Model Control Theory Research Articles

Visceral obesity and psychosocial stress: a generalised control theory model

ABSTRACTThe linking of control theory and information theory via the Data Rate Theorem and its generalisations allows for construction of necessary conditions statistical models of body mass regulation in the context of interaction with a complex dynamic environment. By focusing on the stress-related induction of central obesity via failure of HPA axis regulation, we explore implications for strategies of prevention and treatment. It rapidly becomes evident that individual-centred biomedical reductionism is an inadequate paradigm. Without mitigation of HPA axis or related dysfunctions arising from social pathologies of power imbalance, economic insecurity, and so on, it is unlikely that permanent changes in visceral obesity for individuals can be maintained without constant therapeutic effort, an expensive – and likely unsustainable – public policy.

Optimization and Experimentation of Dual-Mass MEMS Gyroscope Quadrature Error Correction Methods.

This paper focuses on an optimal quadrature error correction method for the dual-mass MEMS gyroscope, in order to reduce the long term bias drift. It is known that the coupling stiffness and demodulation error are important elements causing bias drift. The coupling stiffness in dual-mass structures is analyzed. The experiment proves that the left and right masses’ quadrature errors are different, and the quadrature correction system should be arranged independently. The process leading to quadrature error is proposed, and the Charge Injecting Correction (CIC), Quadrature Force Correction (QFC) and Coupling Stiffness Correction (CSC) methods are introduced. The correction objects of these three methods are the quadrature error signal, force and the coupling stiffness, respectively. The three methods are investigated through control theory analysis, model simulation and circuit experiments, and the results support the theoretical analysis. The bias stability results based on CIC, QFC and CSC are 48 °/h, 9.9 °/h and 3.7 °/h, respectively, and this value is 38 °/h before quadrature error correction. The CSC method is proved to be the better method for quadrature correction, and it improves the Angle Random Walking (ARW) value, increasing it from 0.66 °/√h to 0.21 °/√h. The CSC system general test results show that it works well across the full temperature range, and the bias stabilities of the six groups’ output data are 3.8 °/h, 3.6 °/h, 3.4 °/h, 3.1 °/h, 3.0 °/h and 4.2 °/h, respectively, which proves the system has excellent repeatability.

Linear quadratic optimal controller for cable-driven parallel robots

In recent years, various cable-driven parallel robots have been investigated for their advantages, such as low structural weight, high acceleration, and large work-space, over serial and conventional parallel systems. However, the use of cables lowers the stiffness of these robots, which in turn may decrease motion accuracy. A linear quadratic (LQ) optimal controller can provide all the states of a system for the feedback, such as position and velocity. Thus, the application of such an optimal controller in cable-driven parallel robots can result in more efficient and accurate motion compared to the performance of classical controllers such as the proportional- integral-derivative controller. This paper presents an approach to apply the LQ optimal controller on cable-driven parallel robots. To employ the optimal control theory, the static and dynamic modeling of a 3-DOF planar cable-driven parallel robot (Feriba-3) is developed. The synthesis of the LQ optimal control is described, and the significant experimental results are presented and discussed.

Mosaic gene network modelling identified new regulatory mechanisms in HCV infection

Modelling of gene networks is widely used in systems biology to study the functioning of complex biological systems. Most of the existing mathematical modelling techniques are useful for analysis of well-studied biological processes, for which information on rates of reactions is available. However, complex biological processes such as those determining the phenotypic traits of organisms or pathological disease processes, including pathogen-host interactions, involve complicated cross-talk between interacting networks. Furthermore, the intrinsic details of the interactions between these networks are often missing. In this study, we developed an approach, which we call mosaic network modelling, that allows the combination of independent mathematical models of gene regulatory networks and, thereby, description of complex biological systems. The advantage of this approach is that it allows us to generate the integrated model despite the fact that information on molecular interactions between parts of the model (so-called mosaic fragments) might be missing. To generate a mosaic mathematical model, we used control theory and mathematical models, written in the form of a system of ordinary differential equations (ODEs). In the present study, we investigated the efficiency of this method in modelling the dynamics of more than 10,000 simulated mosaic regulatory networks consisting of two pieces. Analysis revealed that this approach was highly efficient, as the mean deviation of the dynamics of mosaic network elements from the behaviour of the initial parts of the model was less than 10%. It turned out that for construction of the control functional, data on perturbation of one or two vertices of the mosaic piece are sufficient. Further, we used the developed method to construct a mosaic gene regulatory network including hepatitis C virus (HCV) as the first piece and the tumour necrosis factor (TNF)-induced apoptosis and NF-κB induction pathways as the second piece. Thus, the mosaic model integrates the model of HCV subgenomic replicon replication with the model of TNF-induced apoptosis and NF-κB induction. Analysis of the mosaic model revealed that the regulation of TNF-induced signaling by the HCV network is crucially dependent on the RIP1, TRADD, TRAF2, FADD, IKK, IκBα, c-FLIP, and BAR genes. Overall, the developed mosaic gene network modelling approach demonstrated good predictive power and allowed the prediction of new regulatory nodes in HCV action on apoptosis and the NF-κB pathway. Those theoretical predictions could be a basis for further experimental verification.

The convergence and stability analysis of the Jacobi collocation method for solving nonlinear fractional differential equations with integral boundary conditions

In this paper, we apply the Jacobi collocation method for solving nonlinear fractional differential equations with integral boundary conditions. Due to existence of integral boundary conditions, after reformulation of this equation in the integral form, the method is proposed for solving the obtained integral equation. Also, the convergence and stability analysis of the proposed method are studied in two main theorems. Furthermore, the optimum degree of convergence in the L2 norm is obtained for this method. Furthermore, some numerical examples are presented in order to illustrate the performance of the presented method. Finally, an application of the model in control theory is introduced. Copyright © 2015 John Wiley & Sons, Ltd.

Comparing theories' performance in predicting violence

The stakes of choosing the best theory as a basis for violence prevention and offender rehabilitation are high. However, no single theory of violence has ever been universally accepted by a majority of established researchers. Psychiatry, psychology and sociology are each subdivided into different schools relying upon different premises. All theories can produce empirical evidence for their validity, some of them stating the opposite of each other. Calculating different models with multivariate logistic regression on a dataset of N=21,312 observations and ninety-two influences allowed a direct comparison of the performance of operationalizations of some of the most important schools. The psychopathology model ranked as the best model in terms of predicting violence right after the comprehensive interdisciplinary model. Next came the rational choice and lifestyle model and third the differential association and learning theory model. Other models namely the control theory model, the childhood-trauma model and the social conflict and reaction model turned out to have low sensitivities for predicting violence. Nevertheless, all models produced acceptable results in predictions of a non-violent outcome.

Information management strategies and supply chain performance under demand disruptions

The operations management literature presents inadequate comprehensive understanding on information management strategies of mitigating supply chain disruption risks. By using control theory modelling and simulation, this study compares the disruption mitigation effects of three information management strategies. From the aspect of stability, the existing stability boundaries are revised by a new method in a two-echelon case. It shows that supply chains (SC) with popular information management strategies are not evidently more stable than traditional ones. From the aspect of disruption recovery time, an innovative two-echelon swiftest response problem under these information management strategies is formulated and solved. Results show that a collaborative planning, forecasting and replenishment (CPFR) SC with complete SC information performs the best. However, in a later operational risk mitigation test, an information sharing (IS) SC with partial information has the smallest bullwhip effect. From the aspect of demand amplification and frequency response, an innovative frequency–response plot of order amplification is proposed in a time-continuous SC with moving average forecasts. It implies the best frequency response for concurrently mitigating both operational and disruption risks coming from a CPFR SC. But for a certain SC structure there is still a balance between mitigating bullwhip effect and quick response. Moreover, it also implies that anti-bullwhip should exist in a certain condition, as realised in our numerical experiments.

Automated Power Control for Virtualized Infrastructures

Power control for virtualized environments has gained much attention recently. One of the major challenges is keeping underlying infrastructure in reasonably low power states and achieving service-level objectives (SLOs) of upper applications as well. Existing solutions, however, cannot effectively tackle this problem for virtualized environments. In this paper, we propose an automated power control solution for such scenarios in hope of making some progress. The major advantage of our solution is being able to precisely control the CPU frequency levels of a physical environment and the CPU power allocations among virtual machines with respect to the SLOs of multiple applications. Based on control theory and online model estimation, our solution can adapt to the variations of application power demands. Additionally, our solution can simultaneously manage the CPU power control for all virtual machines according to their dependencies at either the application-level or the infrastructure-level. The experimental evaluation demonstrates that our solution outperforms three state-of-the-art methods in terms of achieving the application SLOs with low infrastructure power consumption.

Tips on Stochastic Optimal Feedback Control and Bayesian Spatiotemporal Models: Applications to Robotics

This tutorial paper presents the expositions of stochastic optimal feedback control theory and Bayesian spatiotemporal models in the context of robotics applications. The presented material is self-contained so that readers can grasp the most important concepts and acquire knowledge needed to jump-start their research. To facilitate this, we provide a series of educational examples from robotics and mobile sensor networks.

Understanding the Change Process Involved in Solving Psychological Problems: A Model-based Approach to Understanding How Psychotherapy Works.

A review of the literature on psychotherapy suggests that improvements in effectiveness, efficiency and accessibility have been hampered by a lack of understanding of how psychotherapy works. Central to gaining such understanding is an accurate description of the change process that occurs when someone solves a psychological problem. We describe the Perceptual Control Theory (PCT) model of human functioning, which can be used to understand the nature of psychological problems and how they are solved. PCT suggests that problems can be broadly grouped into two categories: those that can be solved using existing skills and those that require the generation of new skills. In general, psychological problems belong in the second category. PCT describes a fundamental form of learning in which existing structures and systems are reorganized to create new skills, perspective and insights. Psychotherapy based on PCT is aimed at directing reorganization to the source of the problem. Understanding the phenomenon of control is central to understanding how psychotherapy works. Conflict could be considered a general formulation for psychological distress. Therapy will be efficient when the reorganization process is focused at the right level of the client's control hierarchy. Therapy will be effective only when the client's reorganization system-not the therapist-has managed to come up with a solution to the client's problem. What the client says about the nature and reason for their problem is less important than the point of view from which these problems are being discussed.

EAD and DAD mechanisms analyzed by developing a new human ventricular cell model

It has long been suggested that the Ca2+-mechanisms are largely involved in generating the early afterdepolarization (EAD) as well as the delayed afterdepolarization (DAD). This view was examined in a quantitative manner by applying the lead potential analysis to a new human ventricular cell model. In this ventricular cell model, the tight coupled LCC-RyR model (CaRU) based on local control theory (Hinch et al. 2004) and ion channel models mostly based on human electrophysiological data were included to reproduce realistic Ca2+ dynamics as well as the membrane excitation. Simultaneously, the Ca2+ accumulation near the Ca2+ releasing site was incorporated as observed in real cardiac myocytes. The maximum rate of ventricular repolarization (−1.02 mV/ms) is due to IK1 (−0.55 mV/ms) and the rest is provided nearly equally by INCX (−0.20 mV/ms), INaL (−0.16 mV/ms) and INaT (−0.13 mV/ms). These INaL and INaT components are due to closure of the voltage gate, which remains partially open during the plateau potential. DADs could be evoked by applying high-frequency stimulations supplemented by a partial Na+/K+ pump inhibition, or by a microinjection of Ca2+. EADs was evoked by retarding the inactivation of INaL. The lead potential (VL) analysis revealed that IK1 and IKr played the primary role to reverse the AP repolarization to depolarizing limb of EAD. ICaL and INCX amplified EAD, while the remaining currents partially antagonized dVL/dt. The maximum rate of rise of EAD was attributable to the rapid activation of both ICaL (45.5%) and INCX (54.5%).

Passivity-Based Controller of Tri-State Boost Converter

A novel boost converter named tri-state boost converter is introduced in this paper. According to the operation principle of the tri-state boost converter, the Euler-Lagrange model is set up. Based on the passivity-based control theory and Euler-Lagrange model, a passivity-based controller of the tri-state boost converter is designed. By damping injecting, the input current and output voltage of the tri-state boost converter can reach the desired equilibrium values rapidly. The speed of the tri-state boost converter to reach the desired equilibrium values varies when the damping values changes. The controller is simple to design and makes the tri-state boost converter having better dynamic and steady performances. The passivity-based controller proposed in the paper is proved feasible by simulation results.

Optimal control for predicting customized drug dosage for superovulation stage of in vitro fertilization

in vitro fertilization (IVF) is one of the most highly pursued assisted reproductive technologies (ART) worldwide. IVF procedure is divided into four stages: Superovulation, Egg-retrieval, Insemination/Fertilization and Embryo transfer. Among these superovulation is the most crucial stage since it involves external injection of hormones to stimulate development and maturation of multiple follicles or oocytes. Although numerous advancements have been made in IVF procedures, little attention has been given to modifying the existing protocols based on a ‘patient specific’ predictive model. A model for follicle growth and number change as a function of the injected hormones and patient characteristics has been developed and validated for data available on 50 superovulation cycles. The model has 9 patient specific parameters which can be determined from the initial 2 days of observation and can help in projecting the superovulation outcome for the ongoing cycle. Based on this model, the dosage of the hormones to stimulate multiple ovulation or follicle growth is predicted by using the theory of optimal control. The objective of successful superovulation is to obtain maximum number of mature oocytes/follicles within a particular size range. Using the mathematical model of follicle growth dynamics and optimal control theory, optimal dose and frequency of medication customized for each patient (n=5) is predicted for obtaining the desired result. The results indicate a better final day follicle size distribution when the dosage of the hormones is varied by some amounts as compared to the actual dosage given to the patient in the existing cycles. This ensures a better success rate for the superovulation cycles and reduces the costs of excess medication and daily monitoring. The idea is to provide the medical practitioners with a guideline for planned treatment, for a procedure currently based on trial and error in order to get better success rates.

The Mathematics. The Cybernetics. The Informatics

The article discusses the paradigm shift from traditional mathematical models of control theory to the A.N. Kolmogorov's algorithmic theory of computer science. A comparison between the identifiable object information and Shannon's ensemble (entropy) information. The proposed algorithmic models based on the respective approximations of space filling curves also regarded as self-similar recursive structure (fractal approach).

Mathematical Model and Algorithm Design of PDCA Cyclic Block Control Based on Digital Sensor

In this paper we introduce the PDCA theory into the computer communication, realize the computer cyclic block control, and get the overall arrangement network of computer sensor. In the first part we introduce the application of PDCA in computer, the second part we establish of the mathematical model of computer block cyclic control theory. In order to verify the availability of mathematical model, we designed the computer control experiment of digital sensor. After the temperature calibration of DHT11 sensor in temperature check box, the calibration data is stored in the OTP memory in the form of a program. And we use Keilu Vision2 compiler to realize the LED display of detected temperature. It provides the technical reference for the application of computer control technology.

Evaluation of Optimal International Economic Policy based on both the Parametric Control Theory and Global Computable General Equilibrium Model

Based on the GLOBE model global dynamic computable general equilibrium model (to describe functioning and interaction of the Customs Union (CU) of Belarus, Kazakhstan, and Russia with Armenia, Kyrgyzstan, the European Union and the rest of the world) is developed and calibrated. The calibrated model is tested for the possibility of practical application and on its base the possibility of optimal economic policy both within the CU and within functioning and interaction of the CU, the EU, Armenia, Kyrgyzstan and the rest of the world is assessed using methods of the parametric control theory.

English

English

EOQ Revisited with Sustainability Considerations

Abstract The economic order quantity (EOQ) model is a pure economic model in classical inventory control theory. The model is designed to find the order quantity so as to minimize total cost under a deterministic setting. In this study, we revise the standard EOQ model to incorporate sustainability considerations that include environmental and social criteria in addition to the conventional economic considerations. We propose models for a number of different policies and analyze these revised models. Based on our analysis, we show how the triple bottom line considerations of sustainability be appended to traditional cost accounting in EOQ model. We also provide a number of useful insights for decision and policy making practices.

School Connectedness and Problematic Internet Use in Adolescents: A Moderated Mediation Model of Deviant Peer Affiliation and Self-Control

Although a growing body of research documents the negative association between school connectedness and adolescent problematic Internet use (PIU), little is known about the mediating mechanism (i.e., how school connectedness relates to PIU?) and moderating mechanism (i.e., when the protection is most potent?) underlying this relation. The present study examined whether deviant peer affiliation mediated the relationship between school connectedness and PIU, and whether this mediating process was moderated by adolescent self-control. A total of 2,758 Chinese adolescents (46 % male; mean age = 13.53 years, SD = 1.06) from 10 middle schools completed anonymous questionnaires regarding school connectedness, deviant peer affiliation, self-control, and PIU. After controlling for gender, age, socioeconomic status, and parental attachment, it was found that the negative association between school connectedness and adolescent PIU was partially mediated by deviant peer affiliation. Moreover, this indirect link was stronger for adolescents with low self-control than for those with high self-control. These findings underscore the importance of integrating the social control theory and organism-environment interaction model to understand how and when school connectedness impacts adolescent PIU.

High tech supply chain simulation based on dynamical systems model

During the last 45 years, system dynamics as a continuous type of simulation has been used for simulating various problems, ranging from economic to engineering and managerial when limited (historical) information is available. Control theory is another alternative for continuous simulation that also includes the modelling of differential equations, but mostly employed in engineering. Based on the system dynamics simulation model of Sterman’s high-tech growth firms, this paper studies the application of control theory simulation in design of a high-tech supply chain system and compares the results with those of system dynamics-based simulation. It is shown how easily supply chain processes can be translated into a control theory model using the differential equation approach in the place of Laplace transform. In addition, some improvements are also made on Sterman’s model.