Extended‐state‐observer‐based dynamic surface control of flexible‐joint robot systems with input saturation

Abstract

This article presents an extended-state-observer-based dynamic surface control approach for flexible-joint robot systems with asymmetric input saturation and large unknown dynamic knowledge. Traditional controllers for flexible-joint robot systems usually use approximation technology to deal with unknown dynamics knowledge. Unlike the traditional control algorithm, this article utilizes an extended state observer to estimate the unknown dynamics.For the closed-loop system, the delay strategy handles the time-scale separation issue, the filtering system overcomes the "explosion of differentiation" caused by the repeated differentiation of auxiliary control signals, and the mean-value-theorem solves the input saturation problem of the actuator. The stability analysis implies that estimation errors of extended state observers (ESOs) and other state variables are semiglobally uniformly ultimately bounded.Compared with fuzzy control algorithms, the novel ESO-based dynamic surface control approach not only omits online learning time but also uses only a few control parameters to obtain satisfactory tracking performance. Finally, a comparison simulation experiment is provided to illustrate the effectiveness of the gained conclusions.

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

“…However, because of using backstepping approach, explosion of terms is obtained in the control input; furthermore, some of them use deterministic model for dead-zone instead of the fuzzy and uncertain, and they require up to the nth order derivatives of the reference input for controller design. In contrast, CDSC-based schemes in References [13,15,16,[26][27][28][29][30][31]42] eliminate the explosion of terms problem by introducing the first-order filters to obtain derivative of the virtual inputs. However, output response of the CDSC-based approaches is highly sensitive to the time-constant of the first-order filters.…”

“…After that, RBFNN observer‐based output feedback control algorithm is designed. Therefore, because of eliminating full state measurement assumption, the proposed scheme is more suitable for practical systems. Almost all of the existing works, for instance, References [13, 15, 16, 26–31, 42], use CDSC approach to avoid the “explosion of complexity” problem. The CDSC approach uses first‐order filter to obtain derivative of the virtual control inputs.…”

“…Many of the existing control schemes 3,[14][15][16][24][25][26][27][28][29][30][31] are based on the backstepping or CDSC approaches. However, backstepping-based schemes 3,[14][15][16]24,25 have two main disadvantages: (i) they suffer from the "explosion of complexity" problem which appears because of repeatedly differentiating nonlinear functions in the controller design, and (ii) they require up to the nth order derivatives of the reference trajectory in the design procedure.…”

“…However, backstepping-based schemes 3,[14][15][16]24,25 have two main disadvantages: (i) they suffer from the "explosion of complexity" problem which appears because of repeatedly differentiating nonlinear functions in the controller design, and (ii) they require up to the nth order derivatives of the reference trajectory in the design procedure. In contrast, CDSC-based schemes 15,16,[26][27][28][29][30][31] eliminate the "explosion of complexity" problem by introducing the first-order filters to obtain derivative of the virtual inputs. In addition, they require only the reference trajectory and its first and second derivatives.…”

“…2. Almost all of the existing works, for instance, References [13,15,16,[26][27][28][29][30][31]42], use CDSC approach to avoid the "explosion of complexity" problem. The CDSC approach uses first-order filter to obtain derivative of the virtual control inputs.…”