Experimental Study of a Neural Generalized Predictive Force Control for a Hydraulic Actuator

2000 ◽  
Author(s):  
S. He ◽  
N. Sepehri
Keyword(s):  
Neural Networks ◽  
Experimental Study ◽  
Predictive Control ◽  
Force Control ◽  
Feedforward Neural Networks ◽  
Neural Model ◽  
Experimental Results ◽  
Generalized Predictive Control ◽  
Hydraulic Actuator ◽  
Predictive Force Control

Abstract In this paper, multilayer feedforward neural networks (NNs) are used for modeling and force control of a hydraulic actuator. The predictability of the instantaneous linearized neural model is examined and is used along with the generalized predictive control (GPC) algorithm to control the force exerted on the environment. Experimental results show that the neural-based generalized predictive control can handle different contact environments despite high nonlinearity and uncertainty in the hydraulic functions.

scholarly journals Position/Force Control of a Flexible Robotic Structure Using Generalized Predictive Control (GPC)

1997 ◽  
Vol 30 (20) ◽  
pp. 561-568
Author(s):  
J. Hernández ◽  
H. Camblong ◽  
C.F. Nicolás ◽  
J. Landaluze ◽  
R. Reyero
Keyword(s):  
Predictive Control ◽  
Force Control ◽  
Generalized Predictive Control

scholarly journals Experimental Results on Actuator/Sensor Failures in Adaptive GPC Position Control

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 43
Author(s):  
Dariusz Horla
Keyword(s):  
Predictive Control ◽  
Position Control ◽  
Experimental Results ◽  
Generalized Predictive Control ◽  
Sensor Failure ◽  
Servo Drive ◽  
Sensor Failures ◽  
Control Framework ◽  
Free Case ◽  
Performance Deterioration

This work relates to the reliable generalized predictive control issues in the case when actuator or sensor failures take place. The experimental results that form the basis from which the conclusions are drawn from have been obtained in the position control of a servo drive task, and extend the results from the prior research of the author, dedicated to velocity control problems. On the basis of numerous experiments, it has been shown which configuration of prediction horizons is most advantageous from the control performance viewpoint in the adaptive generalized predictive control framework, to cope with the latter failures, and related to a minimum performance deterioration in comparison with the nominal, i.e., failure-free, case. This case study is the main novelty of the presented work, as the other papers available in the field rather focus on additional modifications of the predictive control framework, and not leaving possible room for optimization/alteration of prediction horizons’ values. The results are shown on the basis of the experiments conducted on the laboratory stand with the Modular Servo System of Inteco connected to a mechanical backlash module to cause actuator/sensor failure-like behavior, and with a magnetic brake module to show the performance in the case of an unexpected load.

scholarly journals Fuzzy Predictive Force Control (FPFC) for Speed Sensorless Control of Single-side Linear Induction Motor

2017 ◽  
Vol 7 (6) ◽  
pp. 2132-2138
Author(s):  
S. Masoumi Kazraji ◽  
M. R. Feyzi ◽  
M. B. Bannae Sharifian ◽  
S. Tohidi
Keyword(s):  
Induction Motor ◽  
Predictive Control ◽  
Force Control ◽  
Control Method ◽  
Sensorless Control ◽  
Speed Sensorless ◽  
Linear Induction ◽  
Linear Induction Motor ◽  
Predictive Force Control ◽  
Single Side

In this paper a model fuzzy predictive force control (FPFC) for the speed sensorless control of a single-side linear induction motor (SLIM) is proposed. The main purpose of of predictive control is minimizing the difference between the future output and reference values. This control method has a lower force ripple and a higher convergence speed in comparison to conventional predictive force control (CPFC). In this paper, CPFC and FPFC are applied to a linear induction motor and their results are compared. The results show that this control method has better performance in comparison to the conventional predictive control method.

Neural Networks Predictive Controller Using an Adaptive Control Rate

2016 ◽  
pp. 614-633 ◽  
Author(s):  
Ahmed Mnasser ◽  
Faouzi Bouani ◽  
Mekki Ksouri
Keyword(s):  
Neural Networks ◽  
Gradient Method ◽  
Feedforward Neural Networks ◽  
Neural Model ◽  
Asymptotic Convergence ◽  
Marquardt Method ◽  
Predictive Controller ◽  
The Neural Networks ◽  
The Stability ◽  
Unknown Nonlinear Dynamics

A model predictive control design for nonlinear systems based on artificial neural networks is discussed. The Feedforward neural networks are used to describe the unknown nonlinear dynamics of the real system. The backpropagation algorithm is used, offline, to train the neural networks model. The optimal control actions are computed by solving a nonconvex optimization problem with the gradient method. In gradient method, the steepest descent is a sensible factor for convergence. Then, an adaptive variable control rate based on Lyapunov function candidate and asymptotic convergence of the predictive controller are proposed. The stability of the closed loop system based on the neural model is proved. In order to demonstrate the robustness of the proposed predictive controller under set-point and load disturbance, a simulation example is considered. A comparison of the control performance achieved with a Levenberg-Marquardt method is also provided to illustrate the effectiveness of the proposed controller.

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