Neural network-based adaptive fault-tolerant control for strict-feedback nonlinear systems with input dead zone and saturation
Year: 2024
Type: article
Abstract: This study investigates the issue of adaptive fault-tolerant neural control in strict-feedback nonlinear systems. The system is subjected to actuator faults, dead-zone and saturation. To model the unknown functions, radial basis function neural networks (RBFNN) are employed. The proposed approach utilizes a backstepping technique to formulate an adaptive fault-tolerant controller, drawing upon the Lyapunov stability theory and the approximation capabilities of RBFNN. The resultant controller guarantees the boundedness of all signals in the closed-loop system, ensuring precise tracking of the reference signal by the system output with a small, bounded error. Finally, simulation results are provided to illustrate the efficacy of the proposed strategy in addressing actuator faults, dead-zone, and saturation.
Authors: Kharrat, Mohamed, Krichen, Moez, Alhazmi, Hadil, Mercorelli, Paolo
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,700
OpenAlex ID: W7106809088
FWCI:
Cites:
Cited by:
Related to: