GA based predictive functional control for batch processes under actuator faults

Zhang, Ridong; Zou, Hongbo; Xue, Anke; Gao, Furong

doi:10.1016/j.chemolab.2014.06.006

Cited by 35 publications

(22 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several evolutionary algorithms have been proposed and successfully applied to obtain the multiple trade-off optimal solution [12,16,[18][19][20][21][22]25,32,33] and [35][36][37]. This is because evolutionary algorithms such as PSO and GSA have advantages of operating the search by using a population of solutions.…”

Section: Problem Formulationmentioning

confidence: 98%

See 1 more Smart Citation

A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability

Jiang

Wang

2015

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Section: Problem Formulationmentioning

confidence: 98%

“…The gravitational and inertia masses for each agent at iteration t are adjusted using Eqs. (36) and (37). q l ðtÞ ¼ fit l ðtÞ À worstðtÞ bestðtÞ À worstðtÞ ; ð36Þ…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability

Jiang

Wang

2015

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

“…erefore, the development of injection molding process control has gradually transferred from the traditional continuous-control strategy to control strategies that focus on batch-direction characteristics. e research group of Gao F has made great advances in this field [144,[162][163][164][165][166][167][168][169][170][171][172][173][174][175]. is section will introduce the application of two typical strategies in injection molding-traditional feedback control and iterative learning control-and summarize and analyze them.…”

Section: Process Controlmentioning

confidence: 99%

Intelligent Injection Molding on Sensing, Optimization, and Control

Zhao

Zhang

Dong

et al. 2020

Advances in Polymer Technology

View full text Add to dashboard Cite

Injection molding is one of the most significant material processing methods for mass production of plastic products. It is widely used in various industry sectors, and its products are ubiquitous in our daily life. The settings and optimization of the injection molding process dictate the geometric precision and mechanical properties of the final products. Therefore, sensing, optimization, and control of the injection molding process have a crucial influence on product quality and have become an active research field with abundant literature. This paper defines the concept of intelligent injection molding as the integral application of these three procedures—sensing, optimization, and control. This paper reviews recent studies on methods for the detection of relevant physical variables, optimization of process parameters, and control strategies of machine variables in the molding process. Finally, conclusions are drawn to discuss future research directions and technologies, as well as algorithms worthy of being explored and developed.

show abstract

“…Stability monitoring is very essential for realization of higherorder adaptive IIR filter [9]. To overcome these disadvantages, several different identification structures and corresponding algorithms have been proposed in the literature [10][11][12][13][14]. It has aroused much attention of many researchers to develop efficient, global and robust optimization algorithms for solving the adaptive IIR system identification problem.…”

Section: Introductionmentioning

confidence: 99%

A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Jiang

Wang

2014

Nonlinear Dyn

View full text Add to dashboard Cite

Design of adaptive infinite impulse response (IIR) filter is the process of utilizing adaptive algorithm to iteratively determine the filter parameters to obtain an optimal model for the unknown plant based on minimizing the error cost function. However, the error cost surface of IIR filter is generally nonlinear, non-differentiable and multimodal. Hence, an efficient global optimization technique is required to minimize the error cost objective. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA) is proposed in this paper for IIR filter design. The proposed HPSO-GSA updates particle positions through obeying the influence of gravity acceleration in GSA and receiving direction of cognitive memory and social sharing information from PSO by means of coevolutionary strategy. The effect of key parameters on the performance of the proposed algorithm is firstly studied, and the proper parameters in HPSO-GSA are established using five benchmark plants along with the same-order model. The simulation studies have been performed for the performance comparison of eight algorithms such as PSO, GSA, QPSO, DPSO, FO-DPSO, GAPSO, PSOGSA and the proposed HPSO-GSA for unknown IIR system identification with the same-order and reduced-order filters. Simulation results show that the proposed algorithm has advantages over PSO, GSA and other PSO-based variants in terms of the convergence speed and the MSE levels.

show abstract

GA based predictive functional control for batch processes under actuator faults

Cited by 35 publications

References 25 publications

A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability

A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability

Intelligent Injection Molding on Sensing, Optimization, and Control

A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Contact Info

Product

Resources

About