This Paper Presents a Novel Lifelong Multilayer Neural Network (MNN) Tracking Approach for an Uncertain Nonlinear Continuous-Time Strict Feedback System that is Subject to Time-Varying State Constraints. the Proposed Method Uses a Time-Varying Barrier Function to Accommodate the Constraints Leading to the Development of an Efficient Control Scheme. the Unknown Dynamics Are Approximated using a MNN, with Weights Tuned using a Singular Value Decomposition (SVD)-Based Technique. an Online Lifelong Learning (LL) based Elastic Weight Consolidation (EWC) Scheme is Also Incorporated to Alleviate the Issue of Catastrophic Forgetting. the Stability of the overall Closed-Loop System is Analyzed using Lyapunov Analysis. the Effectiveness of the Proposed Method is Demonstrated by using a Quadratic Cost Function through a Numerical Example of Mobile Robot Control Which Demonstrates a 38% Total Cost Reduction When Compared to the Recent Literature and 6% Cost Reduction is Observed When the Proposed Method with LL is Compared to the Proposed Method Without LL.
I. Ganie and S. Jagannathan, "Lifelong Learning Control of Nonlinear Systems with Constraints using Multilayer Neural Networks with Application to Mobile Robot Tracking," 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, pp. 727 - 732, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/CCTA54093.2023.10252103
Electrical and Computer Engineering
Keywords and Phrases
Lifelong learning; Multilayer neural networks; Singular value decomposition; Time-varying barrier functions
Article - Conference proceedings
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01 Jan 2023