A systematic review of current and emergent manipulator control approaches

doi:10.1007/s11465-015-0335-0

Front. Mech. Eng.

2015, Vol. 10

Issue (2) : 198-210 https://doi.org/10.1007/s11465-015-0335-0

REVIEW ARTICLE

A systematic review of current and emergent manipulator control approaches

Syed Ali AJWAD,Jamshed IQBAL(

),Muhammad Imran ULLAH,Adeel MEHMOOD

Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan

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Abstract

Pressing demands of productivity and accuracy in today’s robotic applications have highlighted an urge to replace classical control strategies with their modern control counterparts. This recent trend is further justified by the fact that the robotic manipulators have complex nonlinear dynamic structure with uncertain parameters. Highlighting the authors’ research achievements in the domain of manipulator design and control, this paper presents a systematic and comprehensive review of the state-of-the-art control techniques that find enormous potential in controlling manipulators to execute cutting-edge applications. In particular, three kinds of strategies, i.e., intelligent proportional-integral-derivative (PID) scheme, robust control and adaptation based approaches, are reviewed. Future trend in the subject area is commented. Open-source simulators to facilitate controller design are also tabulated. With a comprehensive list of references, it is anticipated that the review will act as a first-hand reference for researchers, engineers and industrial-interns to realize the control laws for multi-degree of freedom (DOF) manipulators.

Keywords robot control robust and nonlinear control adaptive control intelligent control industrial manipulators robotic arm

Corresponding Author(s): Jamshed IQBAL

Online First Date: 28 April 2015 Issue Date: 14 July 2015

Cite this article:

Syed Ali AJWAD,Jamshed IQBAL,Muhammad Imran ULLAH, et al. A systematic review of current and emergent manipulator control approaches[J]. Front. Mech. Eng., 2015, 10(2): 198-210.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-015-0335-0
https://academic.hep.com.cn/fme/EN/Y2015/V10/I2/198

Fig.1 AUTAREP realized by researchers at COMSATS Institute, Pakistan [33]

Fig.2 Manipulator control design — Sequential engineering approach

Fig.3 Control architecture of the manipulator-type exoskeleton rehabilitation system [46]

Fig.4 Encoder data of AUTAREP joints for pick & place task [47]

Fig.5 Block diagram of CTC-PD for tracking joint angle of a robot [48]

Fig.6 Tracking using CTC-PD corresponding to various λ values: Ramp responses [49]

Fig.7 Comparative tacking performance in the presence of disturbances: Step responses of SMC and CTC [49]

Fig.8 Waveform illustrating working principle of discrete time MPC

Fig.9 Block diagram illustrating MRAC

Fig.10 Block diagram of adaptive neural network based sliding mode control (NNSMC) control [87]

Fig.11 Block diagram of adaptive Fuzzy logic control

Fig.12 Interface for position control of a manipulator with 3 rotational DOF [96]

Tab.1 Comparative review of reported platforms for simulating control systems for manipulators

1	Khan H, Iqbal J, Baizid K, . Longitudinal and lateral slip control of autonomous wheeled mobile robot for trajectory tracking. Frontiers of Information Technology & Electronic Engineering, 2015, 16(2): 166–172 https://doi.org/10.1631/FITEE.1400183
2	Spong M W, Vidyasagar M. Robot Dynamics and Control. 3rd ed. New York: John Wiley & Sons, 2008
3	Iqbal J, Tsagarakis N G, Fiorilla A E, . A portable rehabilitation device for the hand. In: 2010 Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC). Buenos Aires: IEEE, 2010, 3694–3697 doi:10.1109/IEMBS.2010.5627448
4	Iqbal J, Tsagarakis N, Caldwell D. Design optimization of a hand exoskeleton rehabilitation device. In: Proceedings of Workshop on Understanding the Human Hand for Advancing Robotic Manipulation, Robotics Science and Systems (RSS). Seattle, 2009, 44–45
5	Iqbal J, Tsagarakis N G, Caldwell D G. A human hand compatible optimised exoskeleton system. In: 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO). Tianjin: IEEE, 2010, 685–690 https://doi.org/10.1109/ROBIO.2010.5723409
6	Iqbal J, Tsagarakis N G, Caldwell D G. A multi-DOF robotic exoskeleton interface for hand motion assistance. In: 2011 Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC). Boston: IEEE, 2011, 1575–1578 https://doi.org/10.1109/IEMBS.2011.6090458
7	Iqbal J, Tsagarakis N, Fiorilla A E, . Design requirements of a hand exoskeleton robotic device. In: 14th IASTED International Conference on Robotics and Applications (RA). Massachusetts, 2009, 44–51
8	Khan A A, un Nabi S R, Iqbal J. Surface estimation of a pedestrian walk for outdoor use of power wheelchair based robot. Life Science Journal, 2013, 10(3): 1697–1704
9	Iqbal J, Tsagarakis N, Caldwell D. Design of a wearable direct-driven optimized hand exoskeleton device. In: Proceedings of the Fourth International Conference on Advances in Computer-Human Interactions (ACHI). Gosier: IARIA, 2011, 142–146
10	Azeem M M, Iqbal J, Toivanen P, . Emotions in robots. In: Chowdhry B S, Shaikh F K, Akbar Hussain D M, , eds. Emerging Trends and Applications in Information Communication Technologies. Berlin: Springer, 2012, 144–153
11	Naveed K, Iqbal J, ur Rahman H. Brain controlled human robot interface. In: 2012 International Conference on Robotics and Artificial Intelligence (ICRAI). Rawalpindi: IEEE, 2012, 55–60
12	Iqbal J, Pasha S M, Baizid K, . Computer vision inspired real-time autonomous moving target detection, tracking and locking. Life Science Journal, 2013, 10(4): 3338–3345
13	Iqbal J, Pasha M, Riaz-un-Nabi, . Real-time target detection and tracking: A comparative in-depth review of strategies. Life Science Journal, 2013, 10(3): 804–813
14	Iqbal J, Heikkila S, Halme A. Tether tracking and control of ROSA robotic rover. In: 10th International Conference on Control, Automation, Robotics and Vision (ICARCV). Hanoi: IEEE, 2008, 689–693 https://doi.org/10.1109/ICARCV.2008.4795601
15	Iqbal J, Saad M R, Tahir A M, . State estimation technique for a planetary robotic rover. Revista Facultad de Ingeniería, 2014, 73: 58–68
16	Iqbal J, Tahir A, Islam R U, . Robotics for nuclear power plants—Challenges and future perspectives. In: 2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI). Zurich: IEEE, 2012, 151–156 doi:10.1109/CARPI.2012.6473373
17	Baizid K, Chellali R, Yousnadj A, . Modelling of robotized site and simulation of robot’s optimum placement and orientation zone. In: 21st IASTED International Conference on Modelling and Simulation (MS). Canada, 2010, 9–16
18	Meddahi A, Baizid K, Yousnadj A, . API based graphical simulation of robotized sites. In: IASTED International Conference on Robotics and Applications. Cambridge, 2009, 485–492
19	Groover M P, Weiss M, Nagel R N, . Industrial Robotics: Technology, Programming and Applications. McGraw-Hill Education, 2008
20	Fu K S, Gonzalez R C, Lee C S G. Robotics: Control Sensing Vision and Intelligence. McGraw-Hill Education, 2008
21	Bamdad M. Analytical dynamic solution of a flexible cable-suspended manipulator. Frontiers of Mechanical Engineering, 2013, 8(4): 350–359 https://doi.org/10.1007/s11465-013-0271-9
22	Baizid K, Yousnadj A, Meddahi A, . Time scheduling and optimization of industrial robotized tasks based on genetic algorithms. Robotics and Computer Integrated Manufacturing, 2015, 34: 140–150 https://doi.org/10.1016/j.rcim.2014.12.003
23	Asfahl C. Robots and Manufacturing Automation. 2nd ed. John Wiley & Sons, Inc., 1992
24	Visioli A. Research trends for PID controllers. Acta Polytechnica, 2012, 52(5): 144–150
25	Blevins T. PID advances in industrial control. In: Preprints IFAC Conference on Advances in PID Control. Brescia, 2012
26	McMillan G K. Industrial applications of PID control. In: Vilanova R, Visioli A, eds. PID Control in the Third Millennium. London: Springer, 2012, 415–461 https://doi.org/10.1007/978-1-4471-2425-2_14
27	Brog?rdh T. Present and future robot control development—An industrial perspective. Annual Reviews in Control, 2007, 31(1): 69–79 https://doi.org/10.1016/j.arcontrol.2007.01.002
28	Brog?rdh T. Robot control overview: An industrial perspective. Modeling, Identification and Control, 2009, 30(3): 167–180 https://doi.org/10.4173/mic.2009.3.7
29	Khan M F, Iqbal J, Islam R U. Control strategies for robotic manipulators. In: 2012 International Conference on Robotics and Artificial Intelligence (ICRAI). Rawalpindi: IEEE, 2012, 26–33 https://doi.org/10.1109/ICRAI.2012.6413422
30	Craig J J. Introduction to Robotics. Addison-Wesley Reading, MA, 2006
31	Liang C, Ceccarelli M. Feasible workspace regions for general two-revolute manipulator. Frontiers of Mechanical Engineering, 2011, 6(4): 397–408
32	Islam R U, Iqbal J, Manzoor S, . An autonomous image—Guided robotic system simulating industrial applications. In: 2012 7th International Conference on System of Systems Engineering (SoSE). Genoa: IEEE, 2012, 344–349 https://doi.org/10.1109/SYSoSE.2012.6384195
33	Manzoor S, Islam R U, Khalid A, . An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation. Robotics and Computer-integrated Manufacturing, 2014, 30(3): 351–362 https://doi.org/10.1016/j.rcim.2013.11.003
34	álvarez Chavarría J S, Jiménez Builes J A, Ramírez Pati?o J F. Design cycle of a robot for learning and the development of creativity in engineering. DYNA, 2011, 78(170): 51–58
35	Ajwad S A, Ullah M I, Islam R U, . Modeling robotic arms-A review and derivation of screw theory based kinematics. In: International Conference on Engineering & Emerging Technologies. Lahore, 2014, 66–69
36	Yime-Rodríguez E, Pe?a-Cortés C A, Rojas-Contreras W M. The dynamic model of a four control moment gyroscope system. DYNA, 2014, 81(185): 41–47 https://doi.org/10.15446/dyna.v81n185.35756
37	Márton L, Lantos B. Control of robotic systems with unknown friction and payload. IEEE Transactions on Control Systems Technology, 2011, 19(6): 1534–1539 https://doi.org/10.1109/TCST.2010.2086458
38	Iqbal J, Islam R U, Khan H. Modeling and analysis of a 6 DOF robotic arm manipulator. Canadian Journal on Electrical and Electronics Engineering, 2012, 3(6): 300–306
39	Iqbal J, un Nabi S R, Khan A A, . A novel track-drive mobile robotic framework for conducting projects on robotics and control systems. Journal of Life Science, 2013, 10(3): 130–137
40	Ajwad S A, Iqbal U, Iqbal J. Hardware realization and control of multi-degree of freedom articulated robotic arm. In: Emerging Trends and Applications in Information Communication Technologies, Communications in Computer and Information Science (CCIS). Berlin: Springer2015 (i<?Pub Caret?>n press)
41	Fei Y, Wu Q. Tracking control of robot manipulators via output feedback linearization. Frontiers of Mechanical Engineering in China, 2006, 1(3): 329–335 https://doi.org/10.1007/s11465-006-0034-y
42	Nagaraj B, Murugananth N. A comparative study of PID controller tuning using GA, EP, PSO and ACO. In: 2010 IEEE International Conference on Communication Control and Computing Technologies (ICCCCT). Ramanathapuram: IEEE, 2010, 305–313 https://doi.org/10.1109/ICCCCT.2010.5670571
43	Tan W, Liu J, Chen T, . Comparison of some well-known PID tuning formulas. Computers & Chemical Engineering, 2006, 30(9): 1416–1423 https://doi.org/10.1016/j.compchemeng.2006.04.001
44	Foley M W, Julien R H, Copeland B R. A comparison of PID controller tuning methods. Canadian Journal of Chemical Engineering, 2005, 83(4): 712–722 https://doi.org/10.1002/cjce.5450830412
45	Iqbal J, Tsagarakis N G, Caldwell D G. Human hand compatible underactuated exoskeleton robotic system. Electronics Letters, 2014, 50(7): 494–496 https://doi.org/10.1049/el.2014.0508
46	Iqbal J, Khan H, Tsagarakis N G, . A novel exoskeleton robotic system for hand rehabilitation—Conceptualization to prototyping. Biocybernetics and Biomedical Engineering, 2014, 34(2): 79–89 https://doi.org/10.1016/j.bbe.2014.01.003
47	Iqbal U, Samad A, Nissa Z, . Embedded control system for AUTAREP—A novel AUTonomous articulated robotic educational platform. Technical Gazette, 2014, 21(6): 1255–1261
48	Ajwad S, Ullah M, Baizid K, . A comprehensive state-of-the-art on control of industrial articulated robots. Journal of the Balkan Tribological Association, 2014, 20(4): 499–521
49	Ullah M I, Ajwad S A, Islam R U, . Modeling and computed torque control of a 6 degree of freedom robotic arm. In: 2014 International Conference on Robotics & Emerging Allied Technologies in Engineering (iCREATE). Islamabad: IEEE, 2014, 133–138 https://doi.org/10.1109/iCREATE.2014.6828353
50	Piltan F, Sulaiman N, Jalali A, . Design of model free adaptive fuzzy computed torque controller: Applied to nonlinear second order system. International Journal of Robotics and Automation, 2011, 2(4): 232–244
51	Sharkawy A B, Othman M M, Khalil A M A. A robust fuzzy tracking control scheme for robotic manipulators with experimental verification. Intelligent Control and Automation, 2011, 2(2): 100–111 https://doi.org/10.4236/ica.2011.22012
52	Chen Y, Ma G, Lin S, . Adaptive fuzzy computed-torque control for robot manipulator with uncertain dynamics. International Journal of Advanced Robotic Systems, 2012, 9: 237–245 https://doi.org/10.5772/54643
53	Leines M T, Yang J S. LQR control of an under actuated planar biped robot. In: 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA). Beijing: IEEE, 2011, 1684–1689 https://doi.org/10.1109/ICIEA.2011.5975861
54	Simmons G, Demiris Y. Optimal robot arm control using the minimum variance model. Journal of Robotic Systems, 2005, 22(11): 677–690 https://doi.org/10.1002/rob.20092
55	Islam R U, Iqbal J, Khan Q. Design and comparison of two control strategies for multi-DOF articulated robotic arm manipulator. Control Engineering and Applied Informatics, 2014, 16(2): 28–39
56	Wai R J, Muthusamy R. Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(2): 274–287 https://doi.org/10.1109/TNNLS.2012.2228230
57	Nabaee A R, Piltan F, Ebrahimi M M, . Design intelligent robust partly linear term SMC for robot manipulator systems. International Journal of Intelligent Systems and Applications, 2014, 6: 58–71 https://doi.org/10.5815/ijisa.2014.06.07
58	Hacioglu Y, Arslan Y Z, Yagiz N. MIMO fuzzy sliding mode controlled dual arm robot in load transportation. Journal of the Franklin Institute, 2011, 348(8): 1886–1902 https://doi.org/10.1016/j.jfranklin.2011.05.009
59	Corradini M L, Fossi V, Giantomassi A, . Discrete time sliding mode control of robotic manipulators: Development and experimental validation. Control Engineering Practice, 2012, 20(8): 816–822 https://doi.org/10.1016/j.conengprac.2012.04.005
60	Laghrouche S, Mehmood A, Bagdouri M E. Study of the nonlinear control techniques for single acting VGT pneumatic actuator. International Journal of Vehicle Design, 2012, 60(3/4): 264–285 https://doi.org/10.1504/IJVD.2012.050084
61	Camacho E F, Alba C B. Model Predictive Control. Springer, 2013
62	Wang L. Model Predictive Control System Design and Implementation Using MATLAB?. London: Springer, 2009 https://doi.org/10.1007/978-1-84882-331-0
63	Henmi T, Deng M, Inoue A. Adaptive control of a two-link planar manipulator using nonlinear model predictive control. In: 2010 International Conference on Mechatronics and Automation (ICMA). Xi’an: IEEE, 2010, 1868–1873 https://doi.org/10.1109/ICMA.2010.5588869
64	Mazdarani H, Farrokhi M. Adaptive neuro-predictive control of robot manipulators in work space. In: 2012 17th International Conference on Methods and Models in Automation and Robotics (MMAR). Miedzyzdrojie: IEEE, 2012, 349–354 https://doi.org/10.1109/MMAR.2012.6347864
65	Rojas–Moreno A, Valdivia–Mallqui R. Embedded position control system of a manipulator using a robust nonlinear predictive control. In: 2013 16th International Conference on Advanced Robotics (ICAR). Montevideo: IEEE, 2013, 1–6 https://doi.org/10.1109/ICAR.2013.6766581
66	Copot C, Lazar C, Burlacu A. Predictive control of nonlinear visual servoing systems using image moments. IET Control Theory & Applications, 2012, 6(10): 1486–1496 https://doi.org/10.1049/iet-cta.2011.0118
67	Wang L, Chai S, Rogers E, . Multivariable repetitive-predictive controllers using frequency decomposition. IEEE Transactions on Control Systems Technology, 2012, 20(6): 1597–1604 https://doi.org/10.1109/TCST.2011.2171486
68	Gu D W. Robust Control Design with MATLAB?. London: Springer, 2005 https://doi.org/10.1007/978-1-4471-4682-7
69	Montano O E, Orlov Y. Discontinuous H. ∞-control of mechanical manipulators with frictional joints. In: 2012 9th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). Mexico City: IEEE, 2012, 1–6 https://doi.org/10.1109/ICEEE.2012.6421187
70	Chen C. Robust self-organizing neural-fuzzy control with uncertainty observer for MIMO nonlinear systems. IEEE Transactions on Fuzzy Systems, 2011, 19(4): 694–706 https://doi.org/10.1109/TFUZZ.2011.2136349
71	Siqueira A A G, Terra M H. Mixed model-based/neural network H∞ impedance control of constrained manipulators. In: IEEE International Conference on Control and Automation (ICCA). Christchurch: IEEE, 2009, 1901–1906 https://doi.org/10.1109/ICCA.2009.5410348
72	Yang Z, Fukushima Y, Qin P. Decentralized adaptive robust control of robot manipulators using disturbance observers. IEEE Transactions on Control Systems Technology, 2012, 20(5): 1357–1365 https://doi.org/10.1109/TCST.2011.2164076
73	Kim Y, Seok J, Noh I, . An adaptive disturbance observer for a two-link robot manipulator. In: International Conference on Control, Automation and Systems (ICCAS). Seoul: IEEE, 2008, 141–145 https://doi.org/10.1109/ICCAS.2008.4694540
74	He Z, Xie W. Improved disturbance observer based control structure. In: Chinese Control and Decision Conference (CCDC). Guilin: IEEE, 2009, 1015–1020 https://doi.org/10.1109/CCDC.2009.5191579
75	Parsa M, Farrokhi M. Robust nonlinear model predictive trajectory free control of biped robots based on nonlinear disturbance observer. In: 2010 18th Iranian Conference on Electrical Engineering (ICEE). Isfahan: IEEE, 2010, 617–622 https://doi.org/10.1109/IRANIANCEE.2010.5506996
76	Mohammadi A, Tavakoli M, Marquez H. Disturbance observer-based control of non-linear haptic teleoperation systems. IET Control Theory & Applications, 2011, 5(18): 2063–2074 https://doi.org/10.1049/iet-cta.2010.0517
77	Mohammadi A, Marquez H J, Tavakoli M. Disturbance observer-based trajectory following control of nonlinear robotic manipulators. In: Proceedings of the 23rd Canadian Congress of Applied Mechanics. 2011
78	Mohammadi A, Tavakoli M, Marquez H, . Nonlinear disturbance observer design for robotic manipulators. Control Engineering Practice, 2013, 21(3): 253–267 https://doi.org/10.1016/j.conengprac.2012.10.008
79	Laghrouche S, Ahmed F S, Mehmood A. Pressure and friction observer-based backstepping control for a VGT pneumatic actuator. IEEE Transactions on Control Systems Technology, 2014, 22(2): 456–467 https://doi.org/10.1109/TCST.2013.2258466
80	Arimoto S. Passivity-based control [robot dynamics]. In: IEEE International Conference on Robotics and Automation (ICRA) (Volume 1). San Francisco: IEEE, 2000, 227–232 https://doi.org/10.1109/ROBOT.2000.844063
81	Shibata T, Murakami T. A null space force control based on passivity in redundant manipulator. In: ICM2007 4th IEEE International Conference on Mechatronics. Kumamoto: IEEE, 2007, 1–6 https://doi.org/10.1109/ICMECH.2007.4280027
82	Ott C, Albu-Schaffer A, Kugi A, . On the passivity-based impedance control of flexible joint robots. IEEE Transactions on Robotics, 2008, 24(2): 416–429 https://doi.org/10.1109/TRO.2008.915438
83	Kawai H, Murao T, Sato R, . Passivity-based control for 2DOF robot manipulators with antagonistic bi-articular muscles. In: 2011 IEEE International Conference on Control Applications (CCA). Denver: IEEE, 2011, 1451–1456 https://doi.org/10.1109/CCA.2011.6044445
84	Foudil A, Hadia S. Passivity based control of a 3-DOF robot manipulator. In: International Conference on Communication, Computer & Power (ICCCP). Muscat, 2007, 56–59
85	Bouakrif F, Boukhetala D, Boudjema F. Passivity-based controller-observer for robot manipulators. In: 3rd International Conference on Information and Communication Technologies: From Theory to Applications. Damascus: IEEE, 2008, 1–5 https://doi.org/10.1109/ICTTA.2008.4530121
86	Landau I D, Lozano R, M’Saad M, . Adaptive Control: Algorithms, Analysis and Applications. London: Springer, 2011 https://doi.org/10.1007/978-0-85729-664-1
87	Sun T, Pei H, Pan Y, . Neural network-based sliding mode adaptive control for robot manipulators. Neurocomputing, 2011, 74(14-15): 2377–2384 https://doi.org/10.1016/j.neucom.2011.03.015
88	Aseltine J, Mancini A, Sarture C. A survey of adaptive control systems. IRE Transactions on Automatic Control. 1958, 6(1): 102–108 https://doi.org/10.1109/TAC.1958.1105168
89	?str?m K J, Wittenmark B. On self tuning regulators. Automatica, 1973, 9(2): 185–199 https://doi.org/10.1016/0005-1098(73)90073-3
90	Slotine J J E, Li W. On the adaptive control of robot manipulators. International Journal of Robotics Research, 1987, 6(3): 49–59 https://doi.org/10.1177/027836498700600303
91	Lozano R, Brogliato B. Adaptive control of robot manipulators with flexible joints. IEEE Transactions on Automatic Control, 1992, 37(2): 174–181 https://doi.org/10.1109/9.121619
92	Huang S, Tan K K, Lee T H. Adaptive friction compensation using neural network approximations. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2000, 30(4): 551–557 https://doi.org/10.1109/5326.897081
93	Purwar S, Kar I N, Jha A N. Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints. In: 2004 IEEE International Conference on Fuzzy Systems (Volume 3). IEEE, 2004, 1449–1454 https://doi.org/10.1109/FUZZY.2004.1375387
94	Schindele D, Aschemann H. Adaptive friction compensation based on the LuGre model for a pneumatic rodless cylinder. In: Industrial Electronics, 2009. IECON’09 35th Annual Conference of IEEE. Porto: IEEE, 2009, 1432–1437 https://doi.org/10.1109/IECON.2009.5414726
95	Garpinger O, H?gglund T, Cederqvist L. Software for PID design: Benefits and pitfalls. In: 2nd IFAC Conference on Advances in PID Control. Brescia, 2012, 2(1): 140–145 https://doi.org/10-3182/20120328-3-IT-3014.00024
96	Jara C A, Candelas F A, Gil P, . Ejs+EjsRL: An interactive tool for industrial robots simulation, computer vision and remote operation. Robotics and Autonomous Systems, 2011, 59(6): 389–401 https://doi.org/10.1016/j.robot.2011.02.002
97	Corke P I. A robotics toolbox for MATLAB. IEEE Robotics & Automation Magazine, 1996, 3(1): 24–32 https://doi.org/10.1109/100.486658
98	Corke P. Robotics, Vision and Control: Fundamental Algorithms in MATLAB. Berlin: Springer, 2011
99	Falconi R, Melchiorri C. Roboticad: An educational tool for robotics. In: Proceedings of the 17th IFAC World Congress. Korea, 2008, 9111–9116 https://doi.org/10.3182/20080706-5-KR-1001.01538
100	?lajpah L. Integrated environment for modelling, simulation and control design for robotic manipulators. Journal of Intelligent & Robotic Systems, 2001, 32(2): 219–234 https://doi.org/10.1023/A:1013909415775
101	Maza J I, Ollero A. HEMERO: A MATLAB-Simulink toolbox for robotics. In: 1st Workshop on Robotics Education and Training. Germany, 2001, 43–50
102	Alsina P J. ROBOTLAB: A software for robot graphic simulation. Simpósio Brasileiro de Automa??o Inteligente, 1997, 465–470
103	Nethery J F, Spong M W. Robotica: A mathematica package for robot analysis. IEEE Robotics & Automation Magazine, 1994, 1(1): 13–20 https://doi.org/10.1109/100.296449
104	Dean-Leon E, Nair S, Knoll A. User friendly Matlab-toolbox for symbolic robot dynamic modeling used for control design. In: 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO). Guangzhou: IEEE, 2012, 2181–2188 https://doi.org/10.1109/ROBIO.2012.6491292
105	Rohmer E, Singh S P, Freese M. V-REP: A versatile and scalable robot simulation framework. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Tokyo: IEEE, 2013, 1321–1326 https://doi.org/10.1109/IROS.2013.6696520
106	Breijs A, Klaassens B, Babuska R. Matlab design environment for robotic manipulators. In: 16th IFAC World Congress. Prague, 2005, 1331–1336
107	Shah S V, Nandihal P V, Saha S K. Recursive dynamics simulator (ReDySim): A multibody dynamics solver. Theoretical and Applied Mechanics Letters, 2012, 2: 063011 https://doi.org/10.1063/2.1206311
108	Ferreira N F, Machado J T. RobLib: An educational program for robotics. In: Symposium on Robot Control (SYROCO). Vienna, 2000, 563–568
109	Arm6x Manual. Concurrent Dynamics International, 2014
110	Fueanggan S, Chokchaitam S. Dynamics and kinematics simulation for robots. In: International Association of Computer Science and Information Technology—Spring Conference. Singapore: IEEE, 2009, 136–140 https://doi.org/10.1109/IACSIT-SC.2009.95

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