Bilateral Cooperative Control of Nonlinear Multiagent Systems With State and Output Quantification

Area of Science:

• Control Theory
• Artificial Intelligence
• Systems Engineering

Background:

• Nonlinear multiagent systems (NMASs) present significant control challenges due to their complex dynamics.
• State estimation and adaptive control are crucial for managing uncertainties in NMASs.
• Quantization of states and outputs can further complicate control design.

Purpose of the Study:

• To develop a fuzzy adaptive state and output quantized bilateral cooperative control strategy for NMASs.
• To address the challenges posed by nonlinearities and unmeasurable states in NMASs.
• To design a robust control system that incorporates quantization effects.

Main Methods:

• Fuzzy logic systems (FLS) are employed to approximate unknown nonlinear functions within the NMAS.
• A fuzzy state observer is designed to estimate unmeasurable states.
• A second-order command filter is utilized to manage the time derivative of virtual control functions.
• A uniform quantizer is integrated for fuzzy adaptive inversion in controller design.

Main Results:

• The proposed fuzzy adaptive control method effectively handles nonlinearities and state quantization in NMASs.
• Simulation experiments demonstrate the successful implementation and performance of the control strategy.
• The bilateral cooperative control objective is achieved under the designed fuzzy adaptive framework.

Conclusions:

• The developed fuzzy adaptive control approach provides a viable solution for quantized bilateral cooperative control of NMASs.
• The integration of fuzzy logic, state observers, command filters, and quantization is effective.
• The study validates the proposed method's efficacy through comprehensive simulations.