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Prescribed Performance Tracking Control for Nonlinear Multiagent Systems With Distributed Observation Errors Compensation
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DOI：10.1109/TASE.2024.3417387.png)
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Abstract
En 中文
This paper investigates the prescribed performance formation tracking control problem for nonlinear multiagent systems (MASs) under state constraints. Compared with the existing results using predefined-time distributed observers, an observation error compensation term is designed to prevent the tracking error caused by the estimation error from violating the prescribed performance. Different from the existing related literature, this paper introduces a novel approach by combining a state-dependent transformation method with a time-varying constraint-based prescribed performance control (PPC) method. This approach effectively eliminates the feasibility conditions of state constraints and the initial condition dependence restriction of PPC. Moreover, by designing an appropriate PPC-related time-vary function, the proposed method can accurately map the preset control performance before and after the transformation process. Therefore, the loss of state feasible sets in the existing relevant results can be tackled. The simulation example verifies the feasibility of the control method. Note to Practitioners-This paper studies the prescribed performance formation tracking control problem for nonlinear MASs, which has application value in autonomous driving, smart factories, environmental exploration, unmanned delivery and other fields. By using a state-dependent transformation method and a time-varying function, a PPC strategy, under state constraints, is devised for MASs formation. Moreover, a compensation term is designed to handle the tracking errors caused by the observation errors before the predefined observation time.
Keywords:
Observers
Task analysis
Automation
Multi-agent systems
Estimation error
Error compensation
Topology
Multiagent systems
time-varying formation
distributed observer
prescribed performance control
state constraints
Journal
IF:
6.4
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4.9K
Citations:
1.6W