Fuzzy Sliding Mode Control of Multi-Agent Systems Using Artificial Potentials

Author(s):  
F. Roshanghalb ◽  
J. Mortazavi ◽  
A. Alasty ◽  
H. Sayyaadi

In this paper a fuzzy control strategy of autonomous multi-agent systems is presented. The main purpose is to obtain an improvement on the results of designed sliding mode controllers in previous articles using supervisory fuzzy controller. Similarly, a quasi-static swarm model in n-dimensional space is introduced wherein the inter-individual interactions are based on artificial potential functions; and the motions of members are in direction with the negative gradient of the combined potentials which are the result of a balance between inter-individual interactions and the simultaneous interactions of the swarm members with their environment. Then a general model for vehicle dynamics of each agent has been studied. The aim is to reach the desirable formation of swarm by forcing agent’s motion to follow the quasi-static model. In literature a sliding mode controller is used to perform this task. In this work, same control structure is applied with a difference in control parameters. In this paper, fuzzy rules are introduced for some major control parameters which are efficacious on the results in order to get better performances along with less error during the formation control approach.

2020 ◽  
Vol 42 (8) ◽  
pp. 1461-1474 ◽  
Author(s):  
Mahdi Siavash ◽  
Vahid Johari Majd ◽  
Mahdie Tahmasebi

In this paper, the fault-tolerant formation control of nonlinear stochastic multi-agent systems in the presence of actuator faults, disturbances, and time-varying weighted topology is considered. While most traditional fault-tolerant control methods in the literature use fixed weights on the topology edges, in this study these weights are considered time-varying using a pre-designed function, which allows formulating the system more realistically. Moreover, in contrast with previous works on fault-tolerant multi-agent systems, in this study, the model of the agents is considered to be stochastic in general. Furthermore, the actuators of the agents are considered to have a time-varying fault of additive and multiplicative types. A passive and an active fault-tolerant controllers are designed based on the back-stepping sliding-mode approach. In the passive method, a constant robust controller is proposed using an upper bound of the faults while, in the active controller, the additive and multiplicative faults are estimated using adaptive laws. The active and passive fault-tolerant controllers guarantee that the formation errors converge to a bounded region near the origin in a mean-square sense and all of the existing signals in the closed-loop system remain bounded in probability. The results of the formation control are extended to consensus control as well. Finally, a stochastic multi-aircraft model and an RLC circuit with stochastic part are used as two case studies to illustrate the effectiveness of the proposed design method.


2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986273 ◽  
Author(s):  
Nguyen Xuan-Mung ◽  
Sung Kyung Hong

The formation control problem for multi-agent systems has been explored in recent years. However, controlling a formation of multiple aerial vehicles in the presence of disturbances has been a challenge for control researchers. To deal with this issue, a robust adaptive formation control algorithm for a group of multiple quadcopters is proposed. A nonlinear model of the dynamics of the formation error is obtained based on a leader–follower scheme. This model considers both the relative position in the x– y plane and the relative heading angle between vehicles in the presence of uncertainties. In addition, by means of a model reference control approach, a robust adaptive formation controller is used to steer the vehicles into a formation pattern and have them maintain the formation shape. Numerical simulations demonstrate the effectiveness of the algorithm.


2021 ◽  
pp. 4995-5006
Author(s):  
Jianan Wang ◽  
Changyu Bi ◽  
Dandan Wang ◽  
Chunyan Wang ◽  
Jiayuan Shan

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