Motion Control of a Ball Throwing Robot with a Flexible Robotic Arm
Motion control of flexible arms is more difficult than
that of rigid arms, however utilizing its dynamics enables improved
performance such as a fast motion in short operation time. This paper
investigates a ball throwing robot with one rigid link and one flexible
link. This robot throws a ball at a set speed with a proper control torque.
A mathematical model of this ball throwing robot is derived through
Hamilton’s principle. Several patterns of torque input are designed and
tested through the proposed simulation models. The parameters of
each torque input pattern is optimized and determined by chaos
embedded vector evaluated particle swarm optimization (CEVEPSO).
Then, the residual vibration of the manipulator after throwing is
suppressed with input shaping technique. Finally, a real experiment is
set up for the model checking.
<p>[1] W. Mori, J. Ueda and T. Ogasawara, “A 1-dof dynamics pitching robot
that independently controls velocity, angular velocity and direction of a
ball”, Advanced Robotics, pp. 921-942, 2012.
[2] Y. Hoshino and Y. Kobayashi, “Shock and vibration control of a
golf-swing robot at impacting the ball”, Journal of System Design and
Dynamics, vol. 2, no. 5.
[3] R. C. Eberhart and Y. Shi, Computing Intelligence. Morgan Kaufmann
Publishers, 2007.
[4] Y. Gai, Y. Kobayashi, R. Yamakawa, Y. Hoshino and T. Emaru, “Motion
control of a flexible robotic arm by utilizing its dynamics”, Asia-Pacific
Vibration Conference, vol. 4, pp.1971-1979, 2011.
[5] M. I. Solihin, W. Akmeliawati and R. Akmeliawati, “PSO-based
optimization of state feedback tracking controller for a flexible link
manipulator”,International Conference of Soft Computing and Pattern
Recognition, pp. 72-76, 2009.
[6] A. Shayeghi, H. Shayeghi and H. E. Kalasar, “Application of PSO
technique for seismic control of tall building”, World Academy of Science,
Engineering and Technology, pp. 851-858, 2009.
[7] Nadia Nedjah, Leandro dos Santos Coelho and Luiza de Macedo de
Mourelle, Multi-Objective Swarm Intelligent Systems. Springer, 2009.
[8] K. E. Parsopoulos and M. N. Vrahatis, Particle Swarm Optimization and
Intelligence: Advances and Applications. Information Science Reference,
2009.
[9] J. G. Vlachogiannis and K. Y. Lee, “Multi-objective based on parallel
vector evaluated particle swarm optimization for optimal steady-state
performance of power systems”, Expert Systems with Applications, pp.
10802-10808, 2009.
[10] S. N. Omkar, D. Mudigere, G. N. Naik and S. Gopalakrishnan, “Vector
evaluated particle swarm optimization (VEPSO) for multi-objective
design optimization of composite structures”, Computers and Structures,
pp. 1-14, 2008.
[11] H. Jiang, C. K. Kwong, Z. Chen and Y. C. Ysim, “Chaos particle swarm
optimization and T-S fuzzy modeling approaches to constrained
predictive control”, Expert Systems with Applications, pp. 194-201, 2012.
[12] B. Alatas, E. Akin and A. B. Ozer, “Chaos embedded particle swarm
optimization algorithms”, Chaos, Solitons and Fractals, pp. 1715-1734,
2009.
[13] W.Singhose and W. Seering, Command Generation for Dynamics
Systems. Georgia Institute of Technology, 2011, pp. 33-54.</p>
<p>[1] W. Mori, J. Ueda and T. Ogasawara, “A 1-dof dynamics pitching robot
that independently controls velocity, angular velocity and direction of a
ball”, Advanced Robotics, pp. 921-942, 2012.
[2] Y. Hoshino and Y. Kobayashi, “Shock and vibration control of a
golf-swing robot at impacting the ball”, Journal of System Design and
Dynamics, vol. 2, no. 5.
[3] R. C. Eberhart and Y. Shi, Computing Intelligence. Morgan Kaufmann
Publishers, 2007.
[4] Y. Gai, Y. Kobayashi, R. Yamakawa, Y. Hoshino and T. Emaru, “Motion
control of a flexible robotic arm by utilizing its dynamics”, Asia-Pacific
Vibration Conference, vol. 4, pp.1971-1979, 2011.
[5] M. I. Solihin, W. Akmeliawati and R. Akmeliawati, “PSO-based
optimization of state feedback tracking controller for a flexible link
manipulator”,International Conference of Soft Computing and Pattern
Recognition, pp. 72-76, 2009.
[6] A. Shayeghi, H. Shayeghi and H. E. Kalasar, “Application of PSO
technique for seismic control of tall building”, World Academy of Science,
Engineering and Technology, pp. 851-858, 2009.
[7] Nadia Nedjah, Leandro dos Santos Coelho and Luiza de Macedo de
Mourelle, Multi-Objective Swarm Intelligent Systems. Springer, 2009.
[8] K. E. Parsopoulos and M. N. Vrahatis, Particle Swarm Optimization and
Intelligence: Advances and Applications. Information Science Reference,
2009.
[9] J. G. Vlachogiannis and K. Y. Lee, “Multi-objective based on parallel
vector evaluated particle swarm optimization for optimal steady-state
performance of power systems”, Expert Systems with Applications, pp.
10802-10808, 2009.
[10] S. N. Omkar, D. Mudigere, G. N. Naik and S. Gopalakrishnan, “Vector
evaluated particle swarm optimization (VEPSO) for multi-objective
design optimization of composite structures”, Computers and Structures,
pp. 1-14, 2008.
[11] H. Jiang, C. K. Kwong, Z. Chen and Y. C. Ysim, “Chaos particle swarm
optimization and T-S fuzzy modeling approaches to constrained
predictive control”, Expert Systems with Applications, pp. 194-201, 2012.
[12] B. Alatas, E. Akin and A. B. Ozer, “Chaos embedded particle swarm
optimization algorithms”, Chaos, Solitons and Fractals, pp. 1715-1734,
2009.
[13] W.Singhose and W. Seering, Command Generation for Dynamics
Systems. Georgia Institute of Technology, 2011, pp. 33-54.</p>
@article{"International Journal of Information, Control and Computer Sciences:57266", author = "Yizhi Gai and Yukinori Kobayashi and Yohei Hoshino and Takanori Emaru", title = "Motion Control of a Ball Throwing Robot with a Flexible Robotic Arm", abstract = "Motion control of flexible arms is more difficult than
that of rigid arms, however utilizing its dynamics enables improved
performance such as a fast motion in short operation time. This paper
investigates a ball throwing robot with one rigid link and one flexible
link. This robot throws a ball at a set speed with a proper control torque.
A mathematical model of this ball throwing robot is derived through
Hamilton’s principle. Several patterns of torque input are designed and
tested through the proposed simulation models. The parameters of
each torque input pattern is optimized and determined by chaos
embedded vector evaluated particle swarm optimization (CEVEPSO).
Then, the residual vibration of the manipulator after throwing is
suppressed with input shaping technique. Finally, a real experiment is
set up for the model checking.
", keywords = "Motion control, flexible robotic arm, CEVEPSO, ball
throwing robot.", volume = "7", number = "7", pages = "967-9", }
