Mechatronic systems identification using an impulse response recursive algorithm

Eight-poles of various designs, including an eight-terminal network with three input and five output terminals, are needed to replace some power objects. Especially when only the input and output characteristics of electrical energy are of interest. The paper presents the eightterminal network equations with three input and five output terminals, establishing a connection between these characteristics. Equations of the A-form establish a connection between the input and output voltages and currents; the B-form equations establish a connection between the output and input voltages and currents; G-form equations establish a connection between the input current, output voltages and output voltage, output currents; H-form equations establish the relationship between the input voltage, output currents and output current, output voltages; the Yform equations establish a connection between the input and output currents and the input and output voltages; The Z-form equations establish the relationship between the input and output voltages and the input and output currents. When implementing these equations, attention should be paid to the difference in the directions of the currents in each individual case.

Identification of an Induction Motor by a Microcomputer based Structure

Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity

This paper considers identification of Wiener systems for which the internal variables and output are corrupted by noises. When the internal noise is a sequence of independent and identically distributed (iid) Gaussian random variables, by the Weierstrass transformation (WT) the system under consideration turns to be a Wiener system without internal noise. The nonlinear part of the latter is nothing else than the WT of the nonlinear function of the original system, while the linear subsystem is the same for both systems before and after WT. Under reasonable conditions, the recursive identification algorithms are proposed for the transformed Wiener system, and strong consistency for the estimates is established. By using the inverse WT the nonparametric estimates for the nonlinearity of the original system are derived, and they are strongly consistent if the nonlinearity in the original system is a polynomial. Similar results also hold in the case where the internal noise is non-Gaussian. Simulation results are fully consistent with the theoretical analysis.

MIMO system identification is a fundamental concern in a variety of applications. Various iterative and recursive MIMO system identification algorithms exist in the literature. The iterative algorithms suffer from high computational cost due to large matrix computations and recursive algorithms suffer from slow convergence speed. This paper proposes a fast recursive exact least squares algorithm for MIMO system identification with fast convergence speed and low computational cost. The recursions of the algorithm give rise to a lattice structure. The lattice structure is less sensitive to round-off errors, coefficients variations and can also be used for model order reduction. Although in this work we estimate an FIR (finite impulse response) model of the MIMO system, the framework can also be used for IIR (infinite impulse response) models, and for estimating linear periodically time varying (LPTV) and multivariable systems. The theory proposed is validated using simulation results.

For ARMAX models, modified recursive extended least squares identification algorithms are presented. The basic idea lies in two aspects. One is to decompose the original system into two subsystems. The other is that the most recent information is used to update the parameters, which is different from the hierarchical principle. A simulation example is employed to test the effectiveness of the proposed algorithms.

A recursive algorithm for sampling properties of physical clusters such as size distribution and percolation is presented. The approach can be applied to any system with periodic boundary conditions, given a spatial definition of a cluster. We also introduce some modifications in the algorithm that increases the efficiency considerably if one is only interested in percolation analysis. The algorithm is implemented in Fortran 90 and is compared with a number of iterative algorithms. The recursive cluster identification algorithm is somewhat slower than the iterative methods at low volume fraction but is at least as fast at high densities. The percolation analysis, however, is considerably faster using recursion, for all systems studied. We also note that the CPU time using recursion is independent on the static allocation of arrays, whereas the iterative method strongly depends on the size of the initially allocated arrays.

In this thesis, we formulate Wiener and Kalman filtering for the optimal estimation and prediction of self-similar processes. Our algorithms are based on the scale stationary processes framework which provides a mathematically tractable system theoretic model enabling the development of many signal processing techniques. In this framework, self-similar processes are modeled using scale stationary Auto Regressive Moving Average (ARMA) models. It has been shown that such continuous-time ARMA systems can be processed by generalized Mellin and Scale Transforms, analogous to the use of the Laplace and Fourier Transforms in the continuous-time processing of ordinary stationary ARMA systems. In this work, we first attempt to discretize these models by using exponential sampling and then formulate discrete time algorithms for discrete time processing. These discrete time techniques are used in the implementation of the optimal Wiener filtering algorithm obtained for self-similar processes using least squares techniques. Wiener filtering algorithm in Discrete Generalized Scale Transform (DGST) domain is defined in terms of scale power spectral densities. Hence, we develop a periodogram-like method for the estimation of the power spectrum of self-similar processes using DGST. Wiener filtering algorithm for the restoration of self-similar processes is tested on simulation examples. Investigation of the state space analysis is needed to fully develop the self-similar ARMA processes framework. First, a general state space representation of self-similar ARMA processes is obtained based on first order time varying ordinary differential equations. It is shown that the major difference of this representation is in the scale memory content. Here, we introduced new concepts, i.e., the "multivariate self-similarity" of the states which is captured in the "self-similarity matrix". In this state space representation, the self-similarity of the outputs is represented as a linear combination of self-similar states having different self-similarity parameters. Secondly, we develop a recursive predictive estimation algorithm in the form of a Kalman filter for self-similar processes using the proposed state space representation. Simulation examples suggest that the proposed algorithm is superior to the traditional Kalman filtering technique when the input and the output processes are self-similar.

In this paper, a new modeling method of the variable speed variable pitch wind turbine is proposed. More specifically, the linear parameter varying model of wind turbines is optimized by utilizing the variable forgetting factor recursive least squares identification algorithm, which improves the accuracy of the online estimation of the aerodynamic parameters. As the power factor results in the nonlinear characteristics of the wind turbine, the nonlinear power factor is approximate to a polynomial function of the tip speed ratio and the pitch angle. Then the relationship between the aerodynamic parameters and the power factor can be deduced. Furthermore, the solving problem of aerodynamic parameters can be transformed into the identification of the power factor. It is depicted in the simulation results that the modeling method of wind turbines can be optimized from the proposed variable forgetting factor recursive least squares identification algorithm, and the controller obtained from the optimized model can improve stability.

THE PURPOSE. Currently, brushless motors – electric machines with permanent magnets on the rotor and a rotor position sensor controlled by a sinusoidal voltage from frequency converters, are widely used in mechatronic and robotic systems. The control algorithm is formed on the basis of information about the current values of the parameters brushless motor mechatronic system using a nominal or reference mathematical model, which is, as a rule, an idealized representation of a real device. The non-stationarity of the parameters object of study, as well as the possible uncertainty of its mathematical description due to the simplification of the mathematical model, lead to undesirable or unacceptable results when forming the control algorithm of the mechatronic system. The problem arises of analyzing the dynamic characteristics of a brushless motor under conditions of parametric uncertainty in order to determine the parameters that most affect the functioning of the mechatronic system and the phase coordinates that are sensitive to these changes.METHODS. When solving the problem, methods of the sensitivity theory are used to obtain the corresponding vector-matrix equations, the solution of which is carried out by means of the MatLab software environment.RESULTS. In this paper, sensitivity equations are obtained for the active resistance and projections of the stator winding inductance on the longitudinal and transverse coordinate axes, as well as for the moment of inertia of the brushless motor. A vector-matrix block diagram for calculating the sensitivity functions of a brushless motor is formed, the characteristic feature of which is the presence of a non-zero matrix of free terms, reduced to the input of the sensitivity model. The corresponding Simulink models were built to study the influence of the listed quasi-stationary parameters on the rotation speed and torque on the shaft of the object of study. An analysis of the statistical characteristics additional motion of the specified phase coordinates of the brushless motor has been carried out, and graphical dependencies and steady-state values of dispersions and relative estimates have been obtained.CONCLUSION. An analysis of the dynamic characteristics of a brushless motor under conditions of parametric uncertainty made it possible to determine that the rotation speed of the machine is the most sensitive to parametric disturbances. This coordinate is the most informative and is of maximum interest in the formation of an optimization algorithm for a mechatronic system. The decisive role in the formation of the additional movement of the output coordinates of the brushless motor is made by a change in the projection of the stator inductance on the transverse coordinate axis, which is an order of magnitude greater than the contribution to the additional movement of coordinates from other unstable parameters. It is expedient to use the results obtained in the course of the study when constructing an optimal control algorithm for a mechatronic system under conditions of parametric uncertainty.

The DC motors an outstanding portion of apparatus in automotive and automation industrial applications requiring variable speed and load characteristics due to its ease of controllability. Creating an interface control system for multi DC motor drive operations with centralized speed control, from small-scale models to large industrial applications much demand. By using Lab VIEW (laboratory virtual instrument engineering workbench) as the motor controller, can control a DC motor for multiple purposes using single software environment. The aim of this paper is to propose the centralized speed control of DC motor using Lab VIEW. Here, the Lab VIEW is used for simulating the motor, whereas the input armature voltage of the DC motor is controlled using a virtual Knob in Lab VIEW software. The hardware part of the system (DC motor) and the software (in personal computer) are interfaced using a data acquisition card (DAQ) -Model PCI- 6024E. The voltage and Speed response is obtained using LABVIEW software. Using this software, group of motors’ speed can be controlled from different location using remote telemetry. The propose work also focuses on controlling the speed of the individual DC motor using PWM scheme (Duty cycle based Square wave generation) and DAQ. Help of the DAQ along with Lab VIEW front panel window, the DC motor speed and directions can be change easily in remote way. In order to test the proposed system the laboratory model for an 80W DC motor group (multi drive) is developed for different angular displacements and directions of the motor. The simulation model and experimental results conforms the advantages and robustness of the proposed centralized speed control.

The implementation of PSO algorithm in optimizing PID controller's parameters has been in active research area recently. One of the popular plant to be controlled using this control strategy is a DC motor and various types of DC motors with variations in parameters value have been used in this research area. Moment of inertia is one of the important parameters of a DC motor which can affect the output transient response including rise time, settling time, overshoot and steady state error. This paper investigate the moment of inertia as a significant factor on the performance of PSO algorithm utilizing the weighted transient response index (WTRI) based fitness function. Some popular weight combinations of WTRI were used in the experiment with 3 DC motors with the same specifications except for the moment of inertia. The experimental result concluded the existence of some kind of threshold moment of inertia where the performance of PSO algorithm was not affected by any variation of weight combinations in WTRI based fitness function if the moment of inertia of the DC motor is less than the threshold value. With moment of inertia above this threshold value, the significant variation in the performance of PSO algorithm with different weight combinations in WTRI based fitness function can be seen.

When bullwhip increases in the lead time: An eigenvalue analysis of ARMA demand

In this work, we develop an estimation model for the future tendency of worldwide energy-associated carbon dioxide emissions. The presented model is based on the past statistics of the energy-related carbon dioxide emissions worldwide from 1975 to 2018. We employ the auto-regressive moving average (ARMA) model with the best possible estimation model order which provides the highest level of confidence in generating and estimating the time-series of worldwide energy-related carbon dioxide emissions. Thus, ARMA (3, 3) model has been successfully used to model and estimate the short-term future values for the worldwide energy-related carbon dioxide emissions for the next five years 2019–2023. The simulation results showed that ARMA (3, 3) has was the optimal estimation model order since it recorded the minimum estimation error with highest level of confidence listed as 86.1%. Finally, in 2018, the energy-related CO2 emissions reached 33.14 gigatonnes then the estimation results showed a linear increasing trend in the short future of annual released numbers of the energy-associated carbon dioxide emissions (in gigatonnes) which is expected to reach 35 gigatonnes in 2023.

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.