Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation

The process of electrodeposition can be described in terms of a reaction-diffusion partial differential equation (PDE) system that models the dynamics of the morphology profile and the chemical composition. Here we fit such a model to the different patterns present in a range of electrodeposited and electrochemically modified alloys using PDE constrained optimization. Experiments with simulated data show how the parameter space of the model can be divided into zones corresponding to the different physical patterns by examining the structure of an appropriate cost function. We then use real data to demonstrate how numerical optimization of the cost function can allow the model to fit the rich variety of patterns arising in experiments. The computational technique developed provides a potential tool for tuning experimental parameters to produce desired patterns.     

A review on MRAS-type speed estimators for reliable and efficient induction motor drives

Induction machines have recently been very popular in variable-speed drives, because of their robust construction and relatively low manufacturing costs (brushless), maintenance-free and well-matured control methods. However, for high-precision control and efficiency optimization one needs the information on the rotor speed which can be measured using different speed sensors. All sensors require a mounting space and cabling, they also generate extra costs and reduce system reliability. Therefore, many of the recent research efforts have been dedicated to sensorless or encoderless electrical drives offering such considerable advantages as: lower cost, reduced size and hardware complexity of the drive system, elimination of sensor cables, lower maintenance requirements, possible operation in aggressive environment, higher noise immunity, reliable and user friendly operation. In this article all well-known sensorless techniques are shortly addressed, but the main focus is on the solutions based on the Model Reference Adaptive System (MRAS) concept. The mathematical models and schemes of all types of MRAS-type speed estimators known from the literature are gathered in this article. The comparative analysis of these speed estimators is done from the following points of view: the speed adaptation mechanism derivation based on the Lyapunov theory, stability problems near zero speed and in the regenerating operation mode, and the sensitivity of MRAS estimators to induction machine parameter changes.     

Preparation,characterization and investigation of molecular films coated on diamond-like carbon substrate

For the successful application of boundary lubrication, detailed investigations about the influence of preparation process on molecular films are needed. In this paper, a specially designed device was used for the film preparation. The scanning electron microscope (SEM) combined with atomic force microscope (AFM) was employed to characterize the surface morphology and nanotribological behavior of molecular films. After the liquid phase deposition, molecular films are randomly and densely distributed over Ti-doped diamond-like carbon (Ti-DLC) substrates. Through rigorous surface treatments, island-like molecular films were finally achieved on substrate surfaces. The surface friction of molecular films is obviously lower than that of Ti-DLC surfaces. Then, pin-on-disk tribotests were performed to study the macrofriction behavior of molecular films under different preparation parameters. Based on the orthogonal experiment, the effect of five preparation parameters (solution weight percent, smearing force and processing time of three smearing steps) on initial friction coefficient of molecular films was investigated. The results indicated that the order of significance levels is as follows: processing time of the second smearing step > solution weight percent > processing time of step 1 > processing time of step 3 > smearing force. For the purpose of friction reduction, the appropriate level ranges are 0.75% (Solution), 2.5 N–15 N (Force), 1 min–10 min (Step 1), 1 min–2 min (Step 2) and 1 min, 2 min, 5 min and 15 min (Step 3). The initial friction coefficient under the optimized conditions is around 0.112, and the equilibrium friction coefficient is around 0.162, which is lower than that of unlubricated Ti-DLC substrates.     

Methodology for dependence-based integrated constitutive modelling: An illustrative application to SiCp/Al composites

In industrial forming and machining process, the large plastic deformation of material takes place in wide loading ranges of strain-rate and forming temperature. A satisfactory modelling of quasi-static and dynamic material behaviors is of great importance for understanding physical process and processes optimization. A dependence-based integrated methodology, together with an improved weighted multi-objective parameter identification strategy is presented for the development of phenomenological constitutive model and the parameter identification using experimental data from quasi-static and dynamic tests with instantaneous strain rate variations and plastic strain-related temperature changes. The improved multi-objective parameter identification model is reformulated by introducing three weighting factors for valuing different measure errors and fit standard errors in individual objective function corresponding to each test, considering the sampling point number and active material parameter number under different loading conditions, and balancing optimization opportunity of quasi-static and dynamic sub-objective functions. The methodology is verified for feasibility through illustrative constitutive identification for SiC_p/Al composites. This may provide a methodology of constitutive modelling for predicting material behaviors in quasi-static and dynamic modes equally well.     

State feedback integral control for a rotary direct drive servo valve using a Lyapunov function approach

This paper concerns a state feedback integral control using a Lyapunov function approach for a rotary direct drive servo valve (RDDV) while considering parameter uncertainties. Modeling of this RDDV servovalve reveals that its mechanical performance is deeply influenced by friction torques and flow torques; however, these torques are uncertain and mutable due to the nature of fluid flow. To eliminate load resistance and to achieve satisfactory position responses, this paper develops a state feedback control that integrates an integral action and a Lyapunov function. The integral action is introduced to address the nonzero steady-state error; in particular, the Lyapunov function is employed to improve control robustness by adjusting the varying parameters within their value ranges. This new controller also has the advantages of simple structure and ease of implementation. Simulation and experimental results demonstrate that the proposed controller can achieve higher control accuracy and stronger robustness.     

Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems

In this work, novel application of evolutionary computational heuristics is presented for parameter identification problem of nonlinear Hammerstein controlled auto regressive auto regressive (NHCARAR) systems through global search competency of backtracking search algorithm (BSA), differential evolution (DE) and genetic algorithms (GAs). The mean squared error metric is used for the fitness function of NHCARAR system based on difference between actual and approximated design variables. Optimization of the cost function is conducted with BSA for NHCARAR model by varying degrees of freedom and noise variances. To verify and validate the worth of the presented scheme, comparative studies are carried out with its counterparts DE and GAs through statistical observations by means of weight deviation factor, root of mean squared error, and Thiel’s inequality coefficient as well as complexity measures.     

Parameter Estimation for Incompressible Cake Filtration: Advantages of a Modified Fitting Method

There is a widely used linear strategy to determine the parameters specific cake resistance and filter medium resistance in incompressible cake filtration. In this article, it is intended to demonstrate that this strategy has some disadvantages and should be replaced by an alternative nonlinear approach which yields more exact results. Even though the gains in precision are small for most cases, the nonlinear strategy is favored because it involves no extra effort and is grounded in the same physical theory as the original approach. This claim is based on a broad simulation study using noisy data with known parameter values to compare both fitting strategies and judge their accuracies.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

Tuning of Active Disturbance Rejection Control with application to power plant furnace regulation

Active Disturbance Rejection Control (ADRC) is emerging as a promising solution in dealing with the unmeasurable disturbances and unknown uncertainties, which are treated in a lumped manner and augmented as an extended state variable. Subsequently, an extended state observer (ESO) is designed to estimate and cancel the combined uncertain term in real time, modifying the uncertain plant to behave like a nominal model consisting of integrators. In the original ADRC formulation, the plant model is assumed to be of delay-free and its order is assumed to be equal to that of the real plant. However, a low-order ADRC is preferred and received a wide acceptance in practice because of its simplicity. Currently, the feasibility of such practice is not clearly revealed as well as its potential dangers. To this end, this paper analyzes the control mechanism from the perspective of the modified plant, which, in turn, would give guidance to parameter tuning. The effect of each parameter on the compensation efficiency and stability conditions of the modified plant is analyzed, based on which a complete tuning procedure for ADRC is developed where the initial settings is derived from the existing PI controller parameters. Finally, the proposed tuning method is experimentally used for a furnace pressure regulation of a 1000MW power plant, validating the feasibility of the low-order ADRC, even in the absence of both dynamic model and the information on the model order.