Large Parameter Variations Research Articles

From a local stress approach to fracture mechanics: a comprehensive evaluation of the fatigue strength of welded joints

This paper investigates the possibility of unifying different criteria concerned with the fatigue strength of welded joints. In particular, it compares estimates based on local stress fields due to geometry (evaluated without any crack‐like defect) and residual life predictions in the presence of a crack, according to LEFM. Fatigue strength results already reported in the literature for transverse non‐load‐carrying fillet welds are used as an experimental database. Nominal stress ranges were largely scattered, due to large variations of joint geometrical parameters. The scatter band greatly reduces as soon as a 0.3‐mm virtual crack is introduced at the weld toe, and the behaviour of the joints is given in terms of ΔKI versus total life fatigue. Such calculations, not different from residual life predictions, are easily performed by using the local stress distributions determined near the weld toes in the absence of crack‐like defects. More precisely, the analytical expressions for KI are based on a simple combination of the notch stress intensity factors K1N and K2N for opening and sliding modes. Then, fatigue strength predictions, as accurate as those based on fracture mechanics, are performed by the local stress analysis in a simpler way.

A study on higher-order disturbance observer and robust stability

In earlier papers, it has been reported that a fast and precise servo system, which has low sensitivity to parameter variation and disturbance, can be realized with simple structure by using a high-order disturbance observer. However, no clear and simple design method satisfying specifications for robust stability, the influence of measurement noise, and relative stability (damping characteristics) for large parameter variation has been proposed. In this paper, we clarify the class of robust servo systems realized by adjusting the order of the disturbance observer and the control parameters. We apply this strategy to the design of a second delay system, such as a position servo system, and realize a high-performance robust servo system using a high-order disturbance observer. © 1999 Scripta Technica, Electr Eng Jpn, 128(1): 37–44, 1999

Temperature control with a neural fuzzy inference network

Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance.

Fuzzy adaptive controller design for the joint space control of an agricultural robot

The proper execution of agricultural robotic tasks needs the use of adaptive control techniques. This fact is mainly due to the nature of the systems to control, which are difficult-to-model and time-varying systems. After a review of previous works concerning adaptive control, a solution using a fuzzy adaptive controller is studied for the joint control of such robots. An analytic representation of a particular fuzzy system is first developed to deduce useful conclusions for the controller design. Then, a specialized learning architecture is used to allow the reconstruction of an error signal required for a gradient method for on-line modification of the consequent part of the inference rules of a Sugeno's fuzzy controller. At the same time, a second level constituted by static rules (meta-rules) is introduced to cope with some limits of the learning architecture. Clustering of some rules is proposed to be able to learn those that are not fired most of the time but essential for unusual robot motions. Thanks to this new structure, the controller is dedicated to each meta-rule, and the number of rules with respect to a solution without meta-rule is considerably reduced. Simulation results during large on-line variations in system parameters derived from a typical example of an agricultural robot show the effectiveness of the proposed approach. The controller stability is verified by using the so-called cell-to-cell mapping algorithm. Finally, the feasibility of the implementation of this algorithm in low-end hardware is shown.

A QFT-based robust SVC controller for improving the dynamic stability of power systems

A new design technique for an SVC-based power system damping controller has been proposed. The controller attempts to place all plant poles within a specified region on the s -plane to guarantee the desired closed loop performance. The use of Horowitz's quantitative feedback theory (QFT) permits the design of a `fixed gain controller' that maintains its performance in spite of large variations in the plant parameters during its normal course of operation. The required controller parameters are arrived at by solving an optimization problem that incorporates the control specifications. The performance of this robust controller has been evaluated on a single machine infinite bus system equipped with a mid point SVC, and the results are shown to be consistent with the expected performance of the stabilizer.

The R-S-T digital controller design and applications

The two-degrees-of-freedom R-S-T digital controller is becoming a standard for computer control in industry. This paper presents a methodology for the design of the R-S-T controller, which involves identification of the plant model from data, combined with a robust control design. The performance of the controller can be further enhanced by plant model identification in a closed loop, and re-tuning of the controller. For large parameter variations, adaptation has to be considered in order to maintain the performance. Software packages are available for the design, implementation and commissioning of the R-S-T digital controllers. The methodology is illustrated by its application to the control of deposited zinc in hot-dip galvanizing at SOLLAC (Florange, France).

A Kinetic Model for Simultaneous Fit of Clozapine and Norclozapine Concentrations in Chronic Schizophrenic Patients during Long-Term Treatment

The pharmacokinetic profiles of clozapine and its main metabolite, norclozapine, were investigated in 18 chronic schizophrenic inpatients during long-term treatment. Patients received stable daily doses (between 300 and 900mg) for at least 1 month. Plasma drug concentrations were determined by high performance liquid chromatography. The pharmacokinetic parameters were calculated from both noncompartmental and compartmental approaches with zero-order input rate using a kinetic model for simultaneous fit of clozapine and norclozapine (active metabolite) concentrations. Large interpatient variations in pharmacokinetic parameters of the two drugs were observed. Plasma clozapine concentration peaked on average at 2 hours. The mean elimination rate constants from compartments 1 (k(10)) and 2 (k(20 ), elimination rate constant of norclozapine) were 0.087 and 0.156h(-1), respectively. The rate of formation of norclozapine, k(12), averaged 1.25h(-1). The mean fraction of the administered dose converted to norclozapine was estimated to be 66%. The apparent clearance of clozapine (CL/F) averaged 44.7 L/h and the volume of distribution (V(c)/F) was 7.00 L/kg. The pharmacokinetics of clozapine after multiple doses were linear over the range of clozapine plasma concentrations of 145 to 1411 microg/L. This is the first study assessing the pharmacokinetic profile of clozapine plus norclozapine in plasma during long-term treatment. This pharmacokinetic model can be used to determine the population pharmacokinetic parameters of clozapine and norclozapine in order to optimise individual dosage regimens using a Bayesian methodology.

Multivariable vibration control of a coupled flexible structure using QFT

Quantitative feedback theory (QFT) is a valuable control strategy for multivariable systems that contain parametric uncertainty. This paper uses multivariable quantitative feedback theory (QFT) to design an internal model reference loop (IMRL) controller. The QFT IMRL method allows a single controller to be designed for large time-invariant parameter variations by forcing the system transfer function matrix to be diagonally dominant. The controller architecture causes the coupled system to behave like the uncoupled system with proper choice of reference model. The design method is extended to discrete time by using a pseudo-continuous time (PCT) approach. The control application is extended to robust disturbance rejection rather than robust tracking by designing an outer-loop controller to reduce the sensitivity of each diagonal element. The structure to be controlled is a two degree of freedom flexible beam with variable coupling. The result of the control method is one single discrete decentralized controller able to robustly reject disturbances with nearly identical performance for a large variation in coupling and inertia.

Breast cancer in patients treated for Hodgkin's disease: clinical and pathological analysis of 76 cases in 63 patients.

In a retrospective multicentric analysis, 63 women treated between 1941 to 1988 for Hodgkin's disease (HD) subsequently developed 76 breast cancers (BC). The median age at diagnosis of HD was 26 years (range 7-67), and 22 women (35%) were 20 years old or less. Exclusive radiotherapy (RT) was used in 36 women (57%) and combined modalities with chemotherapy (CT) in 25 (39%). Breast cancer occurred after a median interval of 16 years (range 2-40) and the median age at diagnosis of the first BC was 42 years (range 25-73). TNM classification (UICC, 1978) showed 10 T0 (non-palpable lesions) (13%), 20 T1 (26%), 22 T2 (29%), 8 T3 (11%), 7 T4 (9%) and 9 Tx (12%), giving altogether a total of 76 tumours, including, respectively, 5 and 8 bilateral synchronous and metachronous lesions. Among the 68 tumours initially discovered, 53 ductal infiltrating, one lobular infiltrating and two medullary carcinomas were found. Moreover, two fibrosarcomas and 10 ductal carcinoma in situ (DCIS) were also found. Among 50 axillary dissections for invasive carcinomas, histological involvement was found in 31 cases (62%). 45 tumours were treated by mastectomy, without (n = 35) or with (n = 10) RT. 27 tumours had lumpectomy, without (n = 7) or with RT (n = 20). 2 others received RT only, and one only CT. 7 patients (11%) developed isolated local recurrence. 20 patients (32%) developed metastases and all died; 38 are in complete remission, whereas 5 died of intercurrent disease. The 5-year disease-specific survival rate by the Kaplan-Meier method was 61%. The 5-year disease-specific survival rate for pN0, pN1-3 and pN > or = 3 groups were 91%, 66% and 0%, respectively (P < 0.0001) and 100%, 88%, 64% and 23% for the T0, T1, T2 and T3T4 groups, respectively. These secondary BCs seem to be of two types: a large number of aggressive tumours with a very unfavourable prognosis (especially in the case of pN > 3 and/or T3T4); and many tumours with a 'slow development' such as DCIS and microinvasive lesions, especially in patients treated exclusively by RT. Moreover, a very unusual rate of bilateral tumours (21%) was observed. These secondary BC could be 'in field', in 'border of field' or 'out of field'. However, a complete analysis of doses delivered by supradiaphragmatic irradiation was often very difficult, due to large variations in several parameters. We conclude that young women and girls treated for HD should be carefully monitored by clinical examination, mammography and ultrasonography.

Adaptive control of arterial blood pressure with a learning controller based on multilayer neural networks.

We discuss a two-model multilayer neural network controller for adaptive control of mean arterial blood pressure (MABP) using sodium nitroprusside. A model with an autoregressive moving average (ARMA), representing the dynamics of the system, and a modified back-propagation training algorithm are used to design the control system to meet specified objectives of design (settling time and undershoot/overshoot) and clinical constraints. The controller is associated with a weighting-determinant unit (WDU) to determine and update the output weighting factor of the parallel two-model neural network for adequate control action and a control-signal modification unit (CMU) to comply with clinical constraints and to suppress the effect of adverse noise and to improve the WDU performance. Extensive computer simulations indicate satisfactory performance and robustness of the proposed controller in the presence of much noise, over the full range of plant parameters, uncertainties, and large variations of parameters.

High-performance robust motion control of machine tools: an adaptive robust control approach and comparative experiments

This paper studies the high-performance robust motion control of machine tools. The newly proposed adaptive robust control (ARC) is applied to make the resulting closed-loop system robust to model uncertainties, instead of the disturbance observer (DOB) design previously tested by many researchers. Compared to DOB, the proposed ARC has a better tracking performance and transient in the presence of discontinuous disturbances, such as Coulomb friction, and it is of a lower order. As a result, time-consuming and costly rigorous friction identification and compensation is alleviated, and overall tracking performance is improved. The ARC design can also handle large parameter variations and is flexible in introducing extra nonlinear robust control terms and parameter adaptations to further improve the transient response and tracking performance. An anti-integration windup mechanism is inherently built in the ARC and, thus, the problem of control saturation is alleviated. Extensive comparative experimental tests are performed, and the results show the improved performance of the proposed ARC.

Kinetics and Applications of Atomic Diffusion in Solids: Enormously Large Variations of Diffusion Parameters in S, P, Se, Bi and BaO. Kinetic Electron - Related Interpretation and Diffusion-Melting Correlations

Kinetics and Applications of Atomic Diffusion in Solids: Enormously Large Variations of Diffusion Parameters in S, P, Se, Bi and BaO. Kinetic Electron - Related Interpretation and Diffusion-Melting Correlations

The R-S-T Digital Controller Design and Applications

The two degree of freedom R-S-T digital controller is becoming a standard for computer control in industry. The paper presents the methodology for the design of the R-ST controller which involves identification of the plant model from data combined with a robust control design. The perfonnances of the controller can be fwther enhanced by plant model identification in closd loop and re-tuning of the controller. For large parameter variations adaptation has to be considered for preserving the perfonnances. Software packages are available for the design, implementation and commissioning of the R-S-T digital controllers. The methodology is illustrated by its application to the control of deposited zinc in hot-dip galvanizing at SOLLAC (Florange, France)

Phenotypic heterogeneity of osteoblast-like MC3T3-E1 cells: changes of bradykinin-induced prostaglandin E2 production during osteoblast maturation.

We have examined clonal murine calvarial MC3T3-E1 cells obtained from different sources to compare their osteoblastic features (alkaline phosphatase [ALP], cyclic adenosine monophosphate [cAMP] response to parathyroid hormone, prostaglandin E2 (PGE2) and PGE1, bradykinin-induced production of PGE2). It was found that the sublines investigated showed large variation of the above-mentioned parameters, which may be attributed to distinct differentiated stages of osteoblast development. Increase of ALP activity was paralleled by an increase in cAMP accumulation in response to the above-mentioned agents. The most striking difference was observed with bradykinin-induced production of PGE2. Early stage cells (low ALP) produced high levels of PGE2, whereas cells with high ALP activity showed no bradykinin stimulation at all. This was consistent with the results of specific binding of 3H-bradykinin to its receptor and also correlated well with the bradykinin-induced signal transduction sequence (inositol triphosphate liberation and elevation of intracellular calcium levels). This was confirmed by Northern blot analysis of bradykinin receptor mRNA expression. These results indicate that the widely used osteoblast-like cell line MC3T3-E1 is synonymous for multiple sublines, representing different stages of osteoblast development. These sublines were most likely emerging from the early stage cell line due to the applied culture conditions. Moreover, distinct biochemical features are displayed in correlation to the differentiation stage, thus providing a useful model to study the molecular mechanism of osteoblast maturation.

The dynamic analysis of mechanical systems and structures with large parameter variations

A general approach to the analysis of mechanical systems and structures where large parameter variations occur is outlined. Aircraft and missile problems where there are acknowledged changes in the system model coefficients are identified. Performance variations associated with operational conditions are investigated. Simple solutions to the determinantal equation are proposed.

Force Tracking in Impedance Control

This article presents two simple on-line schemes for force tracking within the impedance-control framework. The force- tracking capability of impedance control is particularly important for providing robustness in the presence of large uncertainties or variations in environmental parameters. The two proposed schemes generate the reference position trajec tory required to produce a desired contact force despite lack of knowledge of the environmental stiffness and location. The first scheme uses direct adaptive control to generate the refer ence position on-line as a function of the force-tracking error. Alternatively, the second scheme utilizes an indirect adaptive strategy in which the environmental parameters are estimated on-line, and the required reference position is computed based on these estimates. In both schemes, adaptation allows au tomatic gain adjustment to provide a uniform performance despite variations in the environmental parameters. Simula tion studies are presented for a 7-DOF Robotics Research arm using full arm dynamics, demonstrating that the adaptive schemes are able to compensate for uncertainties in both the environmental stiffness and location. The simulation studies also highlight the limitations of pure impedance control without the force-tracking capability for robust execution of realistic contact tasks. Experimental results are also presented for the Robotics Research arm to demonstrate that the end effector applies the desired contact force while exhibiting the specified impedance dynamics.

Sliding mode control of a DC/DC PWM converter with PFC implemented by neural networks

An experimental neural controller implementing a variable structure control (VSC) algorithm is proposed for a power factor preregulator. VSC control laws yield fast response and a robust behavior against large parameters variations. A multilayer perceptron learns through backpropagation to approximate the desired adaptive control functions. The main advantage of the neural network implementation in comparison to the numerical implementation is that decreases complexity and cost of the controller, and increases the switching frequency. A simple analog electronic realization of this neural network using discrete operational amplifiers is proposed. This implementation possesses all good properties of sliding mode while avoiding the unnecessary discontinuities of the control input signals and thus eliminating chattering. Experimental results are summarized confirming the validity of the neural network approach.

Antilock Braking System Using Sliding Mode Control.

Current control algorithms for antilock braking system (ABS) are mostly table-driven and should be frequently modified through various experiments, because the braking dynamics are highly nonlinear with system uncertaintieS. In this paper, a new control algorithm is proposed for ABS using the theory of sliding mode control based on a linearized system model, which represents nonlinearities as external disturbances. The proposed sliding mode controller guarantees a highly robust performance against large variations in the system parameters and disturbances. Furthermore, a road surface estimation scheme using a disturbance observer is proposed in order to determine the optimal reference input. Simulation results reveal the advantages of the proposed method in comparison with conventional linear controllers.

Errors and Uncertainties in Reservoir Performance Predictions

Summary While the uncertainty related to mapping/quantification of hydrocarbons initially in place is well understood, there are problems regarding the sources and propagation of errors/uncertainties in reservoir simulation. Because reservoir models use measured data from only a small fraction of the total reservoir volume, the challenge is to construct a reservoir model that uses the available data and minimizes errors in simulation results. Several recent studies have aimed at performing a total uncertainty analysis of reservoir simulation results. Underlying such work is usually a number of hypotheses/assumptions that are not always clearly expressed. In this paper, we will discuss implications of some of the statistical methods that are commonly applied in uncertainty analysis and construction of a geological model. The Bayesian approach, where additional data can reduce uncertainties, is emphasized. Several papers have demonstrated large variations in parameters obtained from routine and special core analysis on samples originating from the same geological building block (lithofacies). This variation, which sometimes may be difficult to dissolve from uncertainty in the measurements, must be accounted for in models that describe small scale variation.

Robust and efficient methods for stochastic finite element analysis using Monte Carlo simulation

In the present paper the weighted integral method and the Monte Carlo simulation are used together with innovative solution strategies based on the Preconditioned Conjugate Gradient method (PCG) to produce robust and efficient solutions for the stochastic finite element analysis of space frames. The numerical tests presented demonstrate the superiority of the proposed computational strategies compared to the widely used Neumann expansion method both in terms of accuracy and computational efficiency. The superiority is more pronounced in cases where the analysis needs to be performed for large variations of the stochastic parameters.