Closed-loop identification for plants under model predictive control

Esmaili, Ali; Li, Jianyi; Xie, Jinyu; Isom, Joshua D.

doi:10.1016/j.conengprac.2017.12.003

Cited by 7 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The shape features of leaves is separated by different parts by using of Feature depends Shape Selection Template (FSST). Esmaili et al [29] have proposed shut circle framework distinguishing proof under unadulterated criticism just as its potential in a modern setting. The recognizability condition for straight plants yields significant down to earth bits of knowledge for a shut circle framework distinguishing proof framework.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Classifier Accuracy in Ayurvedic Medicinal Plants using WO-DNN

Keskar,

Maktedar

2019

IJEAT

View full text Add to dashboard Cite

Identification of right medicinal plants that goes in to the formation of a medicine is significant in ayurvedic medicinal industry. This paper focuses around the automatic identification proof of therapeutic plants that are regularly utilized in Ayurveda. The fundamental highlights required to distinguish a medicinal plant is its leaf shape, color and texture. In this paper, we propose efficient accurate classifier for ayurvedic medical plant identification (EAC-AMP) utilizing using hybrid optimal machine learning techniques. In EAC-AMP, image corners detect first and top, bottom leaf edges are computed by the improved edge detection algorithm. After preprocessing, the segmentation can achieve using spider optimization neural network (SONN), which segments leaf regions from an image. The time and frequency domain features are computed by the symbolic accurate approximation (SAX); other features shape features, color features and tooth features are computed by the two-dimensional binary phase encoding (2DBPE). Finally, a whale optimization with deep neural network (DNN) classifier is used to characterize the type of plants. Accuracy in identification of any ayurvedic plant leaf is achieved by understanding and extracting the plant features. The main objective of the proposed EAC-AMP approach is to increase the accuracy of classifier. MATLAB experimental analysis showed better results such as accuracy, sensitivity and specificity.

show abstract

Section: Related Workmentioning

confidence: 99%

Enhancing Classifier Accuracy in Ayurvedic Medicinal Plants using WO-DNN

Keskar,

Maktedar

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…For some processes, step responses for given step inputs can take long test times as shown in Figure (a). To reduce such test times, methods utilizing time-optimal controls are studied. The time-optimal controls consist of one and two switching times for the first and second order processes, respectively.…”

Section: Time-optimal Control and Minimized Test Time For The Step Re...mentioning

confidence: 99%

“…(6) Compute the time response of square wave input by applying the inverse FFT routine. (7) Extract the step response from the square wave response. This procedure can also be used to replace equations for y s (t) (eqs 8, 15, 20, and 22, reducing its noise levels.…”

Section: ■ (Fft Program Example)mentioning

confidence: 99%

“…They can be applied to stable processes without worrying how to select the model structures. Recent MPC’s have been designed based on state space models that can handle all types of process dynamics including integrating and unstable processes. , For the state space models, process tests with pseudo random binary sequence (PRBS) have been used. Trends to using the state space model and model parameter identifications with PRBS tests will be continued.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Minimized Test Times for Step and Pulse Responses of Slow Linear Processes

Lee

Edgar

Bâldea

et al. 2019

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

Step and pulse responses have long been used to identify process dynamics and design control systems such as PID (proportional-integral-derivative) and model predictive control. They are simple and can represent different types of process dynamics intuitively. However, for some processes, long test times in the open-loop environment are required to obtain them. Here, utilizing the time-optimal control techniques, methods to reduce test times significantly are investigated. From initial short-term responses, exact step and pulse responses are obtained without extrapolations or predictions. Since short-term responses are used, the proposed methods can also be used to restore step responses from fragments of failed step tests. Simulations and experiments are given to illustrate applicability of the proposed methods.

show abstract

“…A sensible solution is to employ data-driven techniques and system identification methods to identify the dynamics of the HVAC subsystems along with their local controllers, i.e., closed-loop dynamics, based on measured data, and then incorporate these closed-loop dynamics into the MPC design. By doing so, the model accuracy of the overall system to be controlled and, consequently, the control performance, can be improved [21], [22]. Closedloop identification for MPC has been studied in different settings in [23], [24].…”

Section: Introductionmentioning

confidence: 99%