Abstract
Based on Lyapunov theory, this research demonstrates the stability of the sliding surface in the consensus problem of multi-agent systems. Each agent in this system is represented by the dynamically uncertain robot, unstructured disturbances, and nonlinear friction, especially when the dynamic function of agent is unknown. All system states use neural network online weight tuning algorithms to compensate for the disturbance and uncertainty. Each agent in the system has a different position, and their trajectory approach to the same target is from each distinct orientation. In this research, we analyze the design of the sliding surface for this model and demonstrate which type of sliding surface is the best for the consensus problem. Lastly, simulation results are presented to certify the correctness and the effectiveness of the proposed control method.
Highlights
In recent years, using Sliding Mode Control (SMC) for robotic trajectory control of nonlinear dynamic properties has received much attention from researchers
The SMC method is relatively easy to implement, but in nonlinear dynamic systems, there are many uncertainties, such as disturbance and friction, that are the main reasons for the reduction of control quality
To deal with this problem, the uncertainty of the system is compensated for by using of sliding mode control combined with a Neural Network (NN), which has been introduced in [1,2,3,4,5,6,7], in which the control algorithm drawnon Lyapunov theorem, the sliding mode control structure, and the neural network learning algorithm are developed
Summary
In recent years, using Sliding Mode Control (SMC) for robotic trajectory control of nonlinear dynamic properties has received much attention from researchers. Has proposed some significant results for nonlinear multi-agent systems when using sliding mode control type, the problems of disturbance rejection with uncertainties for the entire system has not been considered completely in literature. Motivated by the discussion above, different from the previous works, in this research, a synergistic combination of Proportional–Integral–Derivative (PID) sliding mode control with neural networks is proposed to reject disturbances and uncertainties of a nonlinear dynamic system in the consensus problem of multi-agent systems. For the first time, the NN in collaboration with PID sliding mode control [29,30] is implemented to the uncertain nonlinear multi-agent systems for solving the robust adaptive consensus problem. An adaptive neural network controller is designed to neutralize the disturbance and uncertain nonlinear dynamics in the multi-agent system. Rn and Rn×m denote the n-dimensional Euclidean space and the set of n × m real matrices, respectively
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