A Comprehensive Evaluation of PID, Cascade, Model-Predictive, and RTDA Controllers for Regulation of Hypnosis

Gain Scheduling of a Robust Setpoint Tracking Disturbance Rejection and Aggressiveness Controller for a Nonlinear Process

In this paper, a robust setpoint tracking disturbance rejection and aggressiveness (RTD-A) controller is designed and developed to control the liquid level of a conical tank process. Meta-heuristic algorithms like grey wolf optimization and the genetic algorithm are used to tune the parameters of the RTD-A controller. Its performance is later compared with that of the conventional standard proportional integral derivative controller. The gain scheduled RTD-A controller is designed and implemented on a nonlinear conical tank process. Also, various performances attributes such as the integral square error, integral absolute error, integral time absolute error, rise time, and settling time are calculated for the first-order process and conical tank process. The servo responses with RTD-A are also compared against the responses recorded from the conventional control schemes.

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems.

Development of mixed constrained RTDA controller for industrial applications

Most of the industrial processes are MIMO and constrained in nature. So the development of MIMO constrained controller is always preferable and makes challenges to the control community. This paper proposes the development of constrained next generation RTDA controller to handle various linear inequality constraints in a multi-variable control framework. The Lagrangian function is formulated, and the dual problem is solved using Hildreth's algorithm. The combined hard-soft constrained RTDA optimization problem with exact penalty functions is solved using KKT conditions. Performance of the developed constrained RTDA controller is demonstrated using simulation studies of typical industrial unit operations such as nonlinear CSTR process and Wood and Berry distillation column. The performance of the constrained RTDA controller is compared with constrained DMC and GPC.

Design of RTDA controller for industrial process using SOPDT model with minimum or non-minimum zero

This research paper focuses on the design and development of simplified RTDA control law computation formulae for SOPDT process with minimum or non-minimum zero. The design of RTDA control scheme consists of three main components namely process output prediction, model prediction update and control action computation. The systematic approach for computation of the above three components for SOPDT process with minimum or non-minimum zero is developed in this paper. The design, implementation and performance evaluation of the developed controller is demonstrated via simulation examples. The closed loop equation, block diagram representation and theoretical stability derivation for RTDA controller are developed. The performance of proposed controller is compared with IMC, SPC, MPC and PID controller and it is demonstrated on Industrial non-linear CSTR process.