A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.

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Aims To develop a predictive model for Escherichia coli using deep neural networks. Methods and Results Batch experiments are conducted at different temperatures closer to optimum value (36·5°C, 37°C, 37·5°C, 38°C and 38·5°C) to obtain the growth curves of E .coli K‐12. Two primary models namely modified Gompertz and new logistic are chosen. Three secondary models namely Gaussian, nonlinear autoregressive eXogenous (NARX) model and long short‐term memory (LSTM) are developed. The novelty in this paper is the development of secondary models using artificial neural network (ANN) and deep network. The performance measures chosen to compare the developed primary and secondary models are correlation coefficient (R2), root‐mean‐square error (RMSE) and accuracy factor (Af). Results show that modified Gompertz model has better R2 (0·99) and RMSE (0·019) when compared to new logistic model. Also, the deep network model outperforms other secondary models. Based on the primary and novel secondary model, a predictive model (tertiary model) is developed with improved accuracy and is validated. Conclusions The proposed predictive model exhibit good validation results in terms of RMSE and R2 values and can be applied for determining the growth rate of E. coli at a particular temperature value. Significance and Impact of the Study The proposed model can be used in food processing industries during enzyme production such as Chymosin, to predict the growth rate of E. coli as a function of temperature. Also, the developed LSTM and NARX models can be used to predict maximum specific growth rate of other microbial strains with proper training.

Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.

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Millions of people around the world are affected by different kinds of epileptic seizures. A deep brain stimulator is now claimed to be one of the most promising tools to control severe epileptic seizures. The present study proposes Hodgkin-Huxley (HH) model-based Active Fault Tolerant Deep Brain Stimulator (AFTDBS) for brain neurons to suppress epileptic seizures against ion channel conductance variations using a Deep Neural Network (DNN). The AFTDBS contains the following three modules: (i) Detection of epileptic seizures using black box classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), (ii) Prediction of ion channels conductance variations using Long Short-Term Memory (LSTM), and (iii) Development of Reconfigurable Deep Brain Stimulator (RDBS) to control epileptic spikes using Proportional Integral (PI) Controller and Model Predictive Controller (MPC). Initially, the synthetic data were collected from the HH model by varying ion channel conductance. Then, the seizure was classified into four groups namely, normal and epileptic due to variations in sodium ion-channel conductance, potassium ion-channel conductance, and both sodium and potassium ion-channel conductance. In the present work, current controlled deep brain stimulators were designed for epileptic suppression. Finally, the closed-loop performances and stability of the proposed control schemes were analyzed. The simulation results demonstrated the efficacy of the proposed DNN-based AFTDBS.

Food drying is one of the important methods to prevent microbial growth during preservation. However, it is a complex non-linear process where the quality of the food depends on environmental conditions. Therefore, food drying must be carried out under controlled environment. In this paper, an internal model control (IMC) scheme is developed for pineapple drying using the evolutionary algorithms namely: genetic algorithm (GA) and particle swarm optimization (PSO) to achieve the desired quality (single objective). In order to reduce the control effort and hence the cost, without compromising the desired quality, a multi-objective control scheme is also formulated using weighted sum method. The closed loop performance of the control scheme for GA-based IMC-PI controller and PSO-based IMC-PI controller are analyzed for servo and regulatory operations. The results thus obtained are compared both qualitatively and quantitatively. From the simulation results it is observed that PSO-based IMC-PI controller gives better performance and better range of the temperature compared to the other control schemes.