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Adaptive PI Control for Consensus of Multiagent Systems With Relative State Saturation Constraints
IEEE Transactions on Cybernetics
|December 17, 2019
Summary
This study introduces adaptive proportional-integral (PI) protocols to achieve consensus in multi-agent systems with relative state saturation constraints. These protocols ensure system stability and preserve network connectivity.
Area of Science:
- Control Theory
- Networked Systems
- Distributed Systems
Background:
- Relative states between connected agents are crucial in multi-agent systems and often face saturation constraints.
- Existing consensus protocols may not effectively handle these relative state saturation constraints.
Purpose of the Study:
- To develop novel adaptive proportional-integral (PI) protocols for achieving consensus under relative state saturation constraints.
- To ensure system stability and preserve network connectivity in the presence of these constraints.
Main Methods:
- Designed adaptive PI protocols incorporating adaptive coupling weights and saturation functions.
- Developed an iterative learning-based heuristic algorithm to find a diagonally dominant positive-definite solution matrix.
- Constructed stringent saturation functions for the special case of a row full rank input matrix.
Main Results:
- Achieved constrained consensus under relative state saturation constraints.
- Demonstrated non-overshoot and shorter settling times for edge states in specific cases.
- Validated the applicability of the results for preserving communication network connectivity.
Conclusions:
- The proposed adaptive PI protocols effectively address consensus problems with relative state saturation constraints.
- The developed algorithms and specific saturation functions enhance system performance and robustness.
- The findings contribute to the stability and connectivity of networked multi-agent systems.
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