Neural Network Dynamic Surface Backstepping Control for the Speed and Tension System of Reversible Cold Strip Rolling Mill

Abstract

To weaken the influences of uncertainties and system coupling items on the coordinated tracking control performance of the speed and tension system of a reversible cold strip rolling mill, a control strategy is proposed based on nonlinear disturbance observers (NDOs), dynamic surface backstepping control, and neural network adaptive approximation. First, the transformation form of the system model is given, and then NDOs are developed to counteract the unmatched uncertainties. Next, controllers for the speed and tension system are presented by combining backstepping with dynamic surface control. Again, the neural network adaptive method is used to approximate the matched uncertainties of the system, and the approximation values are introduced into the designed controllers for compensation. Finally, simulation research is carried out on the speed and tension system of a 1422 mm reversible cold strip rolling mill by using the actual data, and the results show the validity of the proposed control strategy in comparison with the decentralized overlapping control strategy.

Adaptive Nonsingular Terminal Sliding Mode Backstepping Control for the Speed and Tension System of the Reversible Cold Strip Rolling Mill Using Disturbance Observers

“…1. According to related rolling theory and the dynamic equations of the DC motor, the mathematic model for the speed and tension system of the reversible cold strip rolling mill is described as follows [23]:…”

“…And the adaptive laws can be designed as 23 , ξ 34 , ψ 34 ∈ R + are the adaptive control parameters, and the estimation errors can be defined asρ 14 = ρ 14 −ρ 14 ,ρ 23 = ρ 23 −ρ 23 ,ρ 34 = ρ 34 −ρ 34 , andW i = W i −Ŵ i .…”

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

“…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.…”

Decoupling control for wastewater treatment process based on recurrent fuzzy neural network

“…Recently, due to universal nonlinear approximation capability, neural networks have been widely used to identify and control nonlinear systems . In , a neural network on‐line modeling and controlling method (NNOMC) is proposed for multi‐variable control of WWTP.…”