Endogenous vasopressin and the central control of heart rate during dynamic exercise

The present article contains a brief review on the role of vasopressinergic projections to the nucleus tractus solitarii in the genesis of reflex bradycardia and in the modulation of heart rate control during exercise. The effects of vasopressin on exercise tachycardia are discussed on the basis of both the endogenous peptide content changes and the heart rate response changes observed during running in sedentary and trained rats. Dynamic exercise caused a specific vasopressin content increase in dorsal and ventral brainstem areas. In accordance, rats pretreated with the peptide or the V1 blocker into the nucleus tractus solitarii showed a significant potentiation or a marked blunting of the exercise tachycardia, respectively, without any change in the pressure response to exercise. It is proposed that the long-descending vasopressinergic pathway to the nucleus tractus solitarii serves as one link between the two main neural controllers of circulation, i.e., the central command and feedback control mechanisms driven by the peripheral receptors. Therefore, vasopressinergic input could contribute to the adjustment of heart rate response (and cardiac output) to the circulatory demand during exercise.

Indoor NO2 air pollution and lung function of professional cooks

Studies of cooking-generated NO2 effects are rare in occupational epidemiology. In the present study, we evaluated the lung function of professional cooks exposed to NO2 in hospital kitchens. We performed spirometry in 37 cooks working in four hospital kitchens and estimated the predicted FVC, FEV1 and FEF25-75, based on age, sex, race, weight, and height, according to Knudson standards. NO2 measurements were obtained for 4 consecutive days during 4 different periods at 20-day intervals in each kitchen. Measurements were performed inside and outside the kitchens, simultaneously using Palm diffusion tubes. A time/exposure indicator was defined as representative of the cumulative exposure of each cook. No statistically significant effect of NO2 exposure on FVC was found. Each year of work as a cook corresponded to a decrease in predicted FEV1 of 2.5% (P = 0.046) for the group as a whole. When smoking status and asthma were included in the analysis the effect of time/exposure decreased about 10% and lost statistical significance. On predicted FEF25-75, a decrease of 3.5% (P = 0.035) was observed for the same group and the inclusion of controllers for smoking status and asthma did not affect the effects of time/exposure on pulmonary function parameter. After a 10-year period of work as cooks the participants of the study may present decreases in both predicted FEV1 and FEF25-75 that can reach 20 and 30%, respectively. The present study showed small but statistically significant adverse effects of gas stove exposure on the lung function of professional cooks.

Calibration of closed loop controllers for setting impedances in force-reflecting systems

This paper discusses a new method of impedance control that has been successfully implemented on the master robot of a teleoperation system. The method involves calibrating the robot to quantify the effect of adjustable controller parameters on the impedances along its different axes. The empirical equations relating end-effector impedance to the controller's feedback gains are obtained by performing system identification tests along individual axes of the robot. With these equations, online control of end-effector stiffness and damping is possible without having to monitor joint torques or solving complex algorithms. Hard contact conditions and compliant interfaces have been effectively demonstrated on a telemanipulation test-bed using appropriate combinations of stiffness and damping settings obtained by this method.

A comparative study of multilayered and single layered neural network based predictive controllers

The authors compare the performance of two types of controllers one based on the multilayered network and the other based on the single layered CMAC network (cerebellar model articulator controller). The neurons (information processing units) in the multi-layered network use Gaussian activation functions. The control scheme which is considered is a predictive control algorithm, along the lines used by Willis et al. (1991), Kambhampati and Warwick (1991). The process selected as a test bed is a continuous stirred tank reactor. The reaction taking place is an irreversible exothermic reaction in a constant volume reactor cooled by a single coolant stream. This reactor is a simplified version of the first tank in the two tank system given by Henson and Seborg (1989).

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Reduction of the memory size in the microprogrammed controllers

In the paper the method of reduction of the memory size in the microprogrammed controllers with sharing codes is discussed. The idea is based on the modification of internal modules and connections of the device. Next, the reduction of the microinstruction length based on the hypergraph theory is performed, thus the total size of the memory is highly reduced. © 2011 IEEE.

A New Control Architecture for Robust Controllers in Rear Electric Traction Passenger HEVs

It is well known that control systems are the core of electronic differential systems (EDSs) in electric vehicles (EVs)/hybrid HEVs (HEVs). However, conventional closed-loop control architectures do not completely match the needed ability to reject noises/disturbances, especially regarding the input acceleration signal incoming from the driver's commands, which makes the EDS (in this case) ineffective. Due to this, in this paper, a novel EDS control architecture is proposed to offer a new approach for the traction system that can be used with a great variety of controllers (e. g., classic, artificial intelligence (AI)-based, and modern/robust theory). In addition to this, a modified proportional-integral derivative (PID) controller, an AI-based neuro-fuzzy controller, and a robust optimal H-infinity controller were designed and evaluated to observe and evaluate the versatility of the novel architecture. Kinematic and dynamic models of the vehicle are briefly introduced. Then, simulated and experimental results were presented and discussed. A Hybrid Electric Vehicle in Low Scale (HELVIS)-Sim simulation environment was employed to the preliminary analysis of the proposed EDS architecture. Later, the EDS itself was embedded in a dSpace 1103 high-performance interface board so that real-time control of the rear wheels of the HELVIS platform was successfully achieved.

Ezrin/radixin/moesin: Versatile controllers of signaling molecules and of the cortical cytoskeleton

Ezrin, radixin and moesin (ERM) proteins are widely distributed proteins located in the cellular cortex, in microvilli and adherens junctions. They feature an N-terminal membrane binding domain linked by an alpha-helical domain to the C-terminal actin-binding domain. In the dormant state, binding sites in the N-terminal domain are masked by interactions with the C-terminal region. The alpha-helical domain also contributes to masking of binding sites. A specific sequence of signaling events results in dissociation of these intramolecular interactions resulting in ERM activation. ERM molecules have been implicated in mediating actin-membrane linkage and in regulating signaling molecules. They are involved in cell membrane organization, cell migration, phagocytosis and apoptosis, and may also play cell-specific roles in tumor progression. Their precise involvement in these processes has yet to be elucidated.

Control of recycle processes using neural networks combined with PID controller

This thesis is focused on the control of a system with recycle. A new control strategy using neural network combined with PID controller was proposed. The combined controller was studied and tested on the pressure control of a vaporizer inside a para-xylene production process. The major problems are the negative effects of recycle and the delays on instability and performance. The neural network was designed to move the process close to the set points while the PID accomplishes the finer level of disturbance rejection and offset reductions. Our simulation results show that during control, the neural network was able to determine the nonlinear relationship between steady state and manipulated variables. The results also show the disturbance rejection was handled by PID controller effectively.

Robustness Analysis of Eigenstructure Assignment Controllers on Rigid-Flexible Satellites

Flexible spacecraft with attached solar panels may exhibit undesired vibrations and structural deformations. These types of vehicles show an intrinsic coupling of the elements of the structure. The attitude maneuvers performed by flexible spacecraft may cause non-desired deflections of attached flexible elements. Any attitude and orbit control system generally solves these problems using filters that are designed to attenuate the relative deflections of flexible appendages. In this paper, we propose a method for designing attitude static controllers using an eigenstructure assignment (EA) method. A set of requirements were specified from our understanding of the system modes in an open loop. Exhaustive theoretical and numerical simulations were performed on special cases to verify the controller design procedure. In the design of the controller, we considered all of the aspects that relate to the eigenstructure assignment. The primary objective of this paper is to demonstrate the feasibility of obtaining a high degree of decoupling for some selected modes via the application of an EA method. Finally a robustness analysis is perform to the system together with the designed controller by means of a mu-analysis

Non-linear Control of an Autonomous Ground Vehicle

In this paper, in order to select a speed controller for a specific non-linear autonomous ground vehicle, proportional-integral-derivative (PID), Fuzzy, and linear quadratic regulator (LQR) controllers were designed. Here, in order to carry out the tuning of the above controllers, a multicomputer genetic algorithm (MGA) was designed. Then, the results of the MGA were used to parameterize the PID, Fuzzy and LQR controllers and to test them under laboratory conditions. Finally, a comparative analysis of the performance of the three controllers was conducted.