In This Paper, We Investigate Lifelong Learning (LL)-Based Tracking Control for Partially Uncertain Strict Feedback Nonlinear Systems with State Constraints, employing a Singular Value Decomposition (SVD) of the Multilayer Neural Networks (MNNs) Activation Function based Weight Tuning Scheme. the Novel SVD-Based Approach Extends the MNN Weight Tuning to (Formula Presented.) Layers. a Unique Online LL Method, based on Tracking Error, is Integrated into the MNN Weight Update Laws to Counteract Catastrophic Forgetting. to Adeptly Address Constraints for Safety Assurances, Taking into Account the Effects Caused by Disturbances, We Utilize a Time-Varying Barrier Lyapunov Function (TBLF) that Ensures a Uniformly Ultimately Bounded Closed-Loop System. the Effectiveness of the Proposed Safe LL MNN Approach is Demonstrated through a Leader-Follower Formation Scenario Involving Unknown Kinematics and Dynamics. Supporting Simulation Results of Mobile Robot Formation Control Are Provided, Confirming the Theoretical Findings.


Electrical and Computer Engineering

Second Department

Computer Science

Publication Status

Full Access


Office of Naval Research, Grant N00014‐21‐1‐2232

Keywords and Phrases

adaptive control; formation control; lifelong learning; multilayer neural networks; nonlinear systems; singular value decomposition

International Standard Serial Number (ISSN)

1099-1239; 1049-8923

Document Type

Article - Journal

Document Version


File Type





© 2023 Wiley; International Federation of Automatic Control (IFAC), All rights reserved.

Publication Date

01 Jan 2023