Cooperative estimation algorithms for multi-sensor networks with imprecise measurements

A cooperative estimation algorithm is proposed for mutli-sensor networks with imprecise measurements caused by electromagnetic interferences, abnormal currents and other faults in the multi-sensor measurement process. Adaptive schemes based on a reference model are introduced to overcome the adverse effects of multiplicative interference on the estimated information. Then, rigorous theoretical proofs are developed to analyze the adaptive estimation algorithm. Finally, numerical simulation results are carried out to verify the effectiveness of the theoretical analysis.

Optimization of the semi-active vibration absorbers

In this paper, an efficient numerical approach is proposed to maximize the minimal damping of modes in a prescribed frequency range for general viscous tuned-mass systems. Methods of decomposition and numerical synthesis are considered on the basis of the adaptive schemes. The influence of dynamic vibration absorbers and basic design elastic and damping properties is under discussion. A technique is developed to give the optimal DVA’s for the elimination of excessive vibration in sinusoidal and impact forced system. One task of this work is to analyze parameters identification of the dynamic vibration absorber and the basic structure. The questions of robustness at optimization of DVA are considered. Different types of control management for semi-active DVA’s are applied. Examples of DVA’s practical implementation are presented.

A Robust And Adaptive Approach To Control Of A Continuous Stirred Tank Reactor With Jacket Cooling

Continuous Stirred Tank Reactors (CSTR) are one of the main technological plants used in chemical and biochemical industry. These systems are quite complex with many nonlinearities and the conventional linear control with fixed parameters can be questionable or sometimes unacceptable. The solution should be found in so-called “non-traditional” control approaches like adaptive, robust, fuzzy, or artificial intelligent methods. One way is the utilization of self-tuning adaptive schemes, but computations may be quite difficult, clumsy and time-consuming. This paper brings an alternative principle called a robust approach and the comparison of the robust and adaptive control responses. Robust control considers a CSTR model as a linear system with parametric uncertainty, which covers a family of all feasible plants. Then several controllers with fix parameters are designed so that for all possible plants, the acceptable control behavior is obtained. The two-degree-of-freedom (2DOF) structure for the control law was chosen. Both robust and adaptive control is applied to an original nonlinear model of a CSTR. All calculations and simulations of mathematical models and control responses were performed in the Matlab and Simulink environment.

Renewable Distributed Generations Optimal Penetration in the Distribution Network for Clean and Green Energy

For raising the initiatives to supply clean and green energy globally, many renewable distributed generations are attached to the network. Power losses, voltage profile maintenance and environmental pollution are the most significant restrictions, which hinder the existing power system. Random penetration of the distributed generation in the existing network can cause severe problems like voltage instability, increase in power losses, system islanding, reverse power flows, environment pollution, etc. Therefore, for clean and green energy, optimal penetration of eco-friendly renewable distributed generation is required for power loss minimisation and voltage profile enhancement. Optimal penetration of renewable distributed generation has to deal with constraints like size, location, number, power factor and type. Adaptive schemes are based on biogeography-based optimisation and particle swarm optimization methods to satisfy all the constraints related to the optimal penetration of renewable distributed generation systems in the IEEE 33 bus radial distribution network. The adaptive schemes have been applied for (real and reactive) power loss reduction and enhancing voltage profile.

A METHOD FOR PREDICTING ENERGY-STABILIZED MOTION OF SPACECRAFT BASED ON DIFFERENTIAL TAYLOR TRANSFORMATIONS

To predict the motion of spacecrafts, a numerical-analytical method for integrating the differential equation of the orbital motion of a spacecraft stabilized by the Baumgart differential method is proposed. The stabilization of the differential equation of motion by the Baumgart method is carried out according to the energy of the spacecraft. Stabilization is carried out to reduce the influence of the Lyapunov instability on the accumulation of numerical errors in the integration of the differential equation, which is effective when conducting a long-term numerical prediction of the motion of spacecraft. Integration of the stabilized equation is based on differential Taylor transformations. Computational schemes with a constant step and an integration order are considered, as well as schemes with adaptation by an integration step and order. For adaptive schemes, the results of forecasting the motion of spacecraft according to the criterion “accuracy-computational complexity» for a given relative error of integration with respect to integration phase variables and spacecraft energy are presented. It is shown that both options require setting various internal adaptation parameters, but they have comparable efficiency. Recommendations are proposed on the use of the developed method for integrating energy-stabilized equations for predicting the motion of spacecraft in the near space in the Greenwich rectangular coordinate system.

Two-Stage Adaptive Pooling with RT-qPCR for COVID-19 Screening

We propose two-stage adaptive pooling schemes, 2-STAP and 2-STAMP, for detecting COVID-19 using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) test kits. Similar to the Tapestry scheme of Ghosh et al., the proposed schemes leverage soft information from the RT-qPCR process about the total viral load in the pool. This is in contrast to conventional group testing schemes where the measurements are Boolean. The proposed schemes provide higher testing throughput than the popularly used Dorfman’s scheme. They also provide higher testing throughput, sensitivity and specificity than the state-of-the-art non-adaptive Tapestry scheme. The number of pipetting operations is lower than state-of-the-art non-adaptive pooling schemes, and is higher than that for the Dorfman’s scheme. The proposed schemes can work with substantially smaller group sizes than non-adaptive schemes and are simple to describe. Monte-Carlo simulations using the statistical model in the work of Ghosh et al. (Tapestry) show that 10 infected people in a population of size 961 can be identified with 70.86 tests on the average with a sensitivity of 99.50% and specificity of 99.62%. This is 13.5x, 4.24x, and 1.3x the testing throughput of individual testing, Dorfman’s testing, and the Tapestry scheme, respectively.