Composite adaptive exponential tracking control for large-scale nonlinear systems with sensor faults.

The issue of composite adaptive exponential tracking control for a class of large-scale nonlinear systems under model uncertainties, external disturbances, along with multiplicative and additive time-varying sensor faults is considered in this paper. The command filtered backstepping approach is employed to address the "explosion of terms" issue inherent in standard backstepping method. A novel compensating system is incorporated to mitigate the effects of filtering errors and enhance the convergence of tracking errors. The composite estimation laws are designed by integrating the compensated tracking errors, prediction errors stemming from output estimators, along with the proportional and integral estimation errors of faulty terms. This on-line estimation framework enables achieving fast, robust and accurate estimation, even when employing low learning and modification gains. By incorporating modification terms with appropriate time-varying gains, it is demonstrated that the resulting system is globally exponentially stable. Finally, the effectiveness of the presented FTC approach is illustrated through two simulation examples. • A composite adaptive FTC strategy is developed under time-varying sensor faults. • The proposed method incorporates composite estimation laws into the CFB framework. • A novel compensating system is utilized to mitigate the effects of filtering errors. • The on-line estimation architecture enables fast, robust and accurate estimation. • The closed-loop system is rigorously proven to be globally exponentially stable. [ABSTRACT FROM AUTHOR]