2020
DOI: 10.1109/access.2020.3030282
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Adaptive Fuzzy Finite-Time Tracking Control of Uncertain Non-Affine Multi-Agent Systems With Input Quantization

Abstract: In this paper, the finite-time tracking control problem of a class of multi-agent systems with nonaffine functions and uncertain nonlinearity is investigated, which is different from the existing on high-order multi-agent systems with pure feedback forms. The multi-agent systems considered in this paper, moreover, the nonaffine functions and uncertain nonlinearities are completely unknown, and the input of each follower agent is quantized through a hysteresis quantizer. Based on the help of the fuzzy logic sys… Show more

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Cited by 12 publications
(10 citation statements)
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“…{x Ni (0), d W i } for a givenȳ W i . We note that the residual r i (k) is included in the defined output vectorȳ i (k) according to equation (7). Therefore, in this subsection, we recursively determine the residuals inȳ W i to guarantee positive J W * i , which satisfies the H ∞ performance index (3) under the minimum condition.…”
Section: Distributed Residual-generatormentioning
confidence: 99%
See 1 more Smart Citation
“…{x Ni (0), d W i } for a givenȳ W i . We note that the residual r i (k) is included in the defined output vectorȳ i (k) according to equation (7). Therefore, in this subsection, we recursively determine the residuals inȳ W i to guarantee positive J W * i , which satisfies the H ∞ performance index (3) under the minimum condition.…”
Section: Distributed Residual-generatormentioning
confidence: 99%
“…Accordingly, multi-agent systems (MASs) have received considerable attention due to their characteristics of autonomy [1], [2], distribution [3], [4] and coordination [5], [6]. Control problems of MASs have been extensively studied such as adaptive control [7], [8] and event-triggered control [9]- [11], and many results have been obtained for communication delay [12], [13]. Moreover, the fault-tolerant control problems for MASs has been recently studied by many researchers [14]- [16] because MASs are vulnerable to faults due to their structural complexity.…”
Section: Introductionmentioning
confidence: 99%
“…Techniques such as fuzzy systems allow obtaining a very close approximation of their response without an established mathematical model [8], [9]. Since the mathematical model is a necessity for the design of control strategies, fuzzy logic presents multiple examples of its adaptation to avoid this requirement [10]- [12]. The applications range from control of robotic arms [13], control of the speed of motors [14], including the case previously referred to for speed control of a wind turbine [15].…”
Section: Introductionmentioning
confidence: 99%
“…Neural networks and fuzzy logic systems have been employed to compensate for unknown nonparametric uncertainties in approximation‐based adaptive containment control schemes of nonlinear multi‐agent systems in strict‐feedback 30‐33 and pure‐feedback forms 34,35 . To increase the efficiency of signal transmission under capacity‐limited networks, quantization‐based distributed cooperative control problems have been investigated for uncertain nonlinear multi‐agent systems 36‐41 . These results have been extended to lower‐triangular nonlinear multi‐agent systems.…”
Section: Introductionmentioning
confidence: 99%