Adaptive fuzzy iterative learning fault‐tolerant control for uncertain nonstrict feedback systems

Abstract Considering the main challenges of nonrepetitive initial conditions and the actuator faults, this article proposes an adaptive iterative learning fault‐tolerant control (AILFTC) for high‐order nonstrict feedback nonlinear systems. The actuator faults are compensated by designing an iterative updating law to estimate its effective factor. Although the initial values are different for each iteration, an error tracking algorithm is proposed to ensure that the output converges to the desired trajectory within the initial time interval. By introducing fuzzy logic systems (FLSs) to address the strong nonlinearity of the system and further utilizing the Gaussian function property of the FLS, the nonstrict feedback problem of the system is solved. Stability and convergence are demonstrated by the proposed AILFTC through the design of a composite energy function. Simulation study verifies the results.