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This brief constructs the adaptive backstepping control scheme for a class ofpure-feedback systems with input delay and full state constraints. With thehelp of Mean Value Theorem, the pure-feedback system is transformed intostrict-feedback one. Barrier Lyapunov functions are employed to guarantee allof the states remain constrained within predefined sets. By introducing thePade approximation method and corresponding intermediate, the impact generatedby input delay on the output tracking performance of the system can beeliminated. Furthermore, a low-pass filter driven by a newly-defined controlinput, is employed to generate the actual control input, which facilitates thedesign of backstepping control. To approximate the unknown functions with adesired level of accuracy, the fuzzy logic systems (FLSs) are utilized bychoosing appropriate fuzzy rules, logics and so on. The minimal learningparameter (MLP) technique is employed to decrease the number of nodes andparameters in FLSs, and dynamic surface control (DSC) technique is leveraged toavoid so-called "explosion of complexity". Moreover, smooth robust compensatorsare introduced to circumvent the influences of external disturbance andapproximation errors. By stability analysis, it is proved that all of signalsin the closed-loop system are semi-globally ultimately uniform bounded, and thetracking error can be within a arbitrary small neighbor of origin via selectingappropriate parameters of controllers. Finally, the results of numericalillustration are provided to demonstrate the effectiveness of the designedmethod.

Adaptive Fuzzy Tracking Control for Nonlinear State Constrained Pure-Feedback Systems With Input Delay via Dynamic Surface Technique