scholarly journals Implementation of Neural Predictive Control To Distillation Column

REAKTOR ◽  
2017 ◽  
Vol 10 (1) ◽  
pp. 24
Author(s):  
S. Anwari
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Distillation Column ◽  
Controller Design ◽  
Fundamental Equation ◽  
Small Range ◽  
Unknown Parameters ◽  
Predictive Controller ◽  
Multivariable Models ◽  
Reliable Solution

This paper presents a neural predictive controller that is applied to distillation column. Distillation columns represent complex multivariable system, with fast and slow dynamics, significant interactions and directionality. A phenomenological model (i.e. a model derived from fundamental equation like mass and energy balance) of a distillation column is very complicated. For this reason, classical linear controller, such as PID (Proportional, Integral and Derivative) controller, will provide robustness only over relatively small range operation because of complexity and operation without lack of robustness. In this work, a neural network is developed for modeling and controlling a distillation column based on measured input-outputdata pairs. In distillation column, a neural network is trained on the unknown parameters of the system. The resulting implementationof the neural predictive controller is able to eliminate the most significant obstacles encountered in conventional predictive control application by facilitating  the development of complex multivariable models and providing a rapid, reliable solution to the control algorithm. Controller design and implementation are illustrated for a plant frequently referred to in the literature. Result are given for simulation experiments, which demonstrate the advantage of the neural based predictive controller both at the transient region and at the steady state region to overcome any overshoots.Keywords : neural predictive controller, distillation column, complex multivariable models

FEEDBACK PREDICTIVE CONTROL OF NONHOMOGENEOUS MARKOV JUMP SYSTEMS WITH NONSYMMETRIC CONSTRAINTS

2014 ◽  
Vol 56 (2) ◽  
pp. 138-149
Author(s):  
YANQING LIU ◽  
FEI LIU
Keyword(s):  
Predictive Control ◽  
Controller Design ◽  
Invariant Set ◽  
Markov Jump ◽  
Markov Jump Systems ◽  
Markov Jump System ◽  
Predictive Controller ◽  
Feasibility Region ◽  
Nonhomogeneous Markov Jump System ◽  
Jump Systems

AbstractWe consider feedback predictive control of a discrete nonhomogeneous Markov jump system with nonsymmetric constraints. The probability transition of the Markov chain is modelled as a time-varying polytope. An ellipsoid set is utilized to construct an invariant set in the predictive controller design. However, when the constraints are nonsymmetric, this method leads to results which are over conserved due to the geometric characteristics of the ellipsoid set. Thus, a polyhedral invariant set is applied to enlarge the initial feasible area. The results obtained are for a more general class of dynamical systems, and the feasibility region is significantly enlarged. A numerical example is presented to illustrate the advantage of the proposed method.

Predictive Controller Design and Simulation of Time-Varying System Based on IGPC

2011 ◽  
Vol 291-294 ◽  
pp. 2647-2651
Author(s):  
Qin He Gao ◽  
Wen Liang Guan
Keyword(s):  
Predictive Control ◽  
Controller Design ◽  
Industrial Process ◽  
Model Parameters ◽  
Time Varying ◽  
Controlled System ◽  
System Input ◽  
Predictive Controller ◽  
Industrial Process Control ◽  
Adaptive Abilities

An adaptive predictive controller is proposed to solve the time-varying characteristics of the industrial process control system. The arithmetic of implicit expression generalized predictive control（IGPC） is put forward to compute the optimal control signal increment. In order to decrease the computing work and increase the computing speed, the system input/output data are used to identify the controller parameters directly and the plant model parameters are unnecessary. Simulation results show that the controller can track the change of setting value excellently even though without any prior information of controlled system, and have excellent adaptive abilities for the changes of system external disturbing signals and model parameters.

Reactive planar non-prehensile manipulation with hybrid model predictive control

2020 ◽  
Vol 39 (7) ◽  
pp. 755-773
Author(s):  
Francois R Hogan ◽  
Alberto Rodriguez
Keyword(s):  
Integer Programming ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Controller Design ◽  
Hybrid Control ◽  
Machine Learning Techniques ◽  
Control Approach ◽  
Dynamic Constraints ◽  
Hybrid Dynamics ◽  
Predictive Controller

This article presents an offline solution and online approximation to the hybrid control problem of planar non-prehensile manipulation. Hybrid dynamics and underactuation are key characteristics of this task that complicate the design of feedback controllers. We show that a model predictive control approach used in tandem with integer programming offers a powerful solution to capture the dynamic constraints associated with the friction cone as well as the hybrid nature of contact. We introduce the Model Predictive Controller with Learned Mode Scheduling (MPC-LMS), which leverages integer programming and machine learning techniques to effectively deal with the combinatorial complexity associated with determining sequences of contact modes. We validate the controller design through a numerical simulation study and with experiments on a planar manipulation setup using an industrial ABB IRB 120 robotic arm. Results show that the proposed algorithm achieves closed-loop tracking of a nominal trajectory by reasoning in real-time across multiple contact modalities.

scholarly journals Hybrid Grey Wolf Optimization Nonlinear Model Predictive Control for Aircraft Engines Based on an Elastic BP Neural Network

2019 ◽  
Vol 9 (6) ◽  
pp. 1254 ◽  
Author(s):  
Lingfei Xiao ◽  
Min Xu ◽  
Yuhan Chen ◽  
Yusheng Chen
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Bp Neural Network ◽  
Aircraft Engine ◽  
Aircraft Engines ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Model Predictive Controller ◽  
Predictive Controller

In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this paper. Based on the acquired aircraft engines data, the elastic BP neural network is used to train the prediction model, and the grey wolf optimization algorithm is applied to improve the selection of initial parameters in the elastic BP neural network. The accuracy of network modeling is increased as a result. By introducing the logistics chaotic sequence, the individual optimal search mechanism, and the cross operation, the novel hybrid grey wolf optimization algorithm is proposed and then used in receding horizon optimization to ensure real-time operation. Subsequently, a nonlinear model predictive controller for aircraft engine is obtained. Simulation results show that, with constraints in the control signal, the proposed nonlinear model predictive controller can guarantee that the aircraft engine has a satisfactory performance.

Classical and Neural Network–Based Approach of Model Predictive Control for Binary Continuous Distillation Column

2014 ◽  
Vol 9 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Amit Kumar Singh ◽  
Barjeev Tyagi ◽  
Vishal Kumar
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Distillation Column ◽  
Chemical Engineering ◽  
Research Work ◽  
Settling Time ◽  
Training Data ◽  
Main Process

Abstract The objective of present research work is to develop a neural network–based model predictive control scheme (NN-MPC) for distillation column. To fulfill this objective, an existing laboratory setup of continuous binary-type distillation column (BDC) is used. An equation-based model that uses the fundamental physical and chemical laws along with valid normal assumptions is validated for this experimental setup. Model predictive control (MPC) is one of the main process control techniques explored in the recent past for various chemical engineering applications; therefore, the conventional MPC scheme and the proposed NN-MPC scheme are applied on the equation-based model to control the methanol composition. In NN-MPC scheme, a three-layer feedforward neural network model has been developed and is used to predict the methanol composition over a prediction horizon using the MPC algorithm for searching the optimal control moves. The training data is acquired by the simulation of the equation-based model under the variation of manipulated variables in the defined range. Two cases have been considered, one is for set point tracking and another is for feed flow disturbance rejection. The performance of the control schemes is compared on the basis of performance parameters namely overshoot and settling time. NN-MPC and MPC schemes are also compared with conventional PID controller. The results show the improvement in settling time with NN-MPC scheme as compared to MPC and conventional PID controller for both the cases.

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