Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints

Kybernetes ◽  
2014 ◽  
Vol 43 (9/10) ◽  
pp. 1469-1482 ◽  
Author(s):  
Adel Taeib ◽  
Moêz Soltani ◽  
Abdelkader Chaari
Keyword(s):  
Particle Swarm Optimization ◽  
Predictive Control ◽  
Fuzzy Controller ◽  
Nonlinear Process ◽  
Model Predictive Controller ◽  
Swarm Optimization ◽  
Content Type ◽  
Control Scheme ◽  
Predictive Controller ◽  
New Type

Purpose – The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor. Design/methodology/approach – A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints. Findings – The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller. Originality/value – This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

scholarly journals Estabilizador de Sistemas de Potencia usando Control Predictivo basado en Modelo

2018 ◽  
Vol 15 (3) ◽  
pp. 286 ◽  
Author(s):  
Manuel A Duarte-Mermoud ◽  
Freddy Milla
Keyword(s):  
Particle Swarm Optimization ◽  
Single Machine ◽  
Particle Swarm ◽  
Model Predictive Controller ◽  
Swarm Optimization ◽  
Predictive Controller ◽  
Single Machine Infinite Bus

<p>Se propone un estabilizador de potencia predictivo para amortiguar oscilaciones de potencia en un sistema eléctrico de potencia(SEP) formado por una sola máquina conectada a una barra infinita (Single Machine Infinite Bus, SMIB). Este enfoque considera un análisis de estabilidad de pequeña señal, usando un modelo incremental alrededor de un punto de operación. El estabilizador proporciona señales de control óptimas, debido a que además de utilizar el controlador predictivo basado en modelo (Model Predictive Controller, MPC) sus parámetros se optimizan fuera de línea empleando un algoritmo de optimización por enjambre de partículas (Particle Swarm Optimization, PSO). Su comportamiento se compara con un estabilizador del sistema potencia convencional, con parámetros también optimizados con PSO fuera de línea. Para validar la metodología propuesta, se presentan numerosas simulaciones de respuestas dinámicas del SMIB, para diferentes condiciones de operación y perturbaciones.</p>

scholarly journals Parameter Estimation and Predictive Control Design for a Nonlinear Reactive Distillation Column

2019 ◽  
Vol 8 (4) ◽  
pp. 9675-9678
Keyword(s):  
Predictive Control ◽  
Distillation Column ◽  
Reactive Distillation ◽  
Control Design ◽  
Nonlinear Process ◽  
Least Square ◽  
Biodiesel Production ◽  
Recursive Least Square ◽  
Model Predictive Controller ◽  
Predictive Controller

Reactive distillation column is one of the key elements in the process of Petroleum and chemical industries, which is having nonlinear, multivariable and non-stationary characteristics. The conventional controller like PID provides fruitless control action for nonlinear process. This paper deals the design of the various Model predictive controller algorithms to control composition and Temperature of the Reactive distillation column in biodiesel production. Here the Recursive Least square technique is used to estimate the parameters and build the exact model of the Process. The MATLAB policy is used and accomplished of the GPC, SMPC and PID.

Accelerated micro particle swarm optimization for the solution of nonlinear model predictive control

2017 ◽  
Vol 14 (6) ◽  
pp. 509-521 ◽  
Author(s):  
Halim Merabti ◽  
Khaled Belarbi
Keyword(s):  
Particle Swarm Optimization ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Computation Time ◽  
Unmanned Vehicles ◽  
Nonlinear Model Predictive Control ◽  
Swarm Optimization ◽  
Content Type ◽  
Solution Methods

Purpose Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AµPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one. Design/methodology/approach The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance. Findings The results of the experiments are very promising and show that AµPSO can be considered as an alternative to the classical solution methods. Originality/value The computation time is less than 0.02 ms using an Intel Core i7 with 8GB of RAM.

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

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