RBF Neural Network Sliding Mode Consensus of Multiagent Systems with Unknown Dynamical Model of Leader-follower Agents

다국어 초록 (Multilingual Abstract)

This paper proposed a new methodology to cover the problem of consensus of multiagent systems with sliding mode control based on Radial Basis Function (RBF) neural network. First, neural network adopted to distinguish the uncertainties of the leader and follower agents then a sliding mode tracking controller is applied to force the follower agents to follow the leader’s time-varying states trajectory with the consensus error as small as possible. As the RBF neural network is adopted to approximate the uncertainties, the results can only achieve local consensus. Different from past literature, total error of consensus protocol is considering for sliding surface therefore the local stability of the whole multiagent system is provided meanwhile RBF neural network overcome the problem of unmodeled leader/follower agent dynamics. The weights of the neural networks updated adaptively directly commensurate with consensus error. The point is, there is absolutely no need to have information about dynamical model of the system. The merits of the proposed approach are consisting of consensus protocol robustness, fast error convergence to zero, and local stability of the closed loop multiagent system which is proved by Lyapunov direct method. The simulation results show promising performance of the proposed method on a chaotic system.

참고문헌 (Reference)

1 W. He, "Vibration control of a flexible robotic manipulator in the presence of input deadzone" 13 (13): 48-59, 2017

2 Y. Tang, "Tracking control of networked multi-agent systems under new characterizations of impulses and its applications in robotic systems" 63 (63): 1299-1307, 2016

3 T. Ma, "Synchronization of multi-agent stochastic impulsive perturbed chaotic delayed neural networks with switching topology" 151 : 1392-1406, 2015

4 A. Sharaan, "State dependent Riccati equation sliding mode observer for mathematical dynamic model of chronic myelogenous leukemia" 10 (10):

5 A. Sharafian, "Stable state dependent Riccati equation neural observer for a class of nonlinear systems" 28 (28): 256-270, 2017

6 V. Goyal, "Robust sliding mode control for nonlinear discrete-time delayed systems based on neural network" 6 (6): 75-, 2015

7 Z. Li, "Robust second-order consensus tracking of multiple 3-dof laboratory helicopters via output feedback" 20 (20): 2538-2549, 2015

8 Q. Ma, "Output consensus for heterogeneous multi-agent systems with linear dynamics" 271 : 548-555, 2015

9 G. X. Wen, "Neuralnetwork-based adaptive leader-following consensus control for second-order non-linear multi-agent systems" 9 (9): 1927-1934, 2015

10 T. Sun, "Neural network-based sliding mode adaptive control for robot manipulators" 74 (74): 2377-2384, 2011

11 A. Soriano, "Multi-agent systems platform for mobile robots collision avoidance" 320-323, 2013

12 X. Zhao, "Intelligent tracking control for a class of uncertain high-order nonlinear systems" 27 (27): 1976-1982, 2016

13 Y. H. Chang, "Fuzzy sliding-mode formation control for multirobot systems: design and implementation" 42 (42): 444-457, 2012

14 A. Sharafian, "Fractional neural observer design for a class of nonlinear fractional chaotic systems" 2017

15 L. W. Zhao, "Finite-time consensus tracking of second-order multi-agent systems via nonsingular TSM" 75 (75): 311-318, 2014

16 L. Zhao, "Finite-time consensus for secondorder stochastic multi-agent systems with nonlinear dynamics" 270 : 278-290, 2015

17 X. He, "Distributed finite-time containment control for second-order nonlinear multi-agent systems" 268 : 509-521, 2015

18 L. Yu, "Direct adaptive neural control with sliding mode method for a class of uncertain switched nonlinear systems" 6 (6): 5609-5618, 2010

19 Z. G. Hou, "Decentralized robust adaptive control for the multiagent system consensus problem using neural networks" 39 (39): 636-647, 2009

20 W. He, "Cooperative control of a nonuniform gantry crane with constrained tension" 66 : 146-154, 2016

21 Q. Shen, "Cooperative adaptive fuzzy tracking control for networked unknown nonlinear multiagent systems with time-varying actuator faults" 22 (22): 494-504, 2014

22 Z. Li, "Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint" 57 (57): 213-224, 2010

23 H. Wang, "Adaptive neural tracking control for a class of nonlinear systems with dynamic uncertainties" 47 (47): 3075-3087, 2016

24 W. He, "Adaptive neural network control of an uncertain robot with full-state constraints" 46 (46): 620-629, 2016

25 W. He, "Adaptive neural impedance control of a robotic manipulator with input saturation" 46 (46): 334-344, 2016

26 H. Wang, "Adaptive intelligent control of nonaffine nonlinear time-delay systems with dynamic uncertainties" 47 (47): 1315-1320, 2017

27 W. He, "Adaptive fuzzy neural network control for a constrained robot using impedance learning" 2017

28 X. Zhao, "Adaptive fuzzy hierarchical sliding mode control for a class of MIMO nonlinear time-delay systems with input saturation" 25 (25): 1062-1077, 2016

29 C. P. Chen, "Adaptive consensus control for a class of nonlinear multiagent timedelay systems using neural networks" 25 (25): 1217-1226, 2014

30 Yang Liu, "Adaptive Consensus Control for Multiple Euler-Lagrange Systems with External Disturbance" 제어·로봇·시스템학회 15 (15): 205-211, 2017

31 D. H. Nguyen, "A sub-optimal consensus design for multi-agent systems based on hierarchical LQR" 55 : 88-94, 2015

32 S. C. Muller, "A multiagent system for adaptive power flow control in electrical transmission systems" 10 (10): 2290-2299, 2014

33 Nguyen Trong Tai, "A RBF Neural Network Sliding Mode Controller for SMA Actuator" 제어·로봇·시스템학회 8 (8): 1296-1305, 2010

34 Xiang Gong, "A Novel Leader Following Consensus Approach for Multi-agent Systems with Data Loss" 제어·로봇·시스템학회 15 (15): 763-775, 2017