).Google Scholar
98. Worthmann, K., Mehrez, M. W., Zanon, M., Mann, G. K. I., Gosine, R. G. and Diehl, M., “Model predictive control of nonholonomic mobile robots without stabilizing constraints and costs,” IEEE Trans. Control Syst. Technol. 24 (4), 1394–1406 (2016).Google Scholar
99. Yang, T. T., Liu, Z. Y., Chen, H., and Pei, R., “Formation control and obstacle avoidance for multiple mobile robots,” Acta Autom. Sin. 34 (5), 588–593 (2008).Google Scholar
100. Yoo, S. J., Choi, Y. H. and Park, J. B., “Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: Adaptive learning rates approach,” IEEE Trans. Circuits Syst. I: Regul. Pap. 53 (6), 1381–1394 (2006).Google Scholar
101. Yoon, Y., Choe, T., Park, Y. and Kim, H. J., “Obstacle Avoidance for Wheeled Robots in Unknown Environments Using Model Predictive Control,” Proceedings of the 17th World Congress the International Federation of Automatic Control (2008) vol. 41 (2), pp. 6792–6797.Google Scholar
102. Yu, S., Li, X., Chen, H. and Allgöwer, F., “Nonlinear model predictive control for path following problems,” IFAC Proc. Vol. 45 (17), 145–150 (2012).Google Scholar
103. Yue, M., An, C. and Li, Z., “Constrained adaptive robust trajectory tracking for wip vehicles using model predictive control and extended state observer,” IEEE Trans. Syst., Man Cybern.: Syst. PP (99), 1–10 (2017).Google Scholar
104. Zeng, Z., Lu, H. and Zheng, Z., “High-Speed Trajectory Tracking Based on Model Predictive Control for Omni-Directional Mobile Robots,” Proceedings of the 25th Chinese Control and Decision Conference, CCDC (2013) pp. 3179–3184.Google Scholar
105. Zhou, J., Tang, S., Li, X. and Yin, X., “A Novel Heading Predictive Control Model for Autonomous Ground Vehicles,” Proceedings of the 2016 IEEE International Conference on Mechatronics and Automation (2016) pp. 117–121.Google Scholar
106. Zhu, Y. and Özgüner, Ü., “Constrained model predictive control for nonholonomic vehicle regulation problem,” IFAC Proc. Vol. 41 (2), 9552–9557 (2008). 17th IFAC World CongressGoogle Scholar