Energy-optimal problem of multiple nonholonomic wheeled mobile robots via distributed event-triggered optimization algorithm
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摘要
The distributed event-triggered optimization problem for multiple nonholonomic robots has been studied to minimize the global battery energy consumption. Each robot possesses its own cost function which depends on the state of the hand position and represents battery energy consumption. By coordinate transformation, the dynamics of the hand positions can be formulated into two groups of first-order integrators. Then the distributed event-triggered optimization algorithm is designed such that the states of robots' hand positions exponentially converge to the optimizer of the global cost function.Meanwhile, the velocity and orientation of each robot are ensured to reach zero and a certain constant, respectively. Moreover, the inter-execution time is lower bounded and the Zeno behavior is therefore naturally avoided. Numerical simulations show the effectiveness of the proposed algorithm.
The distributed event-triggered optimization problem for multiple nonholonomic robots has been studied to minimize the global battery energy consumption. Each robot possesses its own cost function which depends on the state of the hand position and represents battery energy consumption. By coordinate transformation, the dynamics of the hand positions can be formulated into two groups of first-order integrators. Then the distributed event-triggered optimization algorithm is designed such that the states of robots' hand positions exponentially converge to the optimizer of the global cost function.Meanwhile, the velocity and orientation of each robot are ensured to reach zero and a certain constant, respectively. Moreover, the inter-execution time is lower bounded and the Zeno behavior is therefore naturally avoided. Numerical simulations show the effectiveness of the proposed algorithm.
The distributed event-triggered optimization problem for multiple nonholonomic robots has been studied to minimize the global battery energy consumption. Each robot possesses its own cost function which depends on the state of the hand position and represents battery energy consumption. By coordinate transformation, the dynamics of the hand positions can be formulated into two groups of first-order integrators. Then the distributed event-triggered optimization algorithm is designed such that the states of robots' hand positions exponentially converge to the optimizer of the global cost function.Meanwhile, the velocity and orientation of each robot are ensured to reach zero and a certain constant, respectively. Moreover, the inter-execution time is lower bounded and the Zeno behavior is therefore naturally avoided. Numerical simulations show the effectiveness of the proposed algorithm.
引文
[1] Angie S, Farokh B B and Yen I L 2011 10th International Symposium on Autonomous Decentralized Systems, March 23-27, 2011 Tokyo, Hiroshima, Japan, p. 147
[2] Jaleel H, Rahmani A and Egerstedt M 2013 IEEE Trans. Autom. Control 58 534
[3] Wang G Q, Luo H and Hu X X 2018 Chin. Phys. B 27 028901
[4] Hao X C, Liu J S, Xie L X, Chen B and Yan N 2018 Chin. Phys. B 27080102
[5] Alfieri A, Bianco A, Brandimarte P and Chiasserini C F 2007 Eur. J.Oper. Res. 181 390
[6] Tokekar P, Karnad N and Isler V 2011 IEEE International Conference on Robotics and Automation May 9-13, 2011 Shanghai, China, p. 1457
[7] Setter T and Egerstedt M 2017 IEEE Trans. Control Syst. Technol. 251257
[8] Nedic A and Ozdaglar A 2009 IEEE Trans. Autom. Control 54 48
[9] Qiu Z R, Liu S and Xie L H 2016 Automatica 68 209
[10] Xi C G and Khan U A 2017 IEEE Trans. Autom. Control 62 3986
[11] Yi P, Hong Y G and Liu F 2015 Syst. Control Lett. 83 45
[12] Lin P, Ren W and Farrel J A 2017 IEEE Trans. Autom. Control 62 2239
[13] Wang A J, Liao X F and Dong T 2018 IET Control Theory Appl. 121515
[14] Hale M T, Nedic A and Egerstedt M 2017 IEEE Trans. Autom. Control62 4421
[15] Liu P, Li H Q and Dai X G 2018 Int. J. Syst. Sci. 49 1256
[16] Rahili S and Ren W 2017 IEEE Trans. Autom.Control 62 1590
[17] Liu J Y, Chen W S and Dai H 2017 Int. J. Syst. Sci. 48 1836
[18] Guo Z J and Chen G 2018 Int. J. Robust Nonlinear Control 28 4900
[19] Wang J H, Xu Y L, Zhang J and Yang D D 2018 Chin. Phys. B 27040504
[20] Cao J, Wu Z H and Peng L 2016 Chin. Phys. B 25 058902
[21] Miao G Y, Cao J D and Alsaedi A 2017 J. Frankl. Inst. 354 6956
[22] Qi B, Cui B T and Lou X Y 2014 Chin. Phys. B 23 110501
[23] Lii Q G, Li H Q and Xia D W 2017 Neuro computing 235 255
[24] Kia S S, Cortes J and Martinez S 2015 Automatica 55 254
[25] Chen W S and Ren W 2016 Automatica 65 90
[26] Liu J Y, Chen W S and Dai H 2016 Int. J. Control Autom. Syst. 14 1421
[27] Liu S, Xie L H and Quevedo D E 2018 IEEE Trans. Control Network Syst. 5 167
[28] Richert D and Cortes J 2016 SIAM J. Control Optim. 54 1769
[29] Wang D, Gupta V and Wang W 2018 Neurocomputing 319 34
[30] Godsil C and Royle G 2001 Algebraic Graph Theory(New York:Springer-Verlag)
[31] Lawton J, Beard R and Young B 2003 IEEE Trans. Robot. Autom. 19933
[32] Chen X, Hao F and Ma B L 2017 IET Control Theory Appl. 11 890
[33] Horn R A and Johnson C R 2003 Matrix Analysis(Cambridge:Cambridge University Press)
[34] Yan J X, Yu H and Xia X H 2018 Neurcomputing 296 100
[35] Bertsekas D P, Nedic A and Ozdaglar A E 2003 Convex Analysis and Optimization(Belmont:Athena Scientific)
[2] Jaleel H, Rahmani A and Egerstedt M 2013 IEEE Trans. Autom. Control 58 534
[3] Wang G Q, Luo H and Hu X X 2018 Chin. Phys. B 27 028901
[4] Hao X C, Liu J S, Xie L X, Chen B and Yan N 2018 Chin. Phys. B 27080102
[5] Alfieri A, Bianco A, Brandimarte P and Chiasserini C F 2007 Eur. J.Oper. Res. 181 390
[6] Tokekar P, Karnad N and Isler V 2011 IEEE International Conference on Robotics and Automation May 9-13, 2011 Shanghai, China, p. 1457
[7] Setter T and Egerstedt M 2017 IEEE Trans. Control Syst. Technol. 251257
[8] Nedic A and Ozdaglar A 2009 IEEE Trans. Autom. Control 54 48
[9] Qiu Z R, Liu S and Xie L H 2016 Automatica 68 209
[10] Xi C G and Khan U A 2017 IEEE Trans. Autom. Control 62 3986
[11] Yi P, Hong Y G and Liu F 2015 Syst. Control Lett. 83 45
[12] Lin P, Ren W and Farrel J A 2017 IEEE Trans. Autom. Control 62 2239
[13] Wang A J, Liao X F and Dong T 2018 IET Control Theory Appl. 121515
[14] Hale M T, Nedic A and Egerstedt M 2017 IEEE Trans. Autom. Control62 4421
[15] Liu P, Li H Q and Dai X G 2018 Int. J. Syst. Sci. 49 1256
[16] Rahili S and Ren W 2017 IEEE Trans. Autom.Control 62 1590
[17] Liu J Y, Chen W S and Dai H 2017 Int. J. Syst. Sci. 48 1836
[18] Guo Z J and Chen G 2018 Int. J. Robust Nonlinear Control 28 4900
[19] Wang J H, Xu Y L, Zhang J and Yang D D 2018 Chin. Phys. B 27040504
[20] Cao J, Wu Z H and Peng L 2016 Chin. Phys. B 25 058902
[21] Miao G Y, Cao J D and Alsaedi A 2017 J. Frankl. Inst. 354 6956
[22] Qi B, Cui B T and Lou X Y 2014 Chin. Phys. B 23 110501
[23] Lii Q G, Li H Q and Xia D W 2017 Neuro computing 235 255
[24] Kia S S, Cortes J and Martinez S 2015 Automatica 55 254
[25] Chen W S and Ren W 2016 Automatica 65 90
[26] Liu J Y, Chen W S and Dai H 2016 Int. J. Control Autom. Syst. 14 1421
[27] Liu S, Xie L H and Quevedo D E 2018 IEEE Trans. Control Network Syst. 5 167
[28] Richert D and Cortes J 2016 SIAM J. Control Optim. 54 1769
[29] Wang D, Gupta V and Wang W 2018 Neurocomputing 319 34
[30] Godsil C and Royle G 2001 Algebraic Graph Theory(New York:Springer-Verlag)
[31] Lawton J, Beard R and Young B 2003 IEEE Trans. Robot. Autom. 19933
[32] Chen X, Hao F and Ma B L 2017 IET Control Theory Appl. 11 890
[33] Horn R A and Johnson C R 2003 Matrix Analysis(Cambridge:Cambridge University Press)
[34] Yan J X, Yu H and Xia X H 2018 Neurcomputing 296 100
[35] Bertsekas D P, Nedic A and Ozdaglar A E 2003 Convex Analysis and Optimization(Belmont:Athena Scientific)