Adaptive Neural Backstepping Control for Uncertain MIMO Systems With Sensor/Actuator Faults and Asymmetric Time-Interval Output Constraints

TY - JOUR

T1 - Adaptive Neural Backstepping Control for Uncertain MIMO Systems With Sensor/Actuator Faults and Asymmetric Time-Interval Output Constraints

AU - Zhou, Ning

AU - Zhao, Canyang

AU - Cheng, Xiaodong

AU - Xia, Yuanqing

AU - Li, Tiejun

AU - Wang, Baohao

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2026

Y1 - 2026

N2 - This article deals with a class of strict-feedback multi-input–multi-output (MIMO) nonlinear systems, which are characterized by model uncertainties, unknown control coefficient matrices, nonvanishing external disturbances, and multiple unknown time-varying faults in both sensors and actuators. To achieve accurate reference trajectory tracking while fulfilling fixed time-interval asymmetric output constraints (FTAOCs), a novel filter-backstepping adaptive-neural-fault-tolerant constrained (FBAFC) algorithm is developed based on only partial information from faulty sensors. The controller integrates radial basis function neural networks (RBFNNs) to approximate unknown nonlinearities and compensate for compound uncertainties. A coordinate transformation is introduced for iterative backstepping design, and a new barrier Lyapunov function (BLF) with a shift function is derived in three phases. The convergence of tracking errors is guaranteed theoretically, even in the presence of multiple actuator faults. Finally, simulation results are presented in two scenarios to validate the proposed method.

AB - This article deals with a class of strict-feedback multi-input–multi-output (MIMO) nonlinear systems, which are characterized by model uncertainties, unknown control coefficient matrices, nonvanishing external disturbances, and multiple unknown time-varying faults in both sensors and actuators. To achieve accurate reference trajectory tracking while fulfilling fixed time-interval asymmetric output constraints (FTAOCs), a novel filter-backstepping adaptive-neural-fault-tolerant constrained (FBAFC) algorithm is developed based on only partial information from faulty sensors. The controller integrates radial basis function neural networks (RBFNNs) to approximate unknown nonlinearities and compensate for compound uncertainties. A coordinate transformation is introduced for iterative backstepping design, and a new barrier Lyapunov function (BLF) with a shift function is derived in three phases. The convergence of tracking errors is guaranteed theoretically, even in the presence of multiple actuator faults. Finally, simulation results are presented in two scenarios to validate the proposed method.

KW - Adaptation mechanism

KW - filter-backstepping adaptive neural constraint control

KW - multiple sensor and actuator faults

KW - strict-feedback nonlinear system

KW - time-interval asymmetric constraints

KW - unknown control gain matrix

UR - https://www.scopus.com/pages/publications/105027959715

U2 - 10.1109/TSMC.2025.3648683

DO - 10.1109/TSMC.2025.3648683

M3 - Article

AN - SCOPUS:105027959715

SN - 2168-2216

VL - 56

SP - 1761

EP - 1775

JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

IS - 3

ER -