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Quantized Backstepping Prescribed Performance Fuzzy Control for Multiagent Systems
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AI
DOI：10.1109/TFUZZ.2025.3638134.png)
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摘要
En 中文
This article addresses the tracking control problem for nonlinear multiagent systems by developing a quantitative prescribed performance control framework. Under the backstepping design architecture, all virtual control signals and actual control inputs are constructed using quantized signals at each recursive step. A novel continuous and differentiable quantization function is employed to eliminate discontinuities commonly associated with traditional discrete quantizers, thereby ensuring smooth control transitions and reducing communication load. To strictly guarantee adherence to the prescribed tracking performance, a dynamic performance function incorporating quantized errors is further introduced. Moreover, the proposed scheme exhibits robustness against actuator faults and maintains satisfactory control accuracy in faulty scenarios. The effectiveness and superiority of the proposed approach are validated through comprehensive simulation.
Keyword:
Adaptive control
backstepping technique
fault-tolerant control
prescribed performance control (PPC)
quantized control
期刊
IF:
11.9
论文数:
5.0K
被引数:
2.9W