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localization; mobile robot; Riccati equations; stochastic communication protocol
In this paper, the mobile robot localization problem is investigated under the stochastic communication protocol (SCP). In the mobile robot localization system, the measurement data including the distance and the azimuth are received by multiple sensors equipped on the robot. In order to relieve the network burden caused by network congestion, the SCP is introduced to schedule the transmission of the measurement data received by multiple sensors. The aim of this paper is to find a solution to the robot localization problem by designing a time-varying filter for the mobile robot such that the filtering error dynamics satisfies the $H_{\infty}$ performance requirement over a finite horizon. First, a Markov chain is introduced to model the transmission of measurement data. Then, by utilizing the stochastic analysis technique and completing square approach, the gain matrices of the desired filter are designed in term of a solution to two coupled backward recursive Riccati equations. Finally, the effectiveness of the proposed filter design scheme is shown in an experimental platform.
[1] Chen, J., Qiao, H.: Muscle-synergies-based neuromuscular control for motion learning and generalization of a musculoskeletal system. IEEE Trans. Systems Man Cybernet.: Systems (2020), 1-14.
[2] Chen, S. Y.: Kalman filter for robot vision: A survey. IEEE Trans. Industr. Electron. 59 (2012), 11, 4409-4420. DOI 10.1109/tie.2011.2162714
[3] Chen, W., Ding, D., Ge, X., Han, Q., Wei, G.: $H_{\infty}$ containment control of multiagent systems under event-triggered communication scheduling: The finite-horizon case. IEEE Trans. Cybernet. 50 (2020), 4, 1372-1382. DOI 10.1109/tcyb.2018.2885567
[4] Ding, D., Han, Q., Wang, Z., Ge, X.: A survey on model-based distributed control and filtering for industrial cyber-physical systems. IEEE Trans. Industr. Inform. 15 (2019), 5, 2483-2499. DOI 10.1109/tii.2019.2905295
[5] Ding, D., Wang, Z., Han, Q.: Neural-network-based output-feedback control with stochastic communication protocols. Automatica 106 (2019), 221-229. DOI 10.1016/j.automatica.2019.04.025 | MR 3952583
[6] Ding, D., Wang, Z., Han, Q., Wei, G.: Neural-network-based output-feedback control under round-robin scheduling protocols. IEEE Trans. Cybernet. 49 (2019), 6, 2372-2384. DOI 10.1109/tcyb.2018.2827037
[7] Ding, D., Wang, Z., Han, Q., Wei, G.: Security control for discrete-time stochastic nonlinear systems subject to deception attacks. IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 5, 779-789. DOI 10.1109/tsmc.2016.2616544
[8] Ding, D., Wang, Z., Ho, D. W. C., Wei, G.: Distributed recursive filtering for stochastic systems under uniform quantizations and deception attacks through sensor networks. Automatica 78 (2017), 231-240. DOI 10.1016/j.automatica.2016.12.026 | MR 3614098
[9] Dong, H., Hou, N., Wang, Z., Liu, H.: Finite-horizon fault estimation under imperfect measurements and stochastic communication protocol: Dealing with finite-time boundedness. Int. J.Robust Nonlinear Control 29 (2019), 1, 117-134. DOI 10.1002/rnc.4382 | MR 3886112
[10] Ge, X., Han, Q.: Consensus of multiagent systems subject to partially accessible and overlapping Markovian network topologies. IEEE Trans. Cybernet. 47 (2017), 8, 1807-1819. DOI 10.1109/tcyb.2016.2570860
[11] Ge, X., Han, Q., Wang, Z.: A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks. IEEE Trans. Cybernet. 49(2019), 1, 171-183. DOI 10.1109/tcyb.2016.2570860
[12] Ge, X., Han, Q., Wang, Z.: A threshold-parameter-dependent approach to designing distributed event-triggered $H_{\infty}$ consensus filters over sensor networks. IEEE Trans. Cybernet. 49 (2019), 4, 1148-1159. DOI 10.1109/tcyb.2017.2789296
[13] Guan, R. P., Ristic, B., Wang, L., Moran, B., Evans, R.: Feature-based robot navigation using a Doppler-azimuth radar. Int. J. Control 90 (2017), 4, 888-900. DOI 10.1080/00207179.2016.1244727 | MR 3613055
[14] Hu, L., Wang, Z., Han, Q., Liu, X.: State estimation under false data injection attacks: Security analysis and system protection. Automatica 87 (2018), 176-183. DOI 10.1016/j.automatica.2017.09.028 | MR 3733913
[15] Huang, C., Shen, B., Chen, H., Shu, H.: A dynamically event-triggered approach to recursive filtering with censored measurements and parameter uncertainties. J. Franklin Inst. 356 (2019), 15, 8870-8889. DOI 10.1016/j.jfranklin.2019.08.029 | MR 4010163
[16] Khan, A., Rinner, B., Cavallaro, A.: Cooperative robots to observe moving targets: Review. IEEE Trans. Cybernet. 48 (2018), 1, 187-198. DOI 10.1109/tcyb.2016.2628161
[17] Kim, Y., An, J., Lee, J.: Robust navigational system for a transporter using GPS/INS fusion. IEEE Trans. Industr. Electron. 65 (2018), 4, 3346-3354. DOI 10.1109/tie.2017.2752137
[18] Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual-inertial odometry using nonlinear optimization. Int. J. Robotics Res. 34 (2015), 3, 314-334. DOI 10.1177/0278364914554813
[19] Li, B., Wang, Z., Han, Q., Liu, H.: Input-to-state stabilization in probability for nonlinear stochastic systems under quantization effects and communication protocols. IEEE Trans. Cybernet. 49 (2019), 9, 3242-3254. DOI 10.1109/tcyb.2018.2839360 | MR 3998230
[20] Li, Q., Shen, B., Wang, Z., Huang, T., Luo, J.: Synchronization control for a class of discrete time-delay complex dynamical networks: A dynamic event-triggered approach. IEEE Trans. Cybernet. 49 (2019), 5, 1979-1986. DOI 10.1109/tcyb.2018.2818941 | MR 3891660
[21] Li, R., Qiao, H.: A survey of methods and strategies for high-precision robotic grasping and assembly tasks-some new trends. IEEE/ASME Trans. Mechatronics 24 (2019), 6, 2718-2732. DOI 10.1109/tmech.2019.2945135
[22] Li, X., Chen, W., Chan, C., Li, B., Song, X.: Multi-sensor fusion methodology for enhanced land vehicle positioning. Inform. Fusion 46 (2019), 51-62. DOI 10.1016/j.inffus.2018.04.006
[23] Liu, H., Sun, F., Fang, B., Zhang, X.: Robotic room-level localization using multiple sets of sonar measurements. IEEE Trans. Instrument. Measurement 66 (2017), 1, 2-13. DOI 10.1109/tim.2016.2618978
[24] Lowry, S., Sunderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., Milford, M. J.: Visual place recognition: A survey. IEEE Trans. Robotics 32 (2016), 1, 1-19. DOI 10.1109/tro.2015.2496823
[25] Luo, R. C., Hsiao, T. J.: Dynamic wireless indoor localization incorporating with an autonomous mobile robot based on an adaptive signal model fingerprinting approach. IEEE Trans. Industr. Electron. 66 (2019), 3, 1940-1951. DOI 10.1109/tie.2018.2833021
[26] Luo, Y., Wang, Z., Wei, G., Alsaadi, F. E., Hayat, T.: State estimation for a class of artificial neural networks with stochastically corrupted measurements under round-robin protocol. Neural Networks 77 (2016), 70-79. DOI 10.1016/j.neunet.2016.01.001
[27] Ma, L., Wang, Z., Liu, Y., Alsaadi, F. E.: Distributed filtering for nonlinear time-delay systems over sensor networks subject to multiplicative link noises and switching topology. Int.J. Robust Nonlinear Control 29 (2019), 10, 2941-2959. DOI 10.1002/rnc.4535 | MR 3973575
[28] Ma, L., Wang, Z., Han, Q., Liu, Y.: Dissipative control for nonlinear Markovian jump systems with actuator failures and mixed time-delays. Automatica 98 (2018), 358-362. DOI 10.1016/j.automatica.2018.09.028 | MR 3866952
[29] Shen, B., Wang, Z., Qiao, H.: Event-triggered state estimation for discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. IEEE Trans. Neural Networks Learning Systems 28 (2017), 5, 1152-1163. DOI 10.1109/tnnls.2016.2516030 | MR 3721783
[30] Shen, B., Wang, Z., Wang, D., Luo, J., Pu, H., Peng, Y.: Finite-horizon filtering for a class of nonlinear time-delayed systems with an energy harvesting sensor. Automatica 100 (2019), 144-152. DOI 10.1016/j.automatica.2018.11.010 | MR 3881144
[31] Wan, X., Wang, Z., Han, Q., Wu, M.: A recursive approach to quantized $H_{\infty}$ state estimation for genetic regulatory networks under stochastic communication protocols. IEEE Trans. Neural Networks Learning Systems 30 (2019), 9, 2840-2852. DOI 10.1109/tnnls.2018.2885723 | MR 4001276
[32] Wan, X., Wang, Z., Han, Q., Wu, M.: Finite-time $H_{\infty}$ state estimation for discrete time-delayed genetic regulatory networks under stochastic communication protocols. IEEE Trans. Circuits Systems I: Regular Papers 65 (2018), 10, 3481-3491. DOI 10.1109/tcsi.2018.2815269 | MR 3854691
[33] Wang, Z., Dong, H., Shen, B., Gao, H.: Finite-horizon $H_{\infty}$ filtering with missing measurements and quantization effects. IEEE Trans. Automat. Control 58 (2013), 7, 1707-1718. DOI 10.1109/tac.2013.2241492 | MR 3072855
[34] Xu, W., Ho, D. W. C., Li, L., Cao, J.: Event-triggered schemes on leader-following consensus of general linear multiagent systems under different topologies. IEEE Trans. Cybernetics 47 (2017), 1, 212-223. DOI 10.1109/tcyb.2015.2510746
[35] Yang, F., Wang, Z., Lauria, S., Liu, X.: Mobile robot localization using robust extended $H_{\infty}$ filtering. Proc. Inst. Mechanical Engineers, Part I: J. Systems Control Engrg. 223 (2009), 8, 1067-1080. DOI 10.1243/09596518jsce791
[36] Zhang, X., Han, Q.: A decentralized event-triggered dissipative control scheme for systems with multiple sensors to sample the system outputs. IEEE Trans. Cybernet. 46 (2016), 12, 2745-2757. DOI 10.1109/tcyb.2015.2487420
[37] Zhang, X., Han, Q., Ge, X., Ding, D., Ding, L., Yue, D., Peng, C.: Networked control systems: A survey of trends and techniques. IEEE/CAA J. Automat. Sinica (2019), 1-17. DOI 10.1109/tcyb.2015.2487420 | MR 3748030
[38] Zou, L., Wang, Z., Gao, H.: Observer-based $H_{\infty}$ control of networked systems with stochastic communication protocol: The finite-horizon case. Automatica 63 (2016), 366-373. DOI 10.1016/j.automatica.2015.10.045 | MR 3430004
[39] Zou, L., Wang, Z., Gao, H.: Set-membership filtering for time-varying systems with mixed time-delays under round-robin and weighted try-once-discard protocols. Automatica 74 (2016), 341-348. DOI 10.1016/j.automatica.2016.07.025 | MR 3569400
[40] Zuo, Z., Han, Q., Ning, B., Ge, X., Zhang, X.: An overview of recent advances in fixed-time cooperative control of multiagent systems. IEEE Trans. Industr. Inform. 14 (2018), 6, 2322-2334. DOI 10.1109/tii.2018.2817248 | MR 3932129
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