Neera Jain

Decentralized Feedback Structures of a Vapor Compression Cycle System

In vapor compression cycle systems, it is desirable to effectively control the thermodynamic cycle by controlling the thermodynamic states of the refrigerant. By controlling the thermodynamic states with an inner loop, supervisory algorithms can manage critical functions and objectives such as maintaining superheat and maximizing the coefficient of performance. In practice, it is generally preferred to tune multiple single-input-single-output (SISO) control inner loops rather than a single multiple-input-multiple-output control inner loop. This paper presents a process by which a simplified feedback control structure, amenable to a decoupled SISO control loop design, may be identified. In particular, the many possible candidate input-output (I/O) pairs for decentralized control are sorted via a decoupling metric, called the relative gain array number. From a reduced set of promising candidate I/O pairs, engineering insight is applied to arrive at the most effective pairings successfully verified on an experimental air-conditioning-and-refrigeration test stand.

Thermodynamics-based optimization and control of vapor-compression cycle operation: Optimization criteria

This paper investigates multiple degree of freedom (MDOF) optimization of steady-state vapor-compression cycle (VCC) operation. Five degrees of freedom (DOFs) of the VCC are optimized using an objective function which minimizes the rate of exergy destruction in the cycle. The use of exergy is motivated by its ability to capture the physics of both the first and second laws of thermodynamics in a single property. A case study is considered in which the optimization is applied to a commercial truck transport refrigeration system (TTRS). The results suggest that by using the optimal set points generated by the exergy-based objective function, an increase of 52.5% in COP can be achieved over nominal operation. In particular, the optimization results highlight the regulation of evaporator and condenser pressure as critical parameters in improving the efficiency of steady-state cycle operation.

Decoupled feedforward control for an air-conditioning and refrigeration system

Multiple control objectives must be met in order to satisfy the system capacity and efficiency requirements of air-conditioning and refrigeration (A/C & R) systems. Moreover, in the HVAC industry, it is generally preferred to tune multiple single-input-single-output (SISO) control loops rather than a single multiple-input-multiple-output (MIMO) control loop. This paper demonstrates that a SISO decentralized control design, presented in, can be enhanced with feedforward control on select control channels to improve the response to reference signals and reduce the effect of system coupling. A feedforward controller is combined with a PI feedback controller to improve the reference tracking performance of average evaporator temperature while reducing the coupling effect of changes in other input channels. Both simulation and experimental results are presented.

Dynamic modeling of refrigerated transport systems with cooling-mode/heating-mode switch operations

This article presents dynamic modeling approaches to predict system performance characteristics of cooling-/heating-mode switch cycling operation, a commonly used temperature regulation approach in refrigerated transport systems. A dynamic model of a commercially available transport refrigeration system is presented, which describes the system dynamics during the mode switch transients. The development of the heat exchanger and accumulator models is highlighted using the switched modeling framework. Model validation against experimental data demonstrates the capabilities of the modeling approach in representing the transient behavior of the mode switch process. Simulation case studies to predict refrigerant mass distribution during transients and system performance with the influence of door-opening events are also provided to demonstrate modeling capabilities. The presented dynamic modeling framework can serve as a valuable tool to evaluate performance with different system configurations and operating strategies in transport refrigeration applications.

Stability analysis for decentralized control of multi-evaporator vapor-compression cycle systems

We consider the problem of stabilizing multi-evaporator vapor-compression cycle (ME-VCC) systems using decentralized controllers. ME-VCC systems, sometimes termed variable-refrigerant-flow systems, are prevalent in large buildings that maintain independent cooled spaces with a single heat rejection unit. We exploit the time-scale separation characteristic of ME-VCC systems and analyze the faster mass flow dynamics and their stability characteristics independently of the slower thermal dynamics in the system. An electrical circuit analogy is used to obtain a linearized state-space representation of the mass flow dynamics for two common architectures of ME-VCC systems. Using concepts from decentralized control theory, we provide conditions under which local static feedback controllers stabilize the overall closed-loop system with robustness to uncertainties in the coupling between subsystems. Our analysis characterizes the beneficial impact that discharge pressure regulating (DPR) valves have on the decentralized controller gains.

LMI Control Design for Nonlinear Vapor Compression Cycle Systems

To effectively control vapor compression cycle (VCC) systems whose dynamics are highly nonlinear, it is necessary to develop plant models and control laws for different operating regions. This paper presents a first-principles modeling framework that captures four operation modes over the operating envelope to construct an invariant-order switched system. To synthesize a multi-input multi-output (MIMO) control system, the Linear Quadratic Regulator (LQR) technique is framed as a control optimization problem with Linear Matrix Inequality (LMI) constraints which can be simultaneously solved for the set of considered linear systems. Stability and performance characteristics of the controlled system are guaranteed using a common quadratic Lyapunov function. Simulation results in a case study show that the LMI-based controller can maintain system operation at optimal set-points with mode switching over a wide operating envelope.Copyright

Thermodynamics-Based Optimization and Control of Vapor-Compression Cycle Operation: Control Synthesis

This paper considers the implementation of an exergy-based multiple degree of freedom (MDOF) optimization and control methodology for the operation of VCC systems. The optimization problem for the standard VCC is characterized in terms of 4 thermodynamic variables and 1 fluid-dynamic variable. The resulting control problem is then analyzed, and a design variable, Λ, is introduced which allows the user to choose how the optimization variables are projected onto a control space of lower dimension. The potential of this approach to improve operational efficiency, with respect to both first and second law efficiency metrics, is demonstrated on an experimental VCC system through implementation of the proposed optimization using a feedforward plus feedback control architecture.Copyright

Comparison of SISO and MIMO control techniques for a diagonally dominant vapor compression system

For vapor compression cycle (VCC) systems, it is important to meet multiple control objectives in order to satisfy system capacity and efficiency requirements. Moreover, in the HVAC industry, it is generally preferred to tune multiple single-input-single-output (SISO) control loops rather than a single multiple-input-multiple-output (MIMO) control loop. This paper shows that when using an appropriate choice of feedback variables, a decentralized control approach consisting of individual SISO control loops performs as well as a MIMO control approach. An identified system model is shown to be diagonally dominant when using a decoupling set of input-output pairings. A linear quadratic Gaussian (LQG) control design is compared to a decentralized SISO control design through experimental results. Both controllers produce comparable time-domain performance characteristics.

Dynamic modeling of trust in human-machine interactions

In an increasingly automated world, trust between humans and autonomous systems is critical for successful integration of these systems into our daily lives. In particular, for autonomous systems to work cooperatively with humans, they must be able to sense and respond to the trust of the human. This inherently requires a control-oriented model of dynamic human trust behavior. In this paper, we describe a gray-box modeling approach for a linear third-order model that captures the dynamic variations of human trust in an obstacle detection sensor. The model is parameterized based on data collected from 581 human subjects, and the goodness of fit is approximately 80% for a general population. We also discuss the effect of demographics, such as national culture and gender, on trust behavior by re-parameterizing our model for subpopulations of data. These demographic-based models can be used to help autonomous systems further predict variations in human trust dynamics.

Dynamic Modeling of Twin-Roll Steel Strip Casting

We consider the problem of dynamic coupling between the rapid thermal solidification and mechanical compression of steel in twin-roll steel strip casting. In traditional steel casting, molten steel is first solidified into thick slabs and then compressed via a series of rollers to create thin sheets of steel. In twin-roll casting, these two processes are combined, thereby making control of the overall system significantly more challenging. Therefore, a simple and accurate model that characterizes these coupled dynamics is needed for model-based control of the system. We model the solidification process with explicit consideration for the mushy (semi-solid) region of steel by using a lumped parameter moving boundary approach. The moving boundaries are also used to estimate the size and composition of the region of steel that must be compressed to maintain a uniform strip thickness. A novelty of the proposed model is the use of a stiffening spring to characterize the stiffness of the resultant strip as a function of the relative amount of mushy and solid steel inside the compression region. In turn this model is used to determine the force required to carry out the compression. Simulation results demonstrate key features of the overall model. INTRODUCTION Motivation and Problem Definition: Near-net-shape manufacturing processes are becoming a major contributor in the reduction of both environmental and economic costs in the industrial sector [1]. For the steel industry, twin-roll strip casting is one of the most prominent near-net-shape manufacturing processes. ∗Address all correspondence to this author. It requires just one-tenth of the facility space, and it reduces the energy consumption by a factor of nine, as compared to traditional steel casting [2]. In the latter, molten steel is first solidified into thick slabs and then compressed via a series of rollers to create thin sheets of steel. In contrast, in twin-roll casting, molten steel is poured directly onto the surface of two casting rolls which simultaneously cool and compress the steel into a strip with a thickness of 1−3 millimeters. Combining these two steps into a single continuous casting process introduces coupling between the rapid thermal solidification dynamics and the mechanical stiffness of the resulting steel strip. To compensate for this coupling from a controls perspective, we require a simple and accurate model that characterizes the system dynamics. Gaps in Literature: Many researchers have modeled the solidification process in twin-roll casting [3–7] but few have considered the coupling between the thermal and mechanical dynamics [5, 6]. In order to design a controller that achieves the desired performance objective of uniform strip thickness, we require a simple model that captures the relevant input-output dynamics of the process. Santos et al. [3] and Liu et al. [4] created high-resolution simulations of the solidification process, but these are too complex to be used for control design. For example, Santos et. al derived a model with over 400 states. Furthermore, these models were intended to only capture the solidification process, and they do not examine the coupling between the solidification dynamics and the mechanical stiffness of the steel strip. Other researchers have derived control-oriented models of the entire process [5–7]. However, their models assume an abrupt phase transition from liquid to solid steel when, in reality, this transition involves the storage of latent heat in a two1 Copyright c