Predictive control of nonlinear plant using piecewise-linear neural model

2017 ◽  
Author(s):  
Daniel Honc ◽  
Petr Dolezel ◽  
Lumir Gago
Keyword(s):  
Predictive Control ◽  
Piecewise Linear ◽  
Neural Model ◽  
Nonlinear Plant

PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant

2016 ◽  
Vol 4 (1) ◽  
pp. 19-42
Author(s):  
Kai Borgeest ◽  
Peter Josef Schneider
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Cooling System ◽  
Control Strategies ◽  
Fan Noise ◽  
Nonlinear Properties ◽  
Control Schemes ◽  
Advanced Control ◽  
Save Energy ◽  
Nonlinear Plant

For the cooling system of a mobile machine with m control variables and with n=m correction variables different control strategies have been investigated in order to minimize power to save energy and to reduce fan noise with sufficient cooling. The plant is nonlinear and not identified. Three different kinds of controllers have been investigated in several variations, i.e. fuzzy control, PI(D) and model predictive control (MPC). 14 different criteria have been used for evaluation. In many respects a linear controller with fuzzy prediction proved best, in particular the prediction model can handle nonlinear properties of the plant. A problem of advanced control schemes with unidentified plants is the difficulty to prove stability.

Using Neural Network Predictive Control for Riser-Slugging Suppression

2009 ◽  
Vol 4 (4) ◽  
Author(s):  
Reza Eslamloueyan ◽  
Elham Hosseinzadeh
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Oil And Gas ◽  
Neural Model ◽  
Nonlinear Process ◽  
Economic Benefits ◽  
Gas Production ◽  
Ann Model ◽  
Oil And Gas Production

Riser-slugging is a flow regime that can occur in multiphase pipeline-riser systems, and is characterized by severe flow and pressure oscillations. Reducing undesired slugging effects can have great economic benefits. Recently, control methods have been proposed to conquer slugging flow problems in pipeline risers. The advantages of using a control system are that it can be installed on existing oil and gas production facilities with no need for expensive equipment and no significant pressure drop is imposed to the system.In this work, a predictive control system based on Neural Network (NN) model of process is developed for handling and suppressing riser-slugging. An ANN model of the plant is used to predict future response of the nonlinear process. Storkaas dynamic model (Storkaas and Skogestad,2002) is employed for the process simulation. Comparing the results of this research to that of others, indicates that the proposed neural model predictive controller makes a significant improvement in the setpoint tracking especially for higher step change in the setpoint value.

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.

Sign in / Sign up

Export Citation Format

Share Document