Leader-Follower Formation Learning Control of Discrete-Time Nonlinear Multiagent Systems

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Distributed Systems

Background:

• Multiagent systems (MASs) require sophisticated control for coordinated tasks like formation keeping.
• Leader-follower architectures are common but achieving robust and adaptive control remains challenging.
• Existing methods often lack efficient knowledge reuse for performance enhancement.

Purpose of the Study:

• To develop a novel formation learning control (FLC) strategy for discrete-time strict-feedback MASs.
• To enable followers to predict and track the leader's future states using learned experience.
• To improve transient control performance through knowledge acquisition and reuse.

Main Methods:

• A two-layer control scheme combining adaptive distributed observers and i_n-step predictors for leader state prediction.
• Adaptive neural network (NN) controllers designed for followers to track predicted leader outputs.
• Development of specific learning rules to acquire and store NN weights as experience knowledge.

Main Results:

• The proposed FLC method effectively solves the leader-follower formation control problem.
• NN weights were proven to converge exponentially to optimal values using an extended stability corollary.
• Simulations demonstrated improved transient control performance compared to non-learning methods.

Conclusions:

• The presented FLC scheme offers a viable approach for adaptive formation control in MASs.
• Reusing learned knowledge significantly enhances control performance and system adaptability.
• The method provides a robust framework for future research in intelligent multiagent control.