Neuro-adaptive command filter control of stochastic time-delayed nonstrict-feedback systems with unknown input saturation

Direct Neural Network Adaptive Tracking Control for Uncertain Non-Strict Feedback Systems With Nonsymmetric Dead-Zone

“…There are a lot of significant results regarding adaptive fuzzy or NN control or FNN control algorithms for nonlinear systems with unknown dead-zones. However, many of these approximation control methods go through updating the estimations of each optimal parameter of FLSs [7][8][9][10][11], [21][22][23][24] NN, FNN directly, resulting the heavy online computation burden due to the rules of fuzzy, the hidden nodes of NN, or FNN are rather large generally. In this paper, Assumption 5 relaxes the conditions that the approximation errors or external disturbance are bounded with only unknown constants rather than known constants or satisfying square integrable condition.…”

“…Remark 6. Compared with many approximation control approaches, which involve updating the estimations of each optimal parameter of FLSs NN, and FNN directly [2][3][4][6][7][8][9][10][11]20,23,[30][31][32], due to the hidden nodes of NN, or FNN and the rules of fuzzy are rather large generally, which result in the heavy online computation burden. Based on Assumption 5, at each design procedure for each system in this paper, fewer parameters need to be adjusted, we only need to approximate the unknown constant for the norm of the optimal parameter.…”

“…Recently, many adaptive researches and control strategies based on backstepping techniques and approximation of fuzzy or NN have been proposed for non-strict feedback uncertain nonlinear systems [18][19][20][21][22][23][24]. Combing with input saturation and output constraint, [18] discussed fuzzy control for non-strict feedback systems.…”

“…Based on finite-time adaptive control approaches, [21,22] analyzed fuzzy states feedback control and output feedback dynamic surface control for non-strict feedback respectively. [23] extended NN adaptive command filter control to stochastic time-delayed systems with unknown input saturation. Neural control methods for full-state constraints and unmodeled dynamics in non-strict feedback uncertain systems are designed [24].…”

“…1) This established control scheme can compensate nonsymmetric dead-zone inputs, uncertainties and solve the problems of included non-affine structure states non-strict feedback simultaneously. Although the previous results in [18][19][20][21][22][23][24] also studied the same control design problem for non-strict feedback nonlinear systems, they do not consider uncertain non-symmetric dead-zones and have computing burden problem.…”