On improving accuracy of computationally efficient nonlinear predictive control based on neural models

Ławryńczuk, Maciej

doi:10.1016/j.ces.2011.07.015

Cited by 21 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same linear approximation is used for prediction over the whole prediction horizon. In the MPC algorithm with Nonlinear Prediction and Linearisation along the Predicted Trajectory (MPC-NPLPT), the rudimentary description of which is given in [8], not the model but the predicted output trajectory is linearised. Linearisation can be carried out once for an assumed trajectory or in an iterative manner a few times at each sampling instant.…”

Section: Mpc-npltp Algorithm With Linearisation Along the Trajectorymentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Predictive Control Based on Least Squares Support Vector Machines Hammerstein Models

Ławryńczuk¹

2013

Adaptive and Natural Computing Algorithms

Self Cite

View full text Add to dashboard Cite

Abstract. This paper shortly describes nonlinear Model Predictive Control (MPC) algorithms for Least Squares Support Vector Machines (LS-SVM) Hammerstein models. The model consists of a nonlinear steady-state part in series with a linear dynamic part. A linear approximation of the model for the current operating point or a linear approximation of the predicted output trajectory along an input trajectory is used for prediction. As a result, all algorithms require solving on-line a quadratic programming problem or a series of such problems, unreliable and computationally demanding nonlinear optimisation is not necessary.

show abstract

Section: Mpc-npltp Algorithm With Linearisation Along the Trajectorymentioning

confidence: 99%

“…neural structures [7,8,10]. Although the feedforward neural network can be efficiently used in practice for modelling of many technological processes, the neural model is entirely a black-box model, i.e.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Predictive Control Based on Least Squares Support Vector Machines Hammerstein Models

Ławryńczuk¹

2013

Adaptive and Natural Computing Algorithms

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such as developing a neural network model, it is easy to make a large number of simulations to feed the neural network whereas it is more difficult to make the experiments that will also lack the same reproducibility. The empirical approaches include polynomial approximations (e.g., autoregressive moving average model with exogenous inputs, ARMAX) [13][14][15], artificial neural networks [16][17][18][19][20][21][22][23], piecewise linear models [24], Volterra series [25], Wiener and Hammerstein models [19][20][21], etc. In the above empirical models, neural network models have found wide applicability because of their inherent capability of handling complex and nonlinear problems and reducing the engineering effort required in controller model development.…”

Section: Introductionmentioning

confidence: 99%

Neural network based nonlinear model predictive control for an intensified continuous reactor

Shi

2015

Chemical Engineering and Processing: Process Intensification

View full text Add to dashboard Cite

“…Model predictive control is one of the mature and commonplace control approaches which is applied in many practical and academic systems [28][29][30][31]. M PC strictly depends on the model of system which is under the control [15,23].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach

Sarailoo¹,

Rafiee²,

Rezaie³

2014

IJISA

View full text Add to dashboard Cite

Abstract-In this paper, a fu zzy predict ive control scheme is proposed for controlling output voltage of a step-down DC-DC converter in presence of disturbance and uncertainty. The DC-DC converter is considered as a hybrid system and modeled by Mixed Logical Dynamical modeling approach. The main objective of the paper is to design a Fuzzy Pred ictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. A model predictive control is designed based on the hybrid model and applied to the system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from lack of robustness in presence of disturbance and uncertainty. So, to succeed in facing up to the problem a fu zzy supervisor is utilized to adjust the main predict ive controller based on the measured states of the system. In this paper it is shown that the proposed fuzzy pred ictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty. Through simulat ions effectiveness of the proposed method is shown. Index Terms-Step-Down DC-DC Converter, Hybrid Model, Mixed Logical Dynamical Model, Model Predictive Control, Fuzzy Supervisor

show abstract

On improving accuracy of computationally efficient nonlinear predictive control based on neural models

Cited by 21 publications

References 24 publications

Nonlinear Predictive Control Based on Least Squares Support Vector Machines Hammerstein Models

Nonlinear Predictive Control Based on Least Squares Support Vector Machines Hammerstein Models

Neural network based nonlinear model predictive control for an intensified continuous reactor

Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach

Contact Info

Product

Resources

About