Fault Tolerant Predictive Control Based on Discrete-Time Sliding Mode Observer for Quadrotor UAV

Qibao Shu; Pu Yang; Yuxia Wang; Ben Ma

doi:10.20965/jaciii.2018.p0498

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JACIII Vol.22 No.4 pp. 498-505

doi: 10.20965/jaciii.2018.p0498

(2018)

Paper:

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Fault Tolerant Predictive Control Based on Discrete-Time Sliding Mode Observer for Quadrotor UAV

Qibao Shu, Pu Yang, Yuxia Wang, and Ben Ma

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics
No.29 Jiangjun Avenue, Jiangning District, Nanjing 211106, China

Received:

October 21, 2017

Accepted:

April 19, 2018

Published:

July 20, 2018

Keywords:

fault-tolerant control, quadrotor UAV, model predictive control, discrete-time sliding mode observer, actuator faults

Abstract

An active fault-tolerant control scheme for a quadrotor unmanned aerial vehicle (UAV) with actuators faults is presented in this paper. The proposed scheme is based on model predictive control (MPC) and the discrete-time sliding mode observer. Considering the impact of disturbances on fault diagnosis, a discrete-time sliding mode observer with simple structure and strong robustness against the disturbances is designed to isolate the actuator faults and estimate the control effectiveness factors accurately. Using the fault diagnosis information, a model predictive active fault tolerant controller with embedded integrator is proposed to compensate parameter uncertainty and bounded disturbances in the realistic control system of the quadrotor. The advantages of the proposed control scheme are the ability of dealing with the control constraints, improving the fault-tolerant control precision and getting better real-time and anti-interference performance. The algorithm comparison experimental results on the quadrotor semi-physical simulation platform validate the feasibility and effectiveness of the proposed control scheme.

Cite this article as:

Q. Shu, P. Yang, Y. Wang, and B. Ma, “Fault Tolerant Predictive Control Based on Discrete-Time Sliding Mode Observer for Quadrotor UAV,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.4, pp. 498-505, 2018.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] C. Liu, B. Jiang, and K. Zhang, “Incipient fault detection using an associated adaptive and sliding-mode observer for quadrotor helicopter attitude control systems,” Circuits, Systems, and Signal Processing, Vol.35, No.10, pp. 3555-3574, 2016.

[2] [2] Y. M. Zhang et al., “Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed,” J. of the Franklin Institute, Vol.350, No.9, pp. 2396-2422, 2013.

[3] [3] Z. X. Liu, C. Yuan, and Y. M. Zhang, “Adaptive fault-tolerant control of unmanned quadrotor helicopter,” Int. Conf. on Unmanned Aircraft System, Control Theory and Applications, Arlington, pp. 980-985, 2016.

[4] [4] A. R. Merheb, H. Noura, and F. Bateman, “Active fault tolerant control of quadrotor UAV using sliding mode control,” Int. Conf. on Unmanned Aircraft Systems, pp. 156-166, 2014.

[5] [5] K. Alexis, G. Nikolakopoulos, and A. Tzes, “Model predictive quadrotor control: attitude, altitude and position experimental studies,” IET Control Theory and Application, Vol.6, No.12, pp. 1812-1827, 2012.

[6] [6] R. D. Zhang, J. Y. Lu, H. Y. Qu, and F. R. Gao, “State space model predictive fault-tolerant control for batch processes with partial actuator failure,” J. of Process Control, Vol.24, No.5, pp. 613-620, 2014.

[7] [7] M. H. Amoozgar, A. Chamseddine, and Y. M. Zhang, “Fault-tolerant fuzzy gain-scheduled PID for a quadrotor helicopter testbed in the presence of actuator faults,” IFAC Proc. Volumes, Vol.45, No.3, pp. 282-287, 2012.

[8] [8] X. He, J. C. Chaudemar, J. Huang, and F. Defay, “Fault tolerant control of quadrotor based on parameter estimation techniques and reconfigurable PID controller,” Joint Int. Mechanical, Electronic and Information Technology Conf., pp. 933-938, 2015.

[9] [9] Z. D. Tian, S. J. Li, and Y. H. Wang, “T-S fuzzy neural network predictive control for burning zone temperature in rotary kiln with improved hierarchical genetic algorithm,” Int. J. of Modelling, Identification and Control, Vol.25, No.4, pp. 323-334, 2016.

[10] [10] Z. D. Tian, S. J. Li, and Y. H. Wang, “Generalized predictive PID control for main steam temperature based on improved PSO algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.21, No.3, pp. 507-517, 2017.

[11] [11] Z. D. Tian, S. J. Li, Y. H. Wang, and H. X. Yu, “Networked control system time-delay compensation based on time-delay prediction and improved implicit GPC,” Algorithms, Vol.8, No.1, pp. 3-18, 2015.

[12] [12] Z. D. Tian, X. W. Gao, B. L. Gong, and T. Shi, “Time-delay compensation method for networked control system based on time-delay prediction and implicit PIGPC,” Int. J. of Automation and Computing, Vol.12, No.6, pp. 648-656, 2015.

[13] [13] Z. D. Tian, S. J. Li, Y. H. Wang, X. D. Wang, and Q. Zhang, “The compensation method for networked control system time-delay based on improved fast implicit GPC,” Int. J. of Control and Automation, Vol.9, No.1, pp. 231-240, 2016.

[14] [14] J. M. Maciejowski and C. N. Jones, “MPC fault-tolerant flight control case study: flight 1862,” IFAC Proc. Volumes, Vol.36, No.5, pp 119-124, 2003.

[15] [15] E. F. Camacho, T. Alamo, and D. M. de la Peña, “Fault-tolerant model predictive control,” IEEE Int. Conf. on Emerging Technologies and Factory Automation, 2010.

[16] [16] D. C. Saluru and R. K. Yedavalli, “Fault tolerant model predictive control of a turbofan engine using C-MAPSS40k,” AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 2013.

[17] [17] B. C. Ding and Z. Tao, “Synthesizing output feedback predictive control for constrained uncertain time-varying discrete systems,” Acta Automatica Sinica, Vol.33, No.1, pp. 78-83, 2007.

[18] [18] F. Xu, V. Puig, C. Ocampo-Martinez, and X. Q. Wang, “Set-valued observer-based active fault-tolerant model predictive control,” Optimal Control Applications and Methods, 2016.

[19] [19] B. Yu, Y. M. Zhang, I. Minchala, and Y. H. Qu, “Fault-tolerant control with linear quadratic and model predictive control techniques against actuator faults in a quadrotor UAV,” Conf. on Control and Fault-Tolerant Systems, pp. 661-666, 2013.

[20] [20] Z. D. Tian, X. W. Gao, and P. Q. Guo, “Network teleoperation robot system control based on fuzzy sliding mode,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.5, pp. 828-835, 2016.

[21] [21] M. Pal, F. Plestan, and A. Chriette, “Discrete-time second-order-sliding-mode observer for state and unknown input estimation: application to a 3DOF helicopter,” European Control Conf., pp. 2914-2919, 2015.

[22] [22] J. Su, W. H. Chen, and B. Li, “High order disturbance observer design for linear and nonlinear systems,” IEEE Int. Conf. on Information and Automation, pp. 1893-1898, 2015.