White Noise Disturbance Research Articles

Novel parameter estimation method for the systems with colored noises by using the filtering identification idea

Compared with the systems with white noise disturbances, the parameter identification of the systems with colored noises (i.e., correlated noises) is more difficult. In this letter, we use the model transformation to study the identification problem for the systems with colored noises by using the filtering identification idea. The basic idea is to transform a system with colored noise into two identification models with white noises and then to propose a novel parameter estimation algorithms, which can generate more accurate parameter estimates than some related existing identification algorithm. The proposed method can be applied to other linear or nonlinear stochastic systems with different structures and colored disturbance noises. The provided simulation example tests the proposed algorithm.

Automated modal parameter estimation for coupled rotor–foundation systems using seal forces as excitation source

Modal analysis techniques are widely used to characterize vibrations in structures, vehicles, machinery, etc. Modal parameters obtained through modal analysis are useful for structural health monitoring and condition monitoring. The common goal is to ensure the integrity of a mechanical system through monitoring of different operational conditions. Operational conditions described by spatial measurements can for instance be used to observe the vibration characteristics of the system and its parts. Therefore, the main originality of this work is to experimentally investigate if the presence of a turbulent gas flow in seal geometries can facilitate operational modal analysis for a coupled rotor–foundation system. A consistent and automatic way of obtaining the vibration characteristics is necessary to be included in the monitoring schemes. Therefore, this work also proposes a method to automatically obtain modal parameters for a bed plate of a rotor–foundation system when the rotor is affected by active magnetic bearings and gas seals. The automatic modal parameter algorithm is built on the stabilization diagram of modal parameters obtained from the stochastic subspace identification. Intermediate results from the automatic modal parameter algorithm show that some of the distance measures i.e., dωn,d, dζ, dMACX, dλ, dMACXωn,d, dMACXζ, and dMACXλ, for modal parameters in a stabilization diagram are better suited than others when distinguishing between possible physical modes and certainly spurious poles. The frequency band of interest for the system is found to influence the number of possible physical modes identified by the automatic algorithm. Measurements from 13 roving accelerometers are combined with the automatic modal parameter algorithm to obtain the vibration characteristics of the rotor and bed plate. For different operational conditions in the seal geometry, seven modes are consistently identified as physical modes by the automatic modal parameter algorithm. The algorithm uses (i) a combination of a white noise disturbance pattern intentionally introduced into the system through the active magnetic bearings and (ii) the operational disturbance coming from the seals, non-intentionally introduced by the turbulent gas flow. The seven physical modes are also identified when (iii) the rotor–foundation system is only disturbed from the turbulent gas flow in the seals. The seven physical modes identified by the algorithm under conditions (i), (ii), and (iii) compare well to the vibration characteristics found in experiments via experimental modal analysis and impact hammer testing as well as via a mathematical model of the rotor–foundation system.

Load model of high power magnet power supply for EAST based on GMPSO-BP neural network

To provide an accurate dynamic load model for the stability analysis of Tokamak, a method of back propagation (BP) neural network employing particle swarm optimization(PSO) algorithm based on mutation of Gaussian white noise disturbance (GMPSO-BP) is recommended. This method performs high-precision fitting using the GMPSO-BP neural network on the measured data of the experimental advanced superconducting Tokamak (EAST) poloidal field magnet power supply, extracts network parameters to build a dynamic load model, and then compares the simulation results with the measured data to calculate the error coefficient. The model is a component of the EAST distribution network's digital simulation model. An analogous load model is employed instead of developing the poloidal field magnet power supply from the mechanism. The revised algorithm has quick convergence times, good initial value adaptability, and error function accuracy of 0.3 to 3 %. The simulation outcomes show that the approach has a greater training effect.

Research on the Control Strategy for Handling Stability of Electric Power Steering System with Active Front Wheel Steering Function

<div>Due to the presence of uncertain disturbances in the actual steering system, disturbances in the system may affect the handling stability of the vehicle. Therefore, this article proposes an integrated steering system control strategy with stronger anti-disturbance performance. When disturbances exist in the system, the proposed control strategy effectively reduces the attitude changes during the vehicle steering process. In the upper-level control strategy, a variable transmission ratio curve is designed to coordinate the high-speed handling stability and low-speed steering sensitivity of the vehicle. On this basis, a sideslip angle observer is proposed based on the extended state observation theory, which does not depend on an accurate system model, thus determining the intervention timing of the active front wheel steering system. In the lower-level control strategy, DR-PI/DR-PID controllers are designed for the integrated steering system. Finally, experiments are conducted in the CarSim/Simulink joint simulation environment. The results indicate that compared to traditional PI/PID controllers and advanced ADRC controllers, DR-PI/DR-PID controllers can effectively suppress step disturbances, sinusoidal disturbances, and white noise disturbances while ensuring the dynamic response characteristics of the system. Under double-lane conditions, the root mean square values of the yaw rate and sideslip angle decrease by 4.35% to 12.3% and 3.21% to 7.72%, respectively. Therefore, the designed control strategy can improve handling stability while ensuring the robustness of the system.</div>

Neuro-Fuzzy-Based Adaptive Control for Autonomous Drone Flight

Adaptive control is the capability of a control system to modify its operation and achieve the best possible operation mode. A quadcopter is a nonlinear, unstable and under-actuated dynamic system, thus providing a challenge to control engineers in controlling and stabilising it during flight. This paper proposes the design, development, and application of an intelligent adaptive hybrid controller to control and stabilise the drone. The training data for adaptive neuro-fuzzy inference systems (ANFIS) are generated by the Linear Quadratic Regulator (LQR) under white-noise disturbance. The trained ANFIS is subsequently used to estimate the parameters of the control distribution matrix for the actual fault condition and the reconfiguration is carried out by computing new feedback gain using the pseudo-inverse technique. For the simple adaptive controller, LQR is also used to generate the desired trajectories of the reference model. In both experiments, the extended Kalman filter is implemented due to its non-linearity benefit. We demonstrate the performance of the proposed approach as a representative case study. The preliminary numerical simulation results further indicate that the proposed method is promising compared to conventional control techniques to control and stabilise a quadcopter drone.

Dynamics analysis of a stochastic SIRVI epidemic model with weak immunity

In this paper, we proposed and analyzed a new stochastic SIRVI epidemic model considering weak immunity generated after vaccination. The dynamics of the model is studied under white noise disturbance. We proved that the disease will eventually disappear if R1s<1, and the disease is persistent if R2s>1. Moreover, we found that the appearance of stochastic environmental disturbance can affect the transmission of infectious diseases, and relatively high noise intensity can suppress the spread of diseases under certain conditions.

A General Deep Learning Method for Computing Molecular Parameters of a Viscoelastic Constitutive Model by Solving an Inverse Problem.

Prediction of molecular parameters and material functions from the macroscopic viscoelastic properties of complex fluids are of great significance for molecular and formulation design in fundamental research as well as various industrial applications. A general learning method for computing molecular parameters of a viscoelastic constitutive model by solving an inverse problem is proposed. The accuracy, convergence and robustness of a deep neural network (DNN)-based numerical solver have been validated by considering the Rolie-Poly model for modeling the linear and non-linear steady rheometric properties of entangled polymer solutions in a wide range of concentrations. The results show that as long as the DNN could be trained with a sufficiently high accuracy, the DNN-based numerical solver would rapidly converge to its solution in solving an inverse problem. The solution is robust against small white noise disturbances to the input stress data. However, if the input stress significantly deviates from the original stress, the DNN-based solver could readily converge to a different solution. Hence, the resolution of the numerical solver for inversely computing molecular parameters is demonstrated. Moreover, the molecular parameters computed by the DNN-based numerical solver not only reproduce accurately the steady viscoelastic stress of completely monodisperse linear lambda DNA solutions over a wide range of shear rates and various concentrations, but also predict a power law concentration scaling with a nearly same scaling exponent as those estimated from experimental results.

Simultaneous detection of CH4 and CO2 through dual modulation off-axis integrated cavity output spectroscopy

This study established a novel method for the simultaneous detection of two-component gases. Radio frequency (RF) white noise disturbance laser current and wavelength modulation were simultaneously used to improve the off-axis integrated cavity output spectroscopy technique, and a high-precision dual modulation OA-ICOS (RF-WM-OA-ICOS) system was established. The two laser beams were coupled into one laser beam that was applied incident to the cavity of RF-WM-OA-ICOS system. The second harmonic signals of CH4 and CO2 gas simultaneously appeared in the rising or falling edge of a triangular wave. This method was used to measure CH4 and CO2 with different concentrations. The results indicated that the proposed system has high stability and can accurately and simultaneously measure the concentrations of CH4 and CO2, with an optimal integration time of 220 s. The minimum detection limit was 10 ppb for CH4 and 1.5 ppm for CO2. The corresponding noise equivalent absorption sensitivity values were calculated as 2.67 × 10−13 cm−1⋅Hz−1/2 and 5.18 × 10−11 cm−1⋅Hz−1/2, respectively. The proposed dual-component gas simultaneous detection method can also be used for high-precision simultaneous detection of other gases. Therefore, this study may serve as a reference for developing portable multicomponent gas analyzers.

Study on Setpoint Tracking Performance of the PID SISO and MIMO Under Noise and Disturbance for Nonlinear Time-Delay Dynamic Systems

This paper presents a case study of the setpoint tracking performance of the proportional integral derivative (PID) controller on the Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) nonlinear digital plants under Gaussian white noise and constant load disturbance for the nonlinear time-delay dynamic system. With the objective of getting a better understanding of the nonlinear discrete-time PID controller, we proposed a case study using two SISO and two MIMO digital plants, and then do the numerical simulations along with the addition of Gaussian white noise and load disturbance to simulate the real environment. In this paper, we compare the results of the system working with and without noise and load disturbance. The study result of this paper shows that on the discrete-time digital nonlinear plant, the PID controller is working well to follow the nonlinear setpoint even under heavy noise and load disturbance. The study compared the performance indexes of the controllers in terms of the maximum error, the Root mean square error (RMSE), the Integral square error (ISE), the Integral absolute error (IAE), and the Integral of time-weighted absolute error (ITAE).

Consensus of nonlinear multiagent systems with uncertain disturbance under stochastic switching topologies

In this paper, we investigate the mean square consensus problem of leader‐following nonlinear multiagent systems which suffer from white noise disturbance under randomly switching interaction topologies. The control protocol of each agent is designed based on its local information from its neighbors and the leader. Using matrix inequality theory and stochastic stability theory, a sufficient condition is presented to guarantee the mean square consensus. Moreover, two special cases: balanced topologies switching randomly and random connection failures are considered. Finally, two corresponding numerical simulations are provided to demonstrate the theoretical results.

A stochastic goodwill model depending on quality level and advertising

This paper focuses on a stochastic goodwill model involving a single manufacturer and a single retailer in a supply chain, in which both quality level and goodwill level are governed by stochastic differential equations. In both the linear-quadratic Stackelberg game and linear-quadratic cooperative game, explicit expressions are obtained for the optimal quality improvement efforts level of the manufacturer, the optimal advertising efforts level of the retailer as well as the optimal profit of the supply chain. We also derive the expectation and variance values for quality and goodwill levels, respectively. The distribution of incremental profit and subsidy are analysed in the cooperative game via the utility theory. Finally, several numerical experiments are reported to show the influence of decay rates and white noise disturbance of the quality and goodwill levels on the expectation of quality and goodwill levels.

A novel hybrid energy management strategy of a diesel-electric hybrid ship based on dynamic programing and model predictive control

The energy-saving characteristics of diesel-electric series hybrid ships largely depend on their energy management strategy. In this paper, a strategy that combines dynamic programing and model predictive control (DP-MPC) is proposed to solve the energy management problems of diesel-electric hybrid ships. The DP-MPC strategy has considered some typical working conditions of a ship, and the corresponding influence of white noise disturbance on the control strategy was studied. The simulation results show that the DP-MPC strategy has an excellent anti-interference capability. The control performance of the DP-MPC strategy is then further analyzed and compared with the rule-based logic threshold control strategy. The simulation results show that the proposed DP-MPC strategy can save 2.5% of the fuel consumption and has a better anti-interference capability than the rule-based control strategy.

Dynamical Behavior of the SEIS Infectious Disease Model with White Noise Disturbance

Mathematical model plays an important role in understanding the disease dynamics and designing strategies to control the spread of infectious diseases. In this paper, we consider a deterministic SEIS model with a saturation incidence rate and its stochastic version. To begin with, we propose the deterministic SEIS epidemic model with a saturation incidence rate and obtain a basic reproduction number R0. Our investigation shows that the deterministic model has two kinds of equilibria points, that is, disease‐free equilibrium E0 and endemic equilibrium E∗. The conditions of asymptotic behaviors are determined by the two threshold parameters R0 and . When R0 &lt; 1, the disease‐free equilibrium E0 is locally asymptotically stable, and it is unstable when R0 &gt; 1. E∗ is locally asymptotically stable when . In addition, we show that the stochastic system exists a unique positive global solution. Conditions and are used to show extinction of the disease in the exponent. Finally, SEIS with a stochastic version has stationary distribution and the ergodicity holds when by constructing appropriate Lyapunov function. Our theoretical finding is supported by numerical simulations. The aim of our analysis is to assist the policy‐maker in prevention and control of disease for maximum effectiveness.

Optimal Adaptive Filtering Algorithm by Using the Fractional-Order Derivative

The previous work for the filter design considers uncorrelated white measurement noise disturbance. For more complex correlated noise disturbance, the conventional adaptive filter results in biased estimates. To overcome this problem, we introduce a linear prefilter to whiten the correlated noise (i.e., colored noise) for obtaining the unbiased estimate of the filter weight. Moreover, the design of some adaptive filters mainly focuses on the integer-order optimization methods. However, compared with the integer-order-based adaptive algorithms, the fractional-order-based algorithms show better performance. Thus, this letter develops a new gradient approach for the adaptive filter design based on the fractional-order derivative and a linear filter. Finally, the simulation results are provided from the system identification perspective for demonstrating the performance analysis of the proposed algorithms.

Full Information H2 Control of Borel-Measurable Markov Jump Systems with Multiplicative Noises

This paper addresses an H2 optimal control problem for a class of discrete-time stochastic systems with Markov jump parameter and multiplicative noises. The involved Markov jump parameter is a uniform ergodic Markov chain taking values in a Borel-measurable set. In the presence of exogenous white noise disturbance, Gramian characterization is derived for the H2 norm, which quantifies the stationary variance of output response for the considered systems. Moreover, under the condition that full information of the system state is accessible to measurement, an H2 dynamic optimal control problem is shown to be solved by a zero-order stabilizing feedback controller, which can be represented in terms of the stabilizing solution to a set of coupled stochastic algebraic Riccati equations. Finally, an iterative algorithm is provided to get the approximate solution of the obtained Riccati equations, and a numerical example illustrates the effectiveness of the proposed algorithm.

Intelligent Pitch Controller Identification and Design

This paper exhibits a comparative assessmentbased on time response specification performance between modern and classical controller for a pitch control system of an aircraft system. The dynamic modeling of pitch control system is considered on the design of an autopilot that controls the pitch angle It starts with a derivation of a suitable mathematical model to describe the dynamics of an aircraft. For getting close to actual conditionsthe white noise disturbance is applied to the system.In this paper it is assumed that the modelpitch control systemis not available. So using the identification system and Box-Jenkins model estimator we identify the pitch control system System’s identification is a procedure for accurately characterizing the dynamic response behavior of a complete aircraft, of a subsystem, or of an individual component from measureddata.To study the effectiveness of the controllers, the LQR Controller and PID Controller and fuzzy controller is developed for controlling the pitch angle of an aircraft system. Simulation results for the response of pitch controller are presented instep’s response. Finally, the performances of pitch control systems are investigated and analyzed based on common criteria of step’s response in order to identify which control strategy delivers better performance with respect to the desired pitch angle. It is found from simulation, that the fuzzy controller gives the best performance compared to PID and LQR controller.

Dynamics Analysis of a Stochastic Plant Disease Model with Continuous Cultural Control Strategy

In this paper, a new stochastic plant disease model with continuous control strategy is proposed and analyzed. The dynamics of the system are explored under white noise disturbance. We prove that if R 1 &gt; 1 , then the disease is persistent; moreover, if R 2 &gt; 1 , then the solutions of the system have a stationary distribution. For the special case, we prove that if R 1 &lt; 1 , then the disease will eventually disappear. Finally, some numerical simulations were implemented to illustrate the theoretical results.

Disrupting Audio Event Detection Deep Neural Networks with White Noise

Audio event detection (AED) systems can leverage the power of specialized algorithms for detecting the presence of a specific sound of interest within audio captured from the environment. More recent approaches rely on deep learning algorithms, such as convolutional neural networks and convolutional recurrent neural networks. Given these conditions, it is important to assess how vulnerable these systems can be to attacks. As such, we develop AED-suited convolutional neural networks and convolutional recurrent neural networks, and attack them next with white noise disturbances, conceived to be simple and straightforward to be implemented and employed, even by non-tech savvy attackers. We develop this work under a safety-oriented scenario (AED systems for safety-related sounds, such as gunshots), and we show that an attacker can use such disturbances to avoid detection by up to 100 percent success. Prior work has shown that attackers can mislead image classification tasks; however, this work focuses on attacks against AED systems by tampering with their audio rather than image components. This work brings awareness to the designers and manufacturers of AED systems, as these solutions are vulnerable, yet may be trusted by individuals and families.

Adaptive neuro-fuzzy and simple adaptive control methods for full three-dimensional coupled buildings subjected to bi-directional seismic excitations

Attenuation of three-dimensional coupled buildings under bi-directional seismic excitations using semi-active control devices is pursued in this paper. Multiple magneto-rheological (MR) dampers are employed to connect two adjacent buildings at multiple levels for real-time control of the structural responses. The MR dampers are managed by adaptive neuro-fuzzy inference systems (ANFIS) and simple adaptive control (SAC) methods so a comparison between the performance of both controllers can be made. The displacement feedback type is used for both control methods to design the closed-loop action. The structural system modeled as two three-dimensional buildings connected by frame elements in which the MR dampers are implemented. The equations of motions of the three-dimensional model are formulated by assuming that each floor diaphragm is rigid in its own plane but flexible in the vertical coordinate. Each link is assumed to have three degrees-of-freedom two translational and one rotational at its ends. The adaptive neuro-fuzzy inference system is designed based on Sugeno-type model. Seven triangular membership functions are chosen to fuzzify the input crisp data in the fuzzy logic controller. The training data for ANFIS are generated by the Linear Quadratic Regulator (LQR) under white-noise disturbance. For the simple adaptive controller, LQR is also used to generate the desired trajectories of the reference-model. Numerical simulations are carried out for both symmetrical and asymmetrical coupled buildings under eleven pairs of major earthquakes. The results show that both ANFIS and SAC can deal very successfully with modeling complexities associated with full three-dimensional models subjected to multi-excitations. In terms of seismic responses reduction, both methods have shown a great potential in enhancing the structural performance under seismic activities.

Dynamical analysis of a stochastic rumor-spreading model with Holling II functional response function and time delay

With the rapid development of information society, rumor plays an increasingly crucial part in social communication, and its spreading has a significant impact on human life. In this paper, a stochastic rumor-spreading model with Holling II functional response function considering the existence of time delay and the disturbance of white noise is proposed. Firstly, the existence of a unique global positive solution of the model is studied. Then the asymptotic behavior of the global solution around the rumor-free and rumor-local equilibrium nodes of the deterministic system is discussed. Finally, through some numerical results, the validity and availability of theoretical analysis is verified powerfully, and it shows that some factors such as the transmission rate, the intensity of white noise, and the time delay have significant relationship with the dynamical behavior of rumor spreading.