Random Disturbances Research Articles

An Energy-Efficiency Prediction Method in Crude Distillation Process Based on Long Short-Term Memory Network

The petrochemical industry is a pillar industry for the development of the national economy affecting people’s daily living standards. Crude distillation process is the core and leading unit of the petrochemical industry. Its energy consumption accounts for more than 20% of the total energy consumption of the whole plant, which is the highest energy consumption link. A model based on the long short-term memory network (LSTM) is proposed in this paper to predict and analyze energy efficiency. This model extracts the complex relationship between many process variables and predicts the energy efficiency of the crude distillation process. Firstly, the process simulation of crude distillation is carried out. By adding random disturbance, the data set in different working conditions is obtained, and the difference between the working conditions is expressed with the distance-coded heat map. Secondly, the Savitzky–Golay (SG) filter is used to smooth the data, which preserves the original characteristics of the data and improves the prediction effect. Finally, the LSTM model is used to predict and analyze the energy efficiency of products under different working conditions. The MAE, MSE, and MAPE results of the LSTM model under different working conditions in the test set are lower than 1.3872%, 0.0307%, and 0.2555%, respectively. Therefore, the LSTM model can be considered a perfect model for the test set, and the prediction results have high reliability to accurately predict the energy efficiency of the crude distillation process.

Disturbance observer based nonsingular fast terminal sliding mode control of underactuated AUV

In this paper, a disturbance observer based nonsingular fast terminal sliding mode control (NFTSMC) scheme is proposed for the trajectory tracking control of an underactuated autonomous underwater vehicle (AUV). A nonlinear disturbance observer (DO) is designed to estimate complex external disturbances and incorporated into a NFTSMC. In constructing sliding surfaces, the parameter selection in the exponential terms in NFTSMC is expanded. Lyapunov's second method proves the uniformly ultimately bounded stability of the proposed DO based NFTSMC. Simulation is performed to demonstrate the effectiveness of the proposed controller. Compared with the existing NTSMC methods, the convergence rate is improved by the proposed NFTSMC. Moreover, the proposed controller has good robustness to smooth external disturbances. For random disturbances along with an impulse at some time, the proposed controller can also track the disturbance well and achieve good robustness.

How colored noise shifts the boundaries of oscillatory zones in suspension flows

A mathematical model of the suspension flow forced by colored random disturbances is considered. We focus on the parameter zone adjacent to the Andronov–Hopf bifurcation separating stable equilibrium and self‐oscillatory modes. A phenomenon of stochastic excitement with colored‐noise‐induced generation of spiking oscillations in the equilibrium zone is investigated. We show how colored noise shifts boundaries of oscillation modes, and study dependence of this shift on the correlation time parameter. Zones of the stochastic resonance are revealed and parametrically described in terms of probability of excitement.

A Stochastic Simulator of a Multi-Component Distillation Tower Built as an Excel Macro

Dynamic process simulation is widely used in teaching controller design, as it allows foreseeing the performance of different control configurations and controller tunings. Currently, most college-level controller design exercises that are based on simulation consider deterministic perturbations (i.e. steps or ramps). In real life however, processes are more likely to face fluctuating, random disturbances, so the use of stochastic simulation in controller tuning exercises would provide students with an experience closer to their future professional practice than that provided by deterministic simulation. However, public institutions attempting to use dynamic, stochastic simulators in teaching, are hindered by the need of buying licenses of simulation packages or specialized programming languages (such as Matlab), as there aren´t any dynamic, stochastic simulators available as downloadable freeware. This paper shows a dynamic, stochastic simulator with a friendly interface of a distillation tower, developed as an Excel macro. This simulator has the advantage that it can be used at no cost to educational institutions since Excel is almost universally known and used by college faculties.

State Estimation and Optimal Control of Four-Tank System with Stochastic Approximation Approach

This study aims to optimally control the level of a four-tank system at the steady state in the random disturbance environment using the stochastic approximation (SA) approach. Firstly, the stochastic optimal control problem of an equivalent discrete-time is introduced, where the voltages to the pumps are the control inputs. By minimizing the sum of squared errors, the liquid levels are estimated. Then, first-order necessary conditions are derived by defining the Hamiltonian function. Thus, the optimal voltages are calculated based on the estimated liquid levels to update the gradient of the cost function. Finally, for illustration, parameters in the system are considered and a simulation is conducted. The simulation results show that the state estimation and control law design can perform well, and the liquid levels are addressed along the steady state. In conclusion, the applicability of the SA approach for handling a four-tank system with random disturbances is demonstrated.

A Fault Diagnosis Tool for Electrohydraulic Actuators

Abstract This letter introduces a new method for fault diagnosis of electrohydraulic actuators (EHA). Common faults include abnormal supply pressure, viscous friction, bulk modulus, and oil leakage. First, a validated model of an EHA is used to simulate faults at varying percent perturbations to generate data. The harmonics of the corresponding servo-valve control signal are calculated using sliding discrete Fourier transform (DFT) and stored. A fully connected neural network is then trained to predict fault cases using harmonic information. Simulation results show high accuracy for diagnosis of individual cases and ability to diagnose mixed cases. The actuator motion reference signal is also shown to impact overall diagnosability. Finally, the method was tested on experimental data consisting of nominal and low-pressure cases. Random disturbances were added to simulation training data to allow more robustness and resulted in 80% accuracy on the 10 experimental datapoints. It is demonstrated that a validated model can be used to diagnose faults in actual experimental setup. Future work will be done to diagnose non-pressure experimental cases.

A Self-Learning Based Dynamic Multi-Objective Evolutionary Algorithm for Resilient Scheduling Problems in Steelmaking Plants

Scheduling is one of the most important missions for plant-wide optimization in steelmaking manufacturing systems. In the context of dynamic scheduling, the decision-maker should simultaneously minimize economic objectives within the decision space and violation penalty out of the decision space. In this study, we introduce a resilient scheduling model in steelmaking plants, which provides flexible decisions, including buffering times in between stages and controllable processing speeds in the casting stage, to enable the solution to absorb random disturbances and recover quickly. We formulate the dynamic steelmaking scheduling problem with resilient responding strategies, which is a variant of dynamic multi-objective optimization problems (DMOP), and propose a resilient scheduling optimization framework to solve it over time. First, we employ a vector with problem-specific knowledge to map the whole decision space to sub-schedules in the casting stage, which contains casting priority, casting speed and scaling ratio. Next, we form a multi-objective linear programming model to evaluate these problem-specific vectors. Last but not least, we develop a self-learning based dynamic multi-objective differential evolutionary algorithm to solve the variant DMOP, in which a hypothesis-testing technique is used to detect and identify environmental changes. The sensitivity analysis and algorithm comparisons are performed on a wide range of problem instances under dynamic environments. Experimental evidence validates that the proposed resilient model and the optimization framework is effective to solve the dynamic scheduling problem in steelmaking plants. Note to Practitioners—This paper investigates a dynamic scheduling problem that comes from steelmaking manufacturing systems and is extensively studied in existing works. Because a decision-maker only has incomplete knowledge about the realistic environments, this paper simultaneously considers that both the decision and objective space of a scheduling problem dynamically changed over time. We develop a resilient scheduling model which can absorb the different types or scales of disturbances caused by unforeseen events, and recover rapidly to the original state. We formulate the resilience scheduling model and propose a dynamic multi-objective algorithm based on the self-learning differential evolutionary algorithm to solve it. The effectiveness of the proposed model and algorithm is validated via sensitivity analysis and comparison to other well-known algorithms. Furthermore, the resilient scheduling model and its optimization framework can also be applied to dynamic scheduling problems in other industries.

A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks

The dynamic-scheduling problem of transmission tasks (DSTT) is an important problem in the daily work of radio and television transmission stations. The transmission effect obtained by the greedy algorithm for task allocation is poor. In the case of more tasks and equipment and smaller time division, the precise algorithm cannot complete the calculation within an effective timeframe. In order to solve this problem, this paper proposes a discrete particle swarm optimization algorithm (DPSO), builds a DSTT mathematical model suitable for the DPSO, solves the problem that particle swarm operations are not easy to describe in discrete problems, and redefines the particle motion strategy and adds random disturbance operation in its probabilistic selection model to ensure the effectiveness of the algorithm. In the comparison experiment, the DPSO achieved much higher success rates than the greedy algorithm (GR) and the improved genetic algorithm (IGA). Finally, in the simulation experiment, the result data show that the accuracy of the DPSO outperforms that of the GR and IGA by up to 3.012295% and 0.11115%, respectively, and the efficiency of the DPSO outperforms that of the IGA by up to 69.246%.

Stochastic Dynamics of Suspension System in Maglev Train: Governing Equations for Response Statistics and Reliability

The suspension system of the maglev train will inevitably be disturbed by random factors such as track irregularities, which will cause random vibration of the train and even affect the safety of the train. Therefore, the research on the response and reliability of suspension system under random disturbance is crucial to its safe operation. In this paper, the response and the reliability of a suspension system are investigated using the theory and methods of stochastic dynamics. First, the magnetic gap and vertical velocity of the suspension system are random due to the random disturbance. Thus, the stochastic response is investigated through the probability density function (PDF), which is governed by the Fokker–Planck–Kolmogorov (FPK) equation corresponding to suspension system. And the response statistics of the suspension system under different system parameters and disturbance intensities are analyzed by solving the corresponding FPK equation using the finite difference (FD) method. Second, random disturbance may lead to the vibration amplitude of the suspension system exceeding the safety domain and causing safety incident, which is a reliability problem in stochastic dynamical systems. The probability that response is still in the safety domain at a given time is the reliability function of the suspension system, which is governed by the backward Kolmogorov equation. The time that the response first passes through the safety domain is the first-passage time, and its n-order moment satisfies the generalized Pontryagin equation. Reliability of the suspension system is analyzed by solving these governing equations using the FD method. In addition, the results of the FD method in this paper are verified with those of Monte Carlo (MC) simulation, which shows the correctness of FD method.

SIMULATION OF RANDOM EXTERNAL DISTURBANCE ACTING ON THE CAR BODY IN THE URGENT BRAKING MODE

Topicality. When creating ESP (Electronic Stability Program) car stability systems, the main task is to determine the values of the varied parameters of the EBD (Electronic Brake Distribution) electronic block, which ensure the required accuracy of stabilization of the car body in urgent braking mode relative to the given direction of movement. The purpose of the article. To solve the problem of parametric synthesis of the EBD block, it is necessary to create a simulation model of external disturbances acting on the car body from the side of the road surface. Well-known simulation models are created for car movement modes on a random surface with a constant speed. At the same time, random external disturbances are stationary in nature. The urgent braking mode is characterized by an intense decrease in the current speed of the car. Results. The paper proposes a method of constructing a random function of an external disturbance acting on the car body in urgent braking mode. It is proved that this perturbation is non-stationary in nature; the forming dynamic link, the input of which is supplied with single "white noise", and the output of which is an external disturbance, is an oscillating dynamic link, the gain and time constants of which depend on the current value of the car's speed during urgent braking.

Metrological Aspects of Assessing Surface Topography and Machining Accuracy in Diagnostics of Grinding Processes

The paper presents probabilistic aspects of diagnostics of grinding processes with consideration of metrological aspects of evaluation of topography of machined surfaces and selected problems of assessment of machining accuracy. The processes of creating the geometric structure of the ground surface are described. It was pointed out that the distribution of features important for process diagnostics depends on the mechanism of cumulative effects of random disturbances. Usually, there is a multiplicative mechanism or an additive mechanism of the component vectors of relative displacements of the tool and workpiece. The paper describes a method for determining the classification ability of specific parameters used to evaluate stereometric features of ground surfaces. It is shown that the ability to differentiate the geometric structure of a certain set of surfaces using a selected parameter depends on the geometric mean of the differences in normalized and sorted, consecutive values of this parameter. A methodology is presented for evaluating the ability of various parameters to distinguish different geometric structures of surfaces. Further, on the basis of analyses of a number of grinding processes, a methodology was formulated for proceeding leading to a comprehensive evaluation of machining accuracy and forecasting its results. It was taken into account that in forecasting the accuracy of grinding, it is necessary to determine the deviations, arising under the conditions of multiplicative interaction of the effects of various causes of inaccuracy. Examples are given of processes in which, due to the deformation of the technological system, dependent on the position of the zone and machining force, varying temperature fields and tool wear, the distributions of dimensional deviations are not the realization of stationary processes. It was emphasized that on the basis of the characteristics of the dispersion of the deviation value in the sum set of elements, it is not possible to infer its causes. Only the determination of the "instantaneous" values of the deviation dispersion parameters allows a more complete diagnosis of the process.

Navigation and closed-loop control of magnetic microrobot in plant vein mimic environment.

In recent years, research on the manipulation and control of microrobot has gradually matured. In order to improve the intelligence of microrobots, navigation study also becomes an important research topic. In practice, microrobots could be disturbed by the flowing liquid when it moves in a microfluidic environment. As a result, the actual trajectory of microrobots will deviate from the intended one. In this paper, firstly, different algorithms for the navigation of microrobots in a simulated plant leaf vein environment are investigated. According to the simulation results, RRT*-Connect is then selected as the path planning algorithm with a relatively better performance. Based on the pre-planned trajectory, a fuzzy PID controller is further designed for precise trajectory tracking, which can effectively eliminate the random disturbance caused by micro-fluid flow during the motion and make it quickly recover to a stable movement state.

Effects of Earth–Rock Dam Heterogeneity on Seismic Wavefield Characteristics

Earth–rock dams are typical soil–rock mixtures with high heterogeneity. Mastering the effect of dam heterogeneity on seismic wavefields is the premise of accurately detecting hidden risks in dams. In this paper, based on the soil–rock mixture characteristics of actual dams, a soil–rock mixture model that can reflect the heterogeneity of dams is established through local segmentation and reassignment of random disturbances. The influence of local area size on model heterogeneity is described. The seismic wavefield in a soil–rock mixture dam is numerically simulated through a staggered-grid finite-difference algorithm with second-order accuracy in time and sixth-order accuracy in space. Then, the effect of dam heterogeneity on effective wavefields is analyzed. The results show that the heterogeneity of the earth–rock dam can lead to scattered waves in the seismic wavefield, and the scattered waves are mainly generated by Rayleigh surface waves. In the seismic record, scattered waves with strong energy appear in the region below the surface waves. The scattered wave energy is weak and close to that in the homogeneous media in the region above the surface waves. As the rock content in the dam increases, the scattering of seismic wavefields and the energy of scattered waves weaken gradually. The scattered waves generated by the heterogeneity of the dam significantly impact the reflected longitudinal wave and converted wave but, affect the reflected shear wave less. The numerical simulation results are consistent with the acquired seismic wavefield from the field test, proving the effectiveness of the numerical simulation for the seismic wavefield propagation characteristics of the earth–rock dam.

Operational Absolutely Optimal Dynamic Control of the Stochastic Differential Plant’s State by Its Output

The problem of synthesizing the average-optimal control law for a dynamic plant subject to random disturbances, if its state variables are measured partially or with random errors, is considered. Using the method of a posteriori sufficient coordinates (SCs), the complexity of constructing the well-known interval-optimal Mortensen controller is described and a much simpler algorithm for finding its operational-optimal analog is obtained. The new controller does not require the solution of the corresponding Bellman equation in inverse time, since it is optimal in the sense of a time-varying criterion. This makes it possible to disregard information about the future behavior of the object and reduces the procedure for finding the dependence of a control on sufficient coordinates to direct-time integration of the Fokker–Planck–Kolmogorov equation and to solving a problem of parametric nonlinear programming. The application of the obtained algorithm is demonstrated by the example of a linear-quadratic-Gaussian problem, as a result of which a new operational version of the well-known separation theorem is formulated. It represents a stochastic control device as a combination of a linear Kalman–Bucy filter and a linear operational-optimal positional controller. The latter differs from the traditional interval-optimal controller by the well-known gain and does not require the solution of the corresponding matrix Riccati equation in inverse time

Modeling and Adaptive Boundary Robust Control of Active Heave Compensation Systems

Heave compensation systems are essential for operations’ safety, reliability, and efficiency in harsh offshore environments. This paper investigates the vibration suppression problem of a type of deep-sea robot with the length of time variation and harsh operating environments for active heave compensation systems, where hydraulic heave compensators implement actuators with input nonlinearity, model coupling, and unknown nonlinear disturbances. A robust adaptive output feedback control scheme based on the backstepping control method is designed to eliminate deep-ocean robot vibration, where the adaptive law handles the system parameter uncertainty. Meanwhile, a nonlinear disturbance observer (NDO) is introduced to overcome the effects of random disturbances and model coupling. In addition, the stability of the whole system is proved according to Lyapunov’s theory, and the scheme is shown to be feasible by theoretical analysis. Finally, a comparative simulation study was conducted to validate the effectiveness of the proposed controller.

Adaptive vibration control method for double-crystal monochromator base on VMD and FxNLMS.

Double-crystal monochromators (DCMs) are one of the most critical optical devices in beamlines at synchrotron sources, directly affecting the quality of the beam energy and position. As the performance of synchrotron light sources continues to improve, higher demands are placed on the stability of DCMs. This paper proposes a novel adaptive vibration control method combining variational modal decomposition (VMD) and filter-x normalized least mean squares (FxNLMS), ensuring DCM stability under random engineering disturbance. Firstly, the sample entropy of the vibration signal is selected as the fitness function, and the number of modal components k and the penalty factor α are optimized by a genetic algorithm. Subsequently, the vibration signal is decomposed into band frequencies that do not overlap with each other. Eventually, each band signal is individually governed by the FxNLMS controller. Numerical results have demonstrated that the proposed adaptive vibration control method has high convergence accuracy and excellent vibration suppression performance. Furthermore, the effectiveness of the vibration control method has been verified with actual measured vibration signals of theDCM.

Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10−3 for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems.

Development of a Distributed Control System for the Hydrodynamic Processes of Aquifers, Taking into Account Stochastic Disturbing Factors

Uncontrolled, intensive extraction of water from mineral water deposits can lead to negative consequences, the penetration of sewage beyond the water-resistant layers, the destruction of the structure of aquifers and the further loss of the mineral water source. This paper deals with the issues of control of hydrodynamic processes under random impacts in the aquifers of mineral water deposits using mathematical models. The analysis of the technological process of mineral water extraction and statistical analysis of retrospective monitoring data of the deposit were carried out in order to confirm the hypothesis of the stationarity of the processes under consideration. A mathematical model of hydrogeological processes occurring under random disturbances has been constructed. The influence of such disturbances is expressed in deviations of the head in the aquifer from the desired value. Controlling consists of maintaining the head of aquifers at a given level in order to preserve the hydro-mineral base of the region. Lumped and distributed controllers are proposed as corrective blocks, the parameters of which are calculated by frequency methods of controller synthesis. The use of a closed loop control system minimizes the influence of random effects.

Optical refocusing through perturbed multimode fiber using Cake-Cutting Hadamard encoding algorithm to improve robustness

Multimode fibres offer the advantages of high resolution and miniaturization over single-mode fibers in optical imaging. However, multimode fibre's imaging is susceptible to perturbations of MMF that can lead to secondary spatial distortions in the transmitted image. Perturbations include random disturbances in the fiber as well as environmental noise. Here, we exploit the fast focusing capability of the Cake-Cutting Hadamard algorithm to counteract the effects of perturbations and improve the system's robustness. Simulation shows that it can approach the theoretical enhancement at 2000 measurements. Experimental results show that the algorithm can help the system to refocus in a short time when MMFs are perturbed. This research will further contribute to using multimode fibres in medicine, communication, and detection.

Combined frequency and voltage regulation in multi-area system using an equilibrium optimiser based non-integer controller with penetration of electric vehicles

The frequency and terminal voltages in power systems are the two critical operating points that balance active and reactive powers, which must be maintained within predefined limits through automatic generation control (AGC) and automatic voltage regulator (AVR) control loops. To address this issue, in this manuscript, the problem of a combined AGC and AVR is solved using a non-integer order PID controller. Its parameters are adjusted using the recently proposed equilibrium optimiser (EO) technique. Along with conventional sources, renewable energy sources such as geothermal power plants and electric vehicles are considered. The EO-tuned non-integer order PID controller outperformed the traditional integer order PI and PID controllers in view of peak overshoots, undershoots and settling times. The performance of EO is evaluated and found superior against widely used particle swarm optimisation and firefly algorithms in the presence of non-integer order PID controller. Different case studies such as random load disturbance, the effect of nonlinearities, the impact of communication time delay and sensitivity analysis have been performed, which also test the heftiness of the non-integer order PID controller. The obtained results are verified using the real-time digital simulator OPAL-RT (OP4510) for nominal conditions.