Finite‐time adaptive control for nonlinear systems with uncertain parameters based on the command filters

Abstract

The trajectory tracking control problem for a class of nonlinear systems with uncertain parameters is considered in this article. A new adaptive finite-time tracking control is designed based on the adaptive backstepping method via the command filters. The command filter mechanism can avoid the calculation of partial derivatives and solve the "explosion of complexity" in the backstepping design. The compensation signals are introduced to eliminate errors produced by the command filters. The proposed adaptive backstepping control can guarantee the tracking error remains in a small neighborhood of the origin in finite time, while the practical finite-time stability of the control systems with uncertain parameters is proven by the stability criterion. The effectiveness of the proposed scheme is verified by some simulation results.

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

“…The simulation results demonstrate the effectiveness of the proposed algorithms and show that the F-MI-RGLS algorithm can obtain higher parameter estimation accuracy and provide reliable model predictions. The proposed approaches in the article can combine other identification methods [105][106][107][108][109][110][111][112] to study the parameter estimation issues of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises [113][114][115][116][117][118][119][120] and can be applied to literatures [121][122][123][124][125][126][127][128] such as paper-making systems, information processing, engineering systems, and so on.…”

Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector

“…The simulation examples show the merits and effectiveness of the proposed algorithms. In addition, The proposed approaches in the article can combine other mathematical tools and statistical strategies and identification methods [89][90][91][92][93][94] to study the parameter estimation issues of other linear stochastic systems and nonlinear stochastic systems with different structures and disturbance noises and can be applied to literatures [95][96][97][98][99][100][101][102] such as paper-making systems, information processing, transportation communication systems [103][104][105][106][107][108][109][110][111] and so on.…”

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

“…The proposed parameter estimation algorithms in this article are based on this identification model. Many identification methods are derived based on the identification models of the systems [37][38][39][40] and these methods can be used to estimate the parameters of other linear systems and nonlinear systems [41][42][43][44] and can be applied to other fields [45][46][47][48][49][50] such as chemical process control systems. There exists the product of the parameter vector b of the CAR model in the forward channel and c of the nonlinear block in the feedback channel in (7) such that the identification model is a typical bilinear-in-parameter model.…”