Combined control strategy using internal model control and adaptive inverse control for electro-hydraulic shaking table

2012 ◽  
Vol 227 (10) ◽  
pp. 2348-2360 ◽  
Author(s):  
Gang Shen ◽  
Zhencai Zhu ◽  
Yu Tang ◽  
Lei Zhang ◽  
Guangda Liu ◽  
...  
Keyword(s):  
Transfer Function ◽  
Control Strategy ◽  
Internal Model ◽  
Internal Model Control ◽  
Shaking Table ◽  
Tracking Accuracy ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Model Control ◽  
Combined Control

An electro-hydraulic shaking table is a useful experimental apparatus to real-time replicate the desired acceleration signal for evaluating the performance of the tested structural systems. The article proposes a combined control strategy to improve the tracking accuracy of the electro-hydraulic shaking table. First, the combined control strategy utilizes an adaptive inverse control as a feedforward controller for extending the acceleration frequency bandwidth of the electro-hydraulic shaking table when the estimated plant model may be a nonminimum phase system and its inverse model is an unstable system. The adaptive inverse control feedforward compensator guarantees the stability of the estimated inverse transfer function. Then, the combined control strategy employs an improved internal model control for obtaining high fidelity tracking accuracy after the modeling error between the estimated inverse transfer function using adaptive inverse control and the electro-hydraulic shaking table actual inverse system is improved by the improved internal model control. So, the proposed control strategy combines the merits of adaptive inverse control feedforward compensator and improved internal model control. The combined strategy is programmed in MATLAB/Simulink, and then is compiled to a real-time PC system with xPC target technology for implementation. The experimental results demonstrate that a better tracking performance with the proposed combined control strategy is achieved in an electro-hydraulic shaking table than with a conventional controller.

scholarly journals New Smith Internal Model Control of Two-Motor Drive System Based on Neural Network Generalized Inverse

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Guohai Liu ◽  
Jun Yuan ◽  
Wenxiang Zhao ◽  
Yaojie Mi
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Internal Model ◽  
Generalized Inverse ◽  
Control Structure ◽  
Control Method ◽  
Internal Model Control ◽  
Drive System ◽  
Motor Drive ◽  
Model Control

Multimotor drive system is widely applied in industrial control system. Considering the characteristics of multi-input multioutput, nonlinear, strong-coupling, and time-varying delay in two-motor drive systems, this paper proposes a new Smith internal model (SIM) control method, which is based on neural network generalized inverse (NNGI). This control strategy adopts the NNGI system to settle the decoupling issue and utilizes the SIM control structure to solve the delay problem. The NNGI method can decouple the original system into several composite pseudolinear subsystems and also complete the pole-zero allocation of subsystems. Furthermore, based on the precise model of pseudolinear system, the proposed SIM control structure is used to compensate the network delay and enhance the interference resisting the ability of the whole system. Both simulation and experimental results are given, verifying that the proposed control strategy can effectively solve the decoupling problem and exhibits the strong robustness to load impact disturbance at various operations.

scholarly journals Fuzzy Feedback Linearizing Controller and Its Equivalence With the Fuzzy Nonlinear Internal Model Control Structure

2007 ◽  
Vol 17 (2) ◽  
pp. 233-248 ◽  
Author(s):  
Reda Boukezzoula ◽  
Sylvie Galichet ◽  
Laurent Foulloy
Keyword(s):  
Internal Model ◽  
Feedback Linearization ◽  
Control Structure ◽  
Fuzzy Controller ◽  
Internal Model Control ◽  
Smith Predictor ◽  
Control Structures ◽  
Inverse Control ◽  
Model Control ◽  
Takagi Sugeno

Fuzzy Feedback Linearizing Controller and Its Equivalence With the Fuzzy Nonlinear Internal Model Control StructureThis paper examines the inverse control problem of nonlinear systems with stable dynamics using a fuzzy modeling approach. Indeed, based on the ability of fuzzy systems to approximate any nonlinear mapping, the nonlinear system is represented by a Takagi-Sugeno (TS) fuzzy system, which is then inverted for designing a fuzzy controller. As an application of the proposed inverse control methodology, two popular control structures, namely, feedback linearization and Nonlinear Internal Model Control (NIMC) are investigated. Moreover, the paper points out that, under some conditions, both of the control structures are equivalent and naturally implement a Smith predictor in the presence of time delays.

Design of a neural internal model control system for a robot

Robotica ◽  
2000 ◽  
Vol 18 (5) ◽  
pp. 505-512 ◽  
Author(s):  
D. T. Pham ◽  
Şahin Yildirim
Keyword(s):  
Internal Model ◽  
Neural Model ◽  
Internal Model Control ◽  
Bp Algorithm ◽  
Recurrent Networks ◽  
Neural Controller ◽  
Inverse Control ◽  
Control Scheme ◽  
Model Control ◽  
Two Degree Of Freedom

This paper describes the design of an Internal Model Control (IMC) system for a planar two-degree-of-freedom robot. IMC was investigated as an alternative to the basic inverse control scheme which is difficult to implement. The proposed IMC system consisted of a forward internal neural model of the robot, a neural controller and a conventional feedback controller, all of which were realised easily. Both the neural model and the neural controller were based on recurrent networks which were trained using the backpropagation (BP) algorithm. The paper presents the results obtained with two types of recurrent networks as well as a conventional PID system.

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