Adaptive finite‐time neural backstepping control for multi‐input and multi‐output state‐constrained nonlinear systems using tangent‐type nonlinear mapping

Abstract

SummaryThis article focuses on the problem of adaptive finite‐time neural backstepping control for multi‐input and multi‐output nonlinear systems with time‐varying full‐state constraints and uncertainties. A tan‐type nonlinear mapping function is first proposed to convert the strict‐feedback system into a new pure‐feedback one without constraints. Neural networks are utilized to cope with unknown functions. To improve learning performance, a composite adaptive law is designed using tracking error and approximate error. A finite‐time convergent differentiator is adopted to avoid the problem of “explosion of complexity.” By theoretical analysis, all the signals of system are proved to be bounded, the outputs can track the desired signals in a finite time, and full‐state constraints are not transgressed. Finally, comparative simulations are offered to confirm the validity of the proposed control scheme.

Adaptive neural output feedback finite‐time command filtered backstepping control for nonlinear systems with full‐state constraints

“…For example, [24][25][26] used the BLF to study the output constrains of HONSs, and the state constraints for HONSs were considered by using BLF in [27,28]. In [29,30], a control method based on DSC with full-state constraints was studied, which not only considers the state constraints but also solves the EOCP, and the adaptive finite-time DSC was considered for nonlinear systems with state constraints in [31]. The CFB method was proposed for HONSs with the state constraints in [32], and [33] further studied the finite-time CFB control method of spacecraft attitude with state constraints.…”

“…The CFB method was proposed for HONSs with the state constraints in [32], and [33] further studied the finite-time CFB control method of spacecraft attitude with state constraints. However, the output feedback is not considered in [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Event‐triggered adaptive output‐feedback control for nonlinear state‐constrained systems using tangent‐type nonlinear mapping

“…Note that, for BLFs‐based and integral BLFs‐based constrained control approaches, the feasibility conditions on virtual signals are needed. In recent years, nonlinear mapping (NM) has received increasing attention [24]. By using NM, the state‐constrained system can be transformed into a new one without constraints, and the feasibility conditions can be removed completely.…”

Finite-Time Admissibility and Controller Design for T-S Fuzzy Stochastic Singular Systems with Distinct Differential Term Matrices

“…T-S fuzzy systems have gained a lot of attentions in the recent years mostly due to fuzzy systems being the omnipotent simulator of nonlinear systems [1]. Meanwhile, the related domestic and foreign research achievements of the T-S fuzzy system are abundant and creative [2][3][4]. Stability is the fundamental characteristic of the system, which is the precondition to make sure the control system can run normally.…”