The consideration of uncertainties in numerical simulation is generally reasonable and is often indicated in order to provide reliable results, and thus is gaining attraction in various fields of simulation technology. However, in multibody system analysis uncertainties have only been accounted for quite sporadically compared to other areas.

The term uncertainties is frequently associated with those of random nature, i.e. aleatory uncertainties, which are successfully handled by the use of probability theory. Actually, a considerable proportion of uncertainties incorporated into dynamical systems, in general, or multibody systems, in particular, is attributed to so-called epistemic uncertainties, which include, amongst others, uncertainties due to a lack of knowledge, due to subjectivity in numerical implementation, and due to simplification or idealization. Hence, for the modeling of epistemic uncertainties in multibody systems an appropriate theory is required, which still remains a challenging topic. Against this background, a methodology will be presented which allows for the inclusion of epistemic uncertainties in modeling and analysis of multibody systems. This approach is based on fuzzy arithmetic, a special field of fuzzy set theory, where the uncertain values of the model parameters are represented by socalled fuzzy numbers, reflecting in a rather intuitive and plausible way the blurred range of possible parameter values. As a result of this advanced modeling technique, more comprehensive system models can be derived which outperform the conventional, crisp-parameterized models by providing simulation results that reflect both the system dynamics and the effect of the uncertainties.

The methodology is illustrated by an exemplary application of multibody dynamics which reveals that advanced modeling and simulation techniques using some well-thought-out inclusion of the presumably limiting uncertainties can provide significant additional benefit.

As a result of the development of modern vehicles, even higher accuracy standards are demanded. As known, Inertial Navigation Systems have an intrinsic increasing error which is the main reason of using integrating navigation systems, where some other sources of measurements are utilized, such as barometric altimeter due to its high accuracy in short times of interval. Using a Robust Kalman Filter (RKF), error measurements are absorbed when a Fault Tolerant Altimeter is implemented. During simulations, in order to test the Nonlinear RKF algorithm, two kind of measurement malfunction scenarios have been taken into consideration; continuous bias and measurement noise increment. Under the light of the results, some recommendations are proposed when integrated altimeters are used.

The paper focuses on the problem of robust fault detection using analytical methods and soft computing. Taking into account the model-based approach to Fault Detection and Isolation (FDI), possible applications of analytical models, and first of all observers with unknown inputs, are considered. The main objective is to show how to employ the bounded-error approach to determine the uncertainty of soft computing models (neural networks and neuro-fuzzy networks). It is shown that based on soft computing models uncertainty defined as a confidence range for the model output, adaptive thresholds can be described. The paper contains a numerical example that illustrates the effectiveness of the proposed approach for increasing the reliability of fault detection. A comprehensive simulation study regarding the DAMADICS benchmark problem is performed in the final part.

Redundancy based methods are proactive scheduling methods for solving the Project

Scheduling Problem (PSP) with non-deterministic activities duration. The fundamental

strategy of these methods is to estimate the activities duration by adding extra time to the

original duration. The extra time allows to consider the risks that may affect the activities

durations and to reduce the number of adjustments to the baseline generated for the project.

In this article, four methods based on redundancies were proposed and compared from two

robustness indicators. These indicators were calculated after running a simulation process.

On the other hand, linear programming was applied as the solution technique to generate

the baselines of 480 projects analyzed. Finally, the results obtained allowed to identify the

most adequate method to solve the PSP with probabilistic activity duration and generate

robust baselines.

In the hybrid multiple H-bridge topology of beam supply, the load change of a DC/DC full-bridge converter can greatly affect the output voltage during onsite operation. An improved sliding mode control (SMC) strategy is thus proposed in this paper, where the rate of switching control is added to the law of system equivalent control to create a law that can realize a complete sliding mode control. Considering the special operating conditions of the load can have an influence on the performance of the controller, the impact of uncertainty existing in onsite conditions is suppressed with the proposed strategy utilized. The validity of the proposed strategy, finally, is verified by simulation, which proves the outperformance of the system in both robustness and dynamics.

This work proposes an optimum design and implementation of fractional-order Butterworth filter of order (1 + α), with the help of analog reconfigurable field-programmable analog array (FPAA). The designed filter coefficients are obtained after dual constraint optimization to balance the tradeoffs between magnitude error and stability margin together. The resulting filter ensures better robustness with less sensitivity to parameter variation and minimum least square error (LSE) in magnitude responses, passband and stopband errors as well as a better –3 dB normalized frequency approximation at 1  rad/s and a stability margin. Finally, experimental results have shown both lowpass and highpass fractional step values. The FPAA-configured outputs represent the possibility to implement the real-time fractional filter behavior with close approximation to the theoretical design.

This article takes up the matter of contemporary threats to cities and urbanity, setting the problems cities face today against the background of the two categories of the resilient city and the city developing sustainably. The author describes and presents the evolution of the sustainable development concept as such, as well as the generational change in priorities that has taken place where the development of urbanised areas is concerned, given the way the concept has undergone a certain devaluation, in the light of its failure to achieve fulfi lment. The challenges cities face today require multi-faceted activity, in respect of increased inclusivity, robustness and resilience, and flexibility. This leaves today’s idea of the resilient city embracing old elements of the sustainable city, but also augmenting them in various ways.

Although the explicit commutativitiy conditions for second-order linear time-varying systems have been appeared in some literature, these are all for initially relaxed systems. This paper presents explicit necessary and sufficient commutativity conditions for commutativity of second-order linear time-varying systems with non-zero initial conditions. It has appeared interesting that the second requirement for the commutativity of non-relaxed systems plays an important role on the commutativity conditions when non-zero initial conditions exist. Another highlight is that the commutativity of switched systems is considered and spoiling of commutativity at the switching instants is illustrated for the first time. The simulation results support the theory developed in the paper.