The fixed-point theorem is widely used in different engineering applications. The present paper focuses on its applications in optimisation. A Matlab toolbox, chich implements the branch-and-bound optimisation method based on the fixed-point theorem, is used for solving different real-life test problems, including estimation of model parameters for the Jiles-Atherton model.

In this paper, we propose a new method of measuring the target velocity by estimating the scaling parameter of a chaos-generating system. First, we derive the relation between the target velocity and the scaling parameter of the chaos-generating system. Then a new method for scaling parameter estimation of the chaotic system is proposed by exploiting the chaotic synchronization property. Finally, numerical simulations show the effectiveness of the proposed method in target velocity measurement.

Vehicle parameters have a significant impact on handling, stability, and rollover propensity. This study demonstrates two methods that estimate the inertia values of a ground vehicle in real-time.

Through the use of the Generalized Polynomial Chaos (gPC) technique for propagating the uncertainties, the uncertain vehicle model outputs a probability density function for each of the variables. These probability density functions (PDFs) can be used to estimate the values of the parameters through several statistical methods. The method used here is the Maximum A-Posteriori (MAP) estimate. The MAP estimate maximizes the distribution of P(β|z) where β is the vector of the PDFs of the parameters and z is the measurable sensor comparison.

An alternative method is the application of an adaptive filtering method. The Kalman Filter is an example of an adaptive filter. This method, when blended with the gPC theory is capable at each time step of updating the PDFs of the parameter distributions. These PDF’s have their median values shifted by the filter to approximate the actual values.

The paper presents a method for estimation of converter drive parameters. This estimation encompassed three types of drives, i.e. a static Scherbius drive, a driver with a brushless direct current (BLDC) motor and a drive with a voltage inverter. For drive modelling and parameter estimation, the author implemented original program mes written in FORTRAN. As well as these, the paper describes an objective function applied for the estimation. The author also compares gradient and gradientless methods, chich are applied for minimization of the objective function. Finally, the author explains the estimation results for example drives, focusing on the coincidence of theoretical and empirical waveforms. The abovementioned procedure led to the general rule, which facilitates estimation efficiency.

Specific requirements are designed and implemented in electronic and telecommunication systems for received signals, especially high-frequency ones, to examine and control the signal radiation. However, as a serious drawback, no special requirements are considered for the transmitted signals from a subsystem. Different industries have always been struggling with electromagnetic interferences affecting their electronic and telecommunication systems and imposing significant costs. It is thus necessary to specifically investigate this problem as every device is continuously exposed to interferences. Signal processing allows for the decomposition of a signal to its different components to simulate each component. Radiation control has its specific complexities in systems, requiring necessary measures from the very beginning of the design. This study attempted to determine the highest radiation from a subsystem by estimating the radiation fields. The study goal was to investigate the level of radiations received and transmitted from the adjacent systems, respectively, and present methods for control and eliminate the existing radiations.

The proposed approach employs an algorithm which is based on multi-component signals, defect, and the radiation shield used in the subsystem. The algorithm flowchart focuses on the separation and of signal components and electromagnetic interference reduction. In this algorithm, the detection process is carried out at the bounds of each component, after which the separation process is performed in the vicinity of the different bounds. The proposed method works based on the Fourier transform of impulse functions for signal components decomposition that was employed to develop an algorithm for separation of the components of the signals input to the subsystem.

Together with the dynamic development of modern computer systems, the possibilities of applying refined methods of nonparametric estimation to control engineering tasks have grown just as fast. This broad and complex theme is presented in this paper for the case of estimation of density of a random variable distribution. Nonparametric methods allow here the useful characterization of probability distributions without arbitrary assumptions regarding their membership to a fixed class. Following an illustratory description of the fundamental procedures used to this end, results will be generalized and synthetically presented of research on the application of kernel estimators, dominant here, in problems of Bayes parameter estimation with asymmetrical polynomial loss function, as well as for fault detection in dynamical systems as objects of automatic control, in the scope of detection, diagnosis and prognosis of malfunctions. To this aim the basics of data analysis and exploration tasks - recognition of outliers, clustering and classification - solved using uniform mathematical apparatus based on the kernel estimators methodology were also investigated