Batch process plays a very crucial and important role in process industries. The increased operational flexibility and trend toward high-quality, low-volume chemical production has put more emphasis on batch processing. In this work, nonlinearities associated with the batch reactor process have been studied. ARX and NARX models have been identified using open-loop data obtained from the pilot plant batch reactor. The performance of the batch reactor with conventional linear controllers results in aggressive manipulated variable action and larger energy consumption due to its inherent nonlinearity. This issue has been addressed in the proposed work by identifying the nonlinear model and designing a nonlinear model predictive controller for a pilot plant batch reactor. The implementation of the proposed method has resulted in smooth response of the manipulated variable as well as reactor temperature on both simulation and real-time experimentation.

Safety critical control systems are used in situations where the failure of a control system due to sensor failure or actuator failure will lead to a loss of life, property or cause environmental harm. A helicopter system really needs a safety critical or reliable control system which faces a lot of turbulent conditions and is expected to perform well, being stable in the case of sensor or/and actuator failures. Also the helicopter system is difficult to model mathematically due to non- linearity and cross coupling. The Twin Rotor MIMO System (TRMS) resembles the helicopter system in behaviour with significant cross coupling between the longitudinal and lateral axes. In this paper an H infinity observer and H infinity controller for a Twin Rotor MIMO System is aimed to be designed which will be reliable in the case of sensor failure or actuator failure also in case of sensor failure and actuator failure.

A parameter-plane method based on Kharitonov theorem for the design of a robust proportional integral derivative (PID) controller for the control of a chemical continuous stirred tank reactor (CSTR), which is an example for a plant with interval parametric uncertainty, has been presented in this paper. A locus of the stability boundary is plotted for each of the four Kharitonov vortex polynomials in the K_P versus K_I parameter plane. An intersecting stability region is identified in the KP-KI plane from which the parameters of the stabilizing PID controller are obtained. Simulation results show that the designed controller successfully stabilizes and improves the performance of the uncertain system under consideration.

A dynamic model for the two-degree-of-freedom Twin Rotor MIMO System (TRMS) is extracted using a blackbox system identification technique. Its behaviour in certain aspects resembles that of a helicopter, with a significant cross coupling between longitudinal and lateral directional motions. Hence, it is an interesting identification and control problem. Using the extracted model an H∞ observer is designed which will estimate the states of the system in presence of worst case noise assumed to be impact on the system

Background and Aims: Remifentanil is a powerful synthetic opioid drug with a short initiation and period of action, making it an ultra-short-acting opioid. It is delivered as an intravenous infusion during surgical procedures for pain management. However, deciding on a suitable dosage depends on various aspects specific to each individual. Methods: Conventional pharmacokinetic and pharmacodynamic (PK-PD) models mainly rely on manually choosing the parameters. Target-controlled drug delivery systems need precise predictions of the drug’s analgesic effects. This work investigates various supervised machine learning (ML) methods to analyse the pharmacokinetic characteristics of remifentanil, imitating the measured data. From the Kaggle database, features such as age, gender, infusion rate, body surface area, and lean body mass are extracted to determine the drug concentration at a specific instant of time. Results: The characteristics show that the prediction algorithms perform better over traditional PK-PD models with greater accuracy and minimum mean squared error (MSE). By optimising the hyperparameters with Bayesian methods, the performance of these models is significantly improved, attaining the minimum MSE value. Conclusion: Applying ML algorithms in drug delivery can significantly reduce resource costs and the time and effort essential for laboratory experiments in the pharmaceutical industry.