Deep Neural Controller: a Neural Network for Model-free Predictive Control and its Application to Viscosity Control in Chemical Process

2017 ◽  
Author(s):  
JUNYOUNG PARK ◽  
HYO-WON SUH ◽  
JINKYOO PARK
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Chemical Process ◽  
Neural Controller ◽  
Model Free

Adaptive Neural Network Based Predictive Control of Nonlinear Systems With Slow Dynamics

2020 ◽  
Author(s):  
Mark Spiller ◽  
Fateme Bakhshande ◽  
Dirk Söffker
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Predictive Control ◽  
Mimo System ◽  
Prediction Equation ◽  
Control Input ◽  
System Behavior ◽  
Slow Dynamics ◽  
Model Free ◽  
Data Driven Approach

Abstract In this paper a data-driven approach for model-free control of nonlinear systems with slow dynamics is proposed. The system behavior is described using a local model respectively a neural network. The network is updated online based on a Kalman filter. By predicting the system behavior two control approaches are discussed. One is obtained by calculating a control input from the one step ahead prediction equation using least squares, the other is obtained by solving a standard linear model predictive control problem. The approaches are tested on a constrained nonlinear MIMO system with slow dynamics.

scholarly journals Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2325
Author(s):  
Sanaz Sabzevari ◽  
Rasool Heydari ◽  
Maryam Mohiti ◽  
Mehdi Savaghebi ◽  
Jose Rodriguez
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Strategies ◽  
Power Converter ◽  
Performance Criterion ◽  
Parameter Mismatch ◽  
Model Free ◽  
Finite Control ◽  
Free Current ◽  
Definition Of

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

Sensor Fault Detection and Validation For Chemical Process Using A Neural Network Model

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Chemical Process ◽  
Sensor Fault ◽  
Sensor Fault Detection
Sign in / Sign up

Export Citation Format

Share Document