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.
I. A. Ganie and S. Jagannathan, "Lifelong Learning-Based Multilayer Neural Network Control of Nonlinear Continuous-Time Strict-Feedback Systems," International Journal of Robust and Nonlinear Control, Wiley; International Federation of Automatic Control (IFAC), Jan 2023.
The definitive version is available at https://doi.org/10.1002/rnc.7039
Electrical and Computer Engineering
Keywords and Phrases
adaptive control; formation control; lifelong learning; multilayer neural networks; nonlinear systems; singular value decomposition
International Standard Serial Number (ISSN)
Article - Journal
© 2023 Wiley; International Federation of Automatic Control (IFAC), All rights reserved.
01 Jan 2023