Distributed Multi-Agent Control for Unknown Leader Dynamics
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Solution Overview
Problem
Heterogeneous multi-agent systems with different dynamics and dimensions pose challenges in distributed control, particularly in cooperative output regulation, where existing methods are not robust to plant uncertainties and require precise knowledge of the exosystem's dynamics, which is often unknown in practical applications.
Innovation Solution
A new definition of the linear cooperative output regulation problem is introduced, allowing for internal model-based distributed dynamic state feedback and output feedback control laws, which do not require decomposition of matrix equations and are applicable even when the exosystem's dynamics are unknown, ensuring uniform ultimate boundedness of tracking errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feedforward design methodology is used for cooperative output regulation, then the control approach can be implemented, but it is not robust to plant uncertainties
Solution Approach 1:
The patent employs feedback mechanisms where each agent incorporates an internal model of the exosystem dynamics and uses output feedback control laws. The controller uses the output tracking error and internal model states to adjust control inputs, creating a closed-loop system that is robust to plant uncertainties while maintaining coordinated output regulation across heterogeneous multi-agent systems.
2Reliability
If internal model principle is used for cooperative output regulation, then robustness to small variations of plant parameters is achieved, but it cannot be applied when the transmission zero condition does not hold
Solution Approach 1:
The patent changes the fundamental approach from relying on transmission zero conditions to using internal model principles with output feedback control. By incorporating the exosystem dynamics directly into each agent's controller through internal models, the system achieves robustness to parameter variations without requiring the transmission zero condition, thus expanding applicability to broader classes of heterogeneous multi-agent systems.
3Measurement precision
If exosystem dynamics are precisely known for cooperative output regulation, then traditional control methods can be applied, but it is challenging to precisely know the system matrix in practical applications
Solution Approach 1:
Each agent in the multi-agent system incorporates its own internal model of the exosystem dynamics, allowing it to autonomously generate the necessary control actions without requiring precise global knowledge of the exosystem matrix. The internal model enables each agent to self-regulate its output based on local measurements and internal dynamics, making the system easier to implement in practical applications where exosystem dynamics are unknown.
4Device complexity
If decomposition of matrix equations is used to prove existence of unique solution, then the problem can be solved for each agent separately, but it increases the complexity of the solution process
Solution Approach 1:
The patent segments the overall control problem into independent subproblems for each agent by using decentralized control laws. Each agent solves its own regulator equations and designs its internal model based on local information and communication with neighbors, avoiding the need to solve large-scale coupled matrix equations centrally. This segmentation reduces both computational complexity and solution time while ensuring existence and uniqueness of solutions for each agent.
Data Source
AI summary
Systems and methods for controlling motion of a vehicle in a group of vehicles. In one embodiment, the system includes a communication interface, a vehicle platform for travelling among the group of vehicles, and an electronic processor. The electronic processor is configured to determine a local virtual tracking error signal and a controller state signal. The electronic processor is also configured to determine a self-navigation input control signal based on the local virtual tracking error signal and the controller state signal. The self-navigation input control signal is for navigating the vehicle platform. A trajectory of an exosystem is based on a boundedness condition. The vehicle communicates with other vehicles in the group of vehicles via a fixed augmented directed connected communication graph topology. Each vehicle in the group of vehicles is stabilizable and satisfies a transmission zero condition. Design matrices of the vehicle satisfy an internal model principle.