Adaptive fuzzy singularity-free finite-time optimal control for nonlinear pure-feedback multiagent systems

Su, Yuanbo; Cao, Liang; Zhou, Xiaoshuai; Pan, Yingnan

doi:10.1016/j.fss.2022.12.004

Cited by 6 publications

(3 citation statements)

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“…where k 1 , 𝛾 1 and 𝜇 1 are design constants, 𝛽 ∈ (0, 1]. Differentiating (32) to time t, and with ( 23), ( 31), ( 33) and (34), we obtain…”

Section: Controller Designmentioning

confidence: 99%

“…Theorem 1. For system (1) under Assumptions 1, 2, and 3, the feed-forward control laws (33), ( 61) and (86), the feed-forward adaptive laws (34), ( 62) and (87), the feedback optimal control laws (48), ( 73) and (96), and the feedback adaptive laws (50), ( 75) and (98), if the initial condition V(0) < P is satisfied, then there are positive constants satisfying the inequality (103), such that all the signals in the closed-loop system are SGFTS and the cost function is minimum, where…”

Section: Stability Analysismentioning

confidence: 99%

“…The FTOC problem was investigated for interconnected non‐strict feedback nonlinear systems with powers of positive odd rational numbers in Reference 33. In Reference 34, the FTC problem was studied for nonlinear pure‐feedback multi‐agent systems, and adaptive fuzzy optimal controllers were designed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finite‐time optimal control for uncertain strict‐feedback nonlinear systems with input saturation and output constraints

Xia,

Li,

Zhang

et al. 2023

Adaptive Control & Signal

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SummaryIn this paper, a finite‐time optimal control (FTOC) strategy is proposed for output constrained uncertain nonlinear systems with input saturation. The controller adopts a feed‐forward and optimal feedback control structure. The second‐order command filter and the auxiliary error compensation system are designed in the feed‐forward controller, which can eliminate the influence of filtering error on system performance while avoiding the singularity problem of finite‐time control. A new critic weight updating law is proposed in the design of the optimal feedback controller, in which a neural network is utilized to approximate the relevant cost function. The control scheme can ensure that all signals in the optimize system are semi‐global practical finite‐time stable (SGPFS), and the cost function is also minimized. The effectiveness of the algorithm is validated through simulation examples.

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“…where k 1 , 𝛾 1 and 𝜇 1 are design constants, 𝛽 ∈ (0, 1]. Differentiating (32) to time t, and with ( 23), ( 31), ( 33) and (34), we obtain…”

Section: Controller Designmentioning

confidence: 99%

Section: Stability Analysismentioning

confidence: 99%