A nonlinear uncertain multi-robot distributed optimization safety formation control method

By using dynamic event-triggered communication and adaptive approximators, the problems of communication waste and obstacle avoidance in multi-arm robotic systems are solved, achieving high-precision formation control and safety constraints for robotic arms. This addresses the issues of communication waste, inadequate obstacle avoidance, and physical limitations in existing technologies.

CN122353592APending Publication Date: 2026-07-10NORTHEASTERN UNIV CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-13
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing multi-robotic arm collaborative systems suffer from problems such as wasted bandwidth due to limited communication bandwidth and nonlinear uncertainties, network congestion, inadequate obstacle avoidance, easy collision of robotic arms, and asymptotically stable tracking errors while ignoring physical constraints.

Method used

The design incorporates a dynamic event-triggered communication mechanism and dynamic variable triggering conditions. By combining source-finding gradients and adaptive approximators, a safe virtual control signal is generated through a quadratic programming problem to ensure that the robotic arm avoids collisions and compensates for uncertainties online during formation control.

Benefits of technology

It achieves high-precision formation control of multi-robotic arm systems in complex environments, avoids collisions, ensures that the state of the robotic arms is within the physical boundaries, reduces communication burden, and improves the safety and stability of the system.

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Abstract

This invention discloses a distributed optimization and safe formation control method for multiple robotic arms with nonlinear uncertainty. By establishing a dynamic model of the multi-robotic arm system with nonlinear uncertainty, a dynamic event triggering method with internal system information is constructed. Each robotic arm broadcasts its position information to its neighboring nodes only when the triggering condition is met. A predetermined reference trajectory is generated for each robotic arm system, converging to the global optimum and satisfying formation constraints. The position tracking error between the actual output of the robotic arm system and the predetermined reference trajectory at each moment is calculated. The virtual control law designed in the first step of the back-reasoning is used as the target to be optimized. A quadratic programming problem is constructed by combining the safety constraint boundary inequality built with the control obstacle function to obtain the safe virtual control law. In the second step of the back-reasoning, the velocity tracking error between the actual state of the robotic arm and the safe virtual control signal is calculated, constructing a back-reasoning controller based on the obstacle Lyapunov function. This invention overcomes the control instability risk caused by strong nonlinearity and achieves high-precision trajectory tracking.
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