Filter-Based Intelligent Output-Constrained Control of Uncertain MIMO Nonlinear Systems With Sensor and Actuator Faults

This article studies the tracking problem for a class of strict-feedback uncertain multi-input-multi-output (MIMO) nonlinear systems, considering both the output constraints and multiple sensor/actuator faults. A novel control approach, named adaptive-neural-backstepping fault-tolerant constrained (ANBFTC) algorithm, is proposed, which incorporates the dynamic surface analysis into the iterative design. A filter-based adaptation coordinate transformation (FBACT) is introduced to define new backstepping iteration variables, eliminating the need for fault amplitudes and bias information. To further address the nonlinear uncertainties inherent in the system, we employ a learning approach, specifically utilizing radial basis function neural networks (RBFNNs), to approximate the uncertainty dynamics. This methodology not only mitigates the computational challenges typically associated with high-order derivatives in iterative designs but also ensures the convergence of tracking errors while adhering to output constraints, even in the presence of multiple sensor/actuator faults. Finally, numerical simulation results are presented to demonstrate the feasibility of the ANBFTC approach.