Identification Recurrent Type 2 Fuzzy Wavelet Neural Network and L2‐Gain Adaptive Variable Sliding Mode Robust Control of Electro‐Hydraulic Servo System (EHSS)

Abstract

An electro-hydraulic servo system (EHSS) is a kind of system with the characteristics of time-variant, serious nonlinearity, parameter and structural uncertainty, and uncertain load disturbance in most cases. These characteristics make it very difficult to realize highly accurate control by conventional methods. In order to solve the above problems, this paper introduces a recurrent type 2 fuzzy wavelet neural network to approximate the unknown nonlinear functions of the dynamic systems through tuning by the desired adaptive law. Based on the identification by recurrent type 2 fuzzy wavelet neural network, a L2 gain design method, combining gain adaptive variable sliding mode control with H infinity control, is proposed for load disturbance, thereby accommodating uncertainties that are the main factors affecting system stability and accuracy in EHSS. In this algorithm, a recurrent type 2 fuzzy wavelet neural network is employed to evaluate the unknown dynamic characteristics of the system and gain adaptive variable sliding mode control to compensate for evaluating errors, and H infinity control to suppress the effect on system by load disturbance. The experiment results show that the proposed system L2 gain design method can make the system exhibit strong robustness to parameter variation and load disturbance.Key Words: Electro-hydraulic servo system (EHSS) nonlinear system, recurrent type 2 fuzzy wavelet neural network, L2 gain adaptive variable sliding mode control, robust control.

Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators

“…The sign(.) function in the discontinuous control term of the NN-SMC defined in (27) and the NT-SMC defined in (19) leads to the chattering problem. In order to eliminate the chattering phenomenon, we consider the following techniques.…”

“…In this Case, the results of circular trajectory tracking control are given while the initial position A 0 of the endeffector O(x, y) lies on the reference circular, the robot model is absolutely accurate, and no model uncertainty is added into the system. The results of circular trajectory tracking of the proposed controller defined in (52) are compared with NT-SMC algorithms under the control law defined in (19), NN-SMC algorithms under the control law defined in (27), and NTC algorithms under the control law defined in (63). The tracking performances of the endeffector for the redundant parallel are given in Fig.…”

“…Recently, computational intelligence related approaches have been widely employed for control, such as fuzzy systems [16][17][18][19][20][21], neural networks [22][23][24][25], neurofuzzy systems [26,27], or radial basis function neural networks (in short, RBFN or RBFNN) [28][29][30]. Some researchers have tried to employ these adaptive networks in sliding mode controllers.…”

An Extended State Observer Based Fractional Order Sliding‐Mode Control for a Novel Electro‐Hydraulic Servo System with Iso‐Actuation Balancing and Positioning

“…The sliding mode control (SMC) has been widely adopted as a variable structure control with no sensitivity to parameter variations and external disturbances; and is not even dependent on accurate system models. Currently, SMC has become a hot topic in the field of nonlinear control . In order to further improve a system's fault tolerance as well as to weaken the inherent chattering phenomenon the SMC, fuzzy reasoning (FR), artificial neural networks (ANN), and higher‐order adaptive sliding mode controls, have been integrated into the SMC to achieve better performances .…”

Super‐Twisting Observer‐Based Integral Sliding Mode Control for Tracking the Rapid Acceleration of a Piston in a Hybrid Electro‐Hydraulic and Pneumatic System

“…Various methods have been devised and successfully applied to deal with this seriously complicated problem of control. Numerous studies for improving control quality such as sliding mode control [19], adaptive nonlinear control [12,13,20], and intelligent control [21,22,31,55] have been proposed. Sliding mode control (SMC) [36,37,[60][61][62][63][64][65][66][67][68] has reliable robustness like other robust controllers [38][39][40][41][42] with ability of reduce the impacts of disturbance, so that it can be applied for a wide range of complex systems [43].…”