Internal model control based on a novel least square support vector machines for MIMO nonlinear discrete systems

Abstract

To improve the robustness of the traditional inverse system method, the internal model control based on a novel least square support vector machines (LS-SVM) is proposed. The novel LS-SVM considers general errors that include noises of input variables and output variables as empirical errors. The data of original MIMO discrete system is exploited to approximate its inverse model by the novel LS-SVM. By cascading the inverse model and the original system to constitute a decoupling pseudo-linear system, the internal model control strategy is carried out to the pseudo-linear system to realize the effective control. Simulation validates that the novel LS-SVM used in the inverse system identification is effective and shows that the internal model control of nonlinear discrete systems has better robustness of anti-interference and parameters varying than that of the open-loop system only based on inverse control.

A High-Precision Fatigue Detecting Method for Air Traffic Controllers Based on Revised Fractal Dimension Feature

“…where m ∈ (1, ∞) is a fuzzy exponent, μ ij denotes the degree that x j belongs to the rule i, μ ij ∈ U, and z i is the ith cluster center. e novel LS-SVM considers general errors that include noises of input variables and output variables as empirical errors [28].…”

Acquisition system and approximation of brain signals

“…The studies of [19, 22-28, 30, 32] use the least squares for the design of some kind of evolving systems. In [29], the authors use the least squares for the training of a support vector machine algorithm. In [31], Phan and Cichocki use the least-squares algorithm for non-negative tensor factorisations.…”

“…The work of [36] improves both convergence and steady-state mean-square error (MSE) performance of the adaptive sparse-interpolated Volterra filter. From the above studies, two interesting interpolation methods are highly considered as are the least squares of [19,[22][23][24][25][26][27][28][29][30][31][32]34], and the nearest neighbour of [20,21,33]. Since this paper is focused in this issue, it would be interesting to design a novel interpolation method for the approximation of non-linear behaviours.…”

“…There are two kinds of signal approximation: one is the estimation of parameters [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], and the other is the interpolation [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”