Hostname: page-component-6d856f89d9-jrqft Total loading time: 0 Render date: 2024-07-16T06:34:39.139Z Has data issue: false hasContentIssue false
  • English
  • Français

PSO-Lyapunov motion/force control of robot arms with model uncertainties

Published online by Cambridge University Press:  04 July 2014

Haifa Mehdi  and
Olfa Boubaker
Haifa Mehdi*
Affiliation:
National Institute of Applied Sciences and Technology, INSAT, Centre Urbain Nord, BP 676-1080 Tunis Cedex, Tunisia
Olfa Boubaker*
Affiliation:
National Institute of Applied Sciences and Technology, INSAT, Centre Urbain Nord, BP 676-1080 Tunis Cedex, Tunisia
*
*Corresponding author. E-mail: haifa.mehdi@gmail.com
E-mail: olfa.boubaker@insat.rnu.tn
Get access Rights & Permissions [Opens in a new window]

Summary

A method for motion/force control of robot arms with model uncertainties is presented. Tracking control of complex trajectories is guaranteed using a Lyapunov approach with high-precision performance ensured using a particle swarm optimization (PSO) algorithm. Tracking performance and robustness are simulated for a robotic device for limb rehabilitation that is designed to be adapted easily to different subjects by considering model parameter uncertainties. Controller parameters are optimized offline using the PSO algorithm with Lyapunov stability conditions considered as inequality constraints. Using the control scheme, the robot can guide limbs on smooth and non-smooth trajectories, under model uncertainties and measurement noise.

Keywords

Motion/force controlStiffness controlConstrained motionRobot armLyapunov theoryModel uncertainties
Type
Articles
Information
Robotica , Volume 34 , Issue 3 , March 2016 , pp. 634 - 651
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Siciliano, B. and Khatib, O., Springer Handbook of Robotics (Springer-Verlag, Berlin, Germany, 2008).CrossRefGoogle Scholar
2.Siciliano, B. and Villani, L., Robot Force Control (Kluwer Academic, Boston, Massachusetts, 1999).CrossRefGoogle Scholar
3.Carbone, G., Grasping in Robotics (Springer-Verlag, London, UK, 2013).CrossRefGoogle Scholar
4.Zeng, G. and Hemami, A., “An overview of robot force control,” Robotica 15, 473–482 (1997).CrossRefGoogle Scholar
5.Chiaverini, S., Siciliano, B. and Villani, L., “A survey of robot interaction control schemes with experimental comparison,” IEEE/ASME Trans. Mechatron. 4, 273–285 (1999).CrossRefGoogle Scholar
6.Briones, J. A., Castillo, E., Carbone, G. and Ceccarelli, M., “Position and Force Control of a Parallel Robot Capaman 2 Bis Parallel Robot for Drilling Tasks,” Proceedings of the IEEE International Conference on Electronics, Robotics and Automotive Mechanics (CERMA `09), Cuernavaca, Morelos (Sept. 22–25, 2009) pp. 181–186.Google Scholar
7.Carbone, G., Villegas, E. and Ceccarelli, M., “Design and validation of force control loops for a parallel manipulator,” Int. J. Intell. Mechatron. Robot. 1, 1–18 (2011).Google Scholar
8.Chen, Y., “Parameter fine-tuning for robots,” IEEE Control Syst. Mag. 9 (2), 35–40 (1989).CrossRefGoogle Scholar
9.Karan, B., “Robust position–force control of robot manipulator in contact with linear dynamic environment,” Robotica 23, 799–803 (2005).CrossRefGoogle Scholar
10.Doulgeri, Z. and Karayiannidis, Y., “Force/position control self-tuned to unknown surface slopes using motion variables,” Robotica 26, 703–710 (2008).CrossRefGoogle Scholar
11.Mehdi, H. and Boubaker, O., “New Robust Tracking Control for Safe Constrained Robots Under Unknown Impedance Environment,” In: Advances in Autonomous Robotics (Herrmann, G.et al., eds.) (Springer-Verlag, Berlin, Germany, 2012) pp. 313–323.CrossRefGoogle Scholar
12.Mehdi, H. and Boubaker, O., “Robust tracking control for constrained robots,” Procedia Eng. 41, 1292–1297 (2012).CrossRefGoogle Scholar
13.Gueaieb, W., Karray, F. and Al-Sharhan, S., “A robust hybrid intelligent position/force control scheme for cooperative manipulators,” IEEE/ASME Trans. Mechatron. 12, 109–125 (2007).CrossRefGoogle Scholar
14.Achili, B., Daachi, B., Amirat, Y., Ali-Cherif, A. and Daâchi, M. E., “A stable adaptive force/position controller for a C5 parallel robot: a neural network approach,” Robotica 30, 1177–1187 (2012).CrossRefGoogle Scholar
15.Li, Z., Ge, S. S., Adams, M. and Wijesoma, W.S., “Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators,” Automatica 44, 776–784 (2008).CrossRefGoogle Scholar
16.Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, New York, 1999).CrossRefGoogle Scholar
17.Kennedy, J., Eberhart, R. and Shi, Y., Swarm Intelligence (Morgan Kaufmann, Burlington, Massachusetts, 2001).Google Scholar
18.Clerc, M. and Kennedy, J., “The particle swarm—explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput. 6, 58–73 (2002).CrossRefGoogle Scholar
19.Dorigo, M. and Birattari, M., “Swarm intelligence,” Scholarpedia 2 (9), 1462 (2007).CrossRefGoogle Scholar
20.Bonabeau, E., Corne, D. W., Knowles, J. D. and Poli, R., “Swarm intelligence theory: a snapshot of the state of the art,” Theor. Comput. Sci. 411, 2081–2083 (2010).CrossRefGoogle Scholar
21.Dorigo, M., Floreano, D., Gambardella, L., Mondada, F., Nolfi, S., Baaboura, T.et al., Swarmanoid: A Novel Concept for the Study of Heterogeneous Robotic Swarms (Resource document) (IRIDIA, Université Libre de Bruxelles, Bruxelles, Belgium, 2013). Available at: http://www.idsia.ch/~gianni/Papers/SwarmanoidPaperTR.pdf (accessed on January 30, 2013).Google Scholar
22.Eberhart, R. C. and Shi, Y., “Comparison between genetic algorithms and particle swarm optimization,” Lect. Notes Comput. Sci. 1447, 611–618 (1998).CrossRefGoogle Scholar
23.Dorigo, M. and Şahin, E., “Swarm robotics,” Auton. Robots 17, 111–113 (2004).CrossRefGoogle Scholar
24.Ducatelle, F., Di Caro, G., Pinciroli, C. and Gambardella, L. M., “Self-organized cooperation between robotic swarms,” Swarm Intell. 5, 73–96 (2011).CrossRefGoogle Scholar
25.Nouyan, S., Campo, A. and Dorigo, M., “Path formation in a robot swarm—self organized strategies to find your way home,” Swarm Intell. 2, 1–23 (2008).CrossRefGoogle Scholar
26.Li, Y., Chen, X., “Mobile robot navigation using particle swarm optimization and adaptive NN,” Lect. Notes Comput. Sci. 3612, 554–559 (2005).Google Scholar
27.Rigatos, G. G., “Multi-robot motion planning using swarm intelligence,” Int. J. Adv. Robot. Syst. 5, 139–144 (2008).CrossRefGoogle Scholar
28.Xu, W., Li, C., Liang, B., Liu, Y. and Xu, Y., “The Cartesian path planning of free-floating space robot using particle swarm optimization,” Int. J. Adv. Robot. Syst. 5, 301–310 (2008).CrossRefGoogle Scholar
29.Sharma, K. D., Chatterjee, A. and Rakshit, A., “A PSO–Lyapunov hybrid stable adaptive fuzzy tracking control approach for vision-based robot navigation,” IEEE Trans. Instrum. Meas. 61, 1908–1914 (2012).CrossRefGoogle Scholar
30.Wei-Der, C. and Shun-Peng, S., “PID controller design of nonlinear systems using an improved particle swarm optimization approach,” Commun. Nonlin. Sci. Numer. Simul. 15, 3632–3639 (2010).Google Scholar
31.Zafer, B. and Oguzhan, K., “A fuzzy logic controller tuned with PSO for 2 DOF robot trajectory control,” Expert Syst. Applic. 38, 1017–1031 (2011).Google Scholar
32.Mehdi, H. and Boubaker, O., “Impedance controller tuned by particle swarm optimization for robotic arms,” Int. J. Adv. Robot. Syst. 8, 93–103 (2011).CrossRefGoogle Scholar
33.Liu, H., Liu, Y. C., Jin, M. H., Sun, K. and Huang, J., “An experimental study on Cartesian impedance control for a joint torque-based manipulator,” Adv. Robot. 22, 1155–1180 (2008).CrossRefGoogle Scholar
34.Jianbin, H., Zongwu, X., Minghe, J., Zainan, J. and Hong, L. “Adaptive impedance-controlled manipulator based on collision detection,” Chin. J. Aeronaut. 22, 105–112 (2009).CrossRefGoogle Scholar
35.Santhakumar, M. and Kim, J., “Indirect adaptive control of an autonomous underwater vehicle-manipulator system for underwater manipulation tasks,” Ocean Eng. 54, 233–243 (2012).Google Scholar
36.Mehdi, H. and Boubaker, O., “Stiffness and impedance control using Lyapunov theory for robot-aided rehabilitation,” Int. J. Social Robot. 4, 107–119 (2012).CrossRefGoogle Scholar
37.Krebs, H. I., Palazzolo, J. J., Dipietro, L., Ferraro, M., Krol, J., Rannekleiv, K., Volpe, B. T. and Hogan, N., “Rehabilitation robotics: performance-based progressive robot-assisted therapy,” Auton. Robots 15, 7–20 (2003).CrossRefGoogle Scholar
38.Loureiro, R., Amirabdollahian, F., Topping, M., Driessen, B. and Harwin, W., “Upper limb robot mediated stroke therapy—GENTLE/s approach,” Auton. Robots 15, 35–51 (2003).CrossRefGoogle Scholar
39.Mehdi, H. and Boubaker, O., “Rehabilitation of a human arm supported by a robotic manipulator: a position/force cooperative control,” J. Comput. Sci. 6, 912–919 (2010).CrossRefGoogle Scholar
40.Eberhart, R. C. and Kennedy, J., “A New Optimizer Using Particle Swarm Theory,” Proceedings of the 6th IEEE International Symposium on Micro Machine and Human Science, Nagoya, Japan (Oct. 4–6, 1995) pp. 39–43.Google Scholar
41.Bratton, D. and Kennedy, J., “Defining a Standard for Particle Swarm Optimization,” Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS), Honolulu, USA (Apr. 1–5, 2007), pp. 120–127.CrossRefGoogle Scholar
42.Shi, Y. and Eberhart, R. C., “A Modified Particle Swarm Optimizer,” Proceedings of the IEEE International Conference on Evolutionary Computation, Anchorage, USA (May 4–9, 1998) pp. 69–73.Google Scholar
43.Santhakumar, M., “Investigation into the dynamics and control of an underwater vehicle-manipulator system,” Model. Simul. Eng. 2013, 1–13 (2013).CrossRefGoogle Scholar