Self-adapting control parameters modifieddifferential evolution for trajectoryplanning of manipulators

Control parameters of original differential evolution （DE） are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE for different optiinization problems. According to the relative position of two different individual vectors selected to generate a difference vector in the searching place, a self-adapting strategy for the scale factor F of the difference vector is proposed. In terms of the convergence status of the target vector in the current population, a self-adapting crossover probability constant CR strategy is proposed. Therefore, good target vectors have a lower CFI while worse target vectors have a large CFI. At the same time, the mutation operator is modified to improve the convergence speed. The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator. Finally, the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.

IMPLICIT ITERATIVE METHODS WITH VARIABLE CONTROL PARAMETERS FOR ILL-POSED OPERATOR EQUATIONS

This paper discusses a kind of implicit iterative methods with some variable parameters, which are called control parameters, for solving ill-posed operator equations. The theoretical results show that the new methods always lead to optimal convergence rates and have some other important features, especially the methods can be implemented parallelly.

Integrated Optimization of Structure and Control Parameters for the Height Control System of a Vertical Spindle Cotton Picker

Vertical picking method is a predominate method used to harvest cotton crop.However,a vertical picking method may cause spindle bending of the cotton picker if spindles collide with stones on the cotton field.Thus,how to realize a precise height control of the cotton picker is a crucial issue to be solved.The objective of this study is to design a height control system to avoid the collision.To design it,the mathematical models are established first.Then a multi-objective optimization model represented by structure parameters and control parameters is proposed to take the pressure of chamber without piston,response time and displacement error of the height control system as the opti-mization objectives.An integrated optimization approach that combines optimization via simulation,particle swarm optimization and simulated annealing is proposed to solve the model.Simulation and experimental test results show that the proposed integrated optimization approach can not only reduce the pressure of chamber without piston,but also decrease the response time and displacement error of the height control system.

Optimization of control parameters for petroleum waste composting

Composting is being widely employed in the treatment of petroleum waste. The purpose of this study was to find the optimum control parameters for petroleum waste in-vessel composting. Various physical and chemical parameters were monitored to evaluate their influence on the microbial communities present in composting. The CO2 evolution and the number of microorganisms were measured as the activity of composting. The results demonstrated that the optimum temperature, pH and moisture content were 56.5 - 59.5 degreesC, 7.0 - 8.5 and 55 % - 60%, respectively. Under the optimum conditions, the removal efficiency of petroleum hydrocarbon reached 83.29% after 30 days composting.

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

Optimization and Configuration of Control Parameters to Enhance Small-signal Stability of Hybrid LCC-MMC HVDC System

This paper investigates the small-signal stability of the hybrid high-voltage direct current(HVDC)transmission system.The system is composed of line commutated converter(LCC)as rectifier and modular multi-level converter(MMC)as inverter under weak AC grid condition.Firstly,the impact of short-circuit ratio(SCR)at inverter side on the system stability is investigated by eigen-analysis,and the key control parameters which have major impact on the dominant mode are identified by the participation factor and sensitivity analysis.Then,considering the quadratic index and damping ratio characteristic,an objective function for evaluating the system stability is developed,and an optimization and configuration method for control parameters is presented by the utilization of Monte Carlo method.The eigenvalue results and the electromagnetic transient(EMT)simulation results show that,with the optimized control parameters,the small-signal stability and the dynamic responses of the hybrid system are greatly improved,and the hybrid system can even operate under weak AC grid condition.

Development of a Novel Feedforward Neural Network Model Based on Controllable Parameters for Predicting Effluent Total Nitrogen

The problem of effluent total nitrogen(TN)at most of the wastewater treatment plants(WWTPs)in China is important for meeting the related water quality standards,even under the condition of high energy consumption.To achieve better prediction and control of effluent TN concentration,an efficient prediction model,based on controllable operation parameters,was constructed in a sequencing batch reactor process.Compared with previous models,this model has two main characteristics:①Superficial gas velocity and anoxic time are controllable operation parameters and are selected as the main input parameters instead of dissolved oxygen to improve the model controllability,and②the model prediction accuracy is improved on the basis of a feedforward neural network(FFNN)with algorithm optimization.The results demonstrated that the FFNN model was efficiently optimized by scaled conjugate gradient,and the performance was excellent compared with other models in terms of the correlation coefficient(R).The optimized FFNN model could provide an accurate prediction of effluent TN based on influent water parameters and key control parameters.This study revealed the possible application of the optimized FFNN model for the efficient removal of pollutants and lower energy consumption at most of the WWTPs.

THE STUDY ON A KIND OF CONTROL SYSTEM WITH NONLINEAR PARABOLIC DISTRIBUTED PARAMETERS

The modelling of one kind of nonlinear parabolic distributed parameter control system with moving boundary, which had extensive applications was presented, Two methods were used to investigate the basic characteristics of the system: I) transforming the system it? the variable domain into that in the fixed domain; 2) transforming the distributed parameter system into the lumped parameter system. It is found that there are two critical values for the control variable : the larger one determines whether or not the boundary would move, while the smaller one determines whether or not the boundary, would atop automatically. For one-dimensional system of planar, cylindrical and spherical cases the definite solution problem can be expressed as a unified form. By means of the computer simulation the open-loop control system and close-cycle feedback control system have been investigated. Numerical results agree well with theoretical results. The computer simulation shows that the system is well posed, stable, measurable and controllable.

Simulation Study on the Controllable Dielectrophoresis Parameters of Graphene

The method of using dielectrophoresis （DEP） to assemble graphene between micro-electrodes has been proven to be simple and efficient. We present an optimization method for the kinetic formula of graphene DEP, and discuss the simulation of the graphene assembly process based on the finite element method. The simulated results illustrate that the accelerated motion of graphene is in agreement with the distribution of the electric field squared gradient. We also conduct research on the controllable parameters of the DEP assembly such as the alternating current （AC） frequency, the shape of micro-electrodes, and the ratio of the gap between electrodes to the characteristic/geometric length of graphene （λ）. The simulations based on the Clausius-Mossotti factor reveal that both graphene velocity and direction are influenced by the AC frequency. When graphene is close to the electrodes, the shape of micro-electrodes will exert great influence on the velocity of graphene. Also, λ has a great influence on the velocity of graphene. Generally, the velocity of graphene would be greater when λ is in the range of 0.4 0.6. The study is of a theoretical guiding significance in improving the precision and efficiency of the graphene DEP assembly.

System Identification and Parameter Self-Tuning Controller on Deep-Sea Mining Vehicle

System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.

A Mathematical Model of COVID-19: Analysis and Identification of Parameters for Better Decision Making

Since the onset of the COVID-19 epidemic, the world has been impressed by two things: The number of people infected and the number of deaths. Here, we propose a mathematical model of the spread of this disease, analyze this model mathematically and determine one or more dominant factors in the propagation of the COVID-19 epidemic. We consider the S-E-I-R epidemic model in the form of ordinary differential equations, in a population structured in susceptibles S, exposed E as caregivers, travelers and assistants at public events, infected I and recovered R classes. Here we decompose the recovered class into two classes: The deaths class D and the class of those who are truly healed H. After the model construction, we have calculated the basic reproduction number R0, which is a function of certain number of parameters like the size of the exposed class E. In our paper, the mathematical analysis, which consists in searching the equilibrium points and studying their stability, is done. The work identifies some parameters on which one can act to control the spread of the disease. The numerical simulations are done and they illustrate our theoretical analysis.

High value of controlled attenuation parameter predicts a poor antiviral response in patients with chronic hepatits B

BACKGROUND: Controlled attenuation parameter (CAP) is a non-invasive method for diagnosing hepatic steatosis based on vibration-controlled transient elastography. The objective of this study was to investigate the effect of high value of CAP on antiviral therapy in patients with chronic hepatitis B (CHB). METHODS: Patients with CHB receiving enticavir for initial antiviral therapy were studied; they were divided into the high CAP group and normal CAP group at baseline according to the CAP values. The effect of the antiviral therapy between the two groups were compared at week 12, 24 and 48. Patients with high CAP value at baseline were divided into three subgroups, mild, moderate and severe elevation; the therapeutic response were compared among patients with normal CAP and subgroups of patients with elevated CAP. RESULTS: A total of 153 patients were enrolled. Among them, 63 were in the high CAP group and 90 in the normal CAP group. Patients with high CAP had lower rates of ALT normalization and HBV DNA clearance in response to antiviral therapy compared with those with normal CAP at week 12, 24 and 48. Further analysis showed that the rate of ALT normalization in patients with mildly and moderately elevated CAP were significant lower than those with normal CAP at week 12 and 24; while the difference was not significant between the patients with normal CAP and those with severely elevated CAP. The rate of HBV DNA clearance was significantly lower in patients with severely elevated CAP compared with those with normal CAP at week 12, 24 and 48. CONCLUSION: CHB patients with high CAP had poor response to antiviral therapy.

A novel methodology for constructing a multi-wing chaotic and hyperchaotic system with a unified step function switching control

This paper aims at developing a novel method of constructing a class of multi-wing chaotic and hyperchaotic system by introducing a unified step function. In order to overcome the essential difficulties in iteratively adjusting multiple parameters of conventional multi-parameter control, this paper introduces a unified step function controlled by a single parameter for constructing various multi-wing chaotic and hyperchaotic systems. In particular, to the best of the authors＇ knowledge, this is also the first time to find a non-equilibrium multi-wing hyperchaotic system by means of the unified step function control. According to the heteroclinic loop Shilnikov theorem, some properties for multi-wing attractors and its chaos mechanism are further discussed and analyzed. A circuit for multi-wing systems is designed and implemented for demonstration, which verifies the effectiveness of the proposed approach.

Determination of temperature distribution and control parameter in a two-dimensional parabolic inverse problem with overspecified data

In this paper, we present a new algorithm to solve a two-dimensional parabolic inverse problem with a source parameter, which appears in many physical phenomena. A linearized compact difference scheme for this problem is constructed using the finite difference method. The discretization accuracy is second-order in time and fourth-order in space. We obtain the unique solvability and present an alternating direction implicit algorithm to solve this difference scheme. The results of numerical experiments are presented to demonstrate the accuracy of this algorithm.

Evaluation of controlled attenuation parameter in assessing hepatic steatosis in patients with autoimmune liver diseases

BACKGROUND Hepatic steatosis commonly occurs in some chronic liver diseases and may affect disease progression.AIM To investigate the performance of controlled attenuation parameter(CAP)for the diagnosis of hepatic steatosis in patients with autoimmune liver diseases(AILDs).METHODS Patients who were suspected of having AILDs and underwent liver biopsy were consistently enrolled.Liver stiffness measurement(LSM)and CAP were performed by transient elastography.The area under the receiver operating characteristic(AUROC)curve was used to evaluate the performance of CAP for diagnosing hepatic steatosis compared with biopsy.RESULTS Among 190 patients with biopsy-proven hepatic steatosis,69 were diagnosed with autoimmune hepatitis(AIH),18 with primary biliary cholangitis(PBC),and 27 with AIH-PBC overlap syndrome.The AUROCs of CAP for the diagnosis of steatosis in AILDS were 0.878(0.791-0.965)for S1,0.764(0.676-0.853)for S2,and 0.821(0.716-0.926)for S3.The CAP value was significantly related to hepatic steatosis grade(P<0.001).Among 69 patients with AIH,the median CAP score was 205.63±47.36 dB/m for S0,258.41±42.83 dB/m for S1,293.00±37.18 dB/m for S2,and 313.60±27.89 dB/m for S3.Compared with patients with nonalcoholic fatty liver disease(NAFLD)presenting with autoimmune markers,patients with AIH concomitant with NAFLD were much older and had higher serum IgG levels and LSM values.CONCLUSION CAP can be used as a noninvasive diagnostic method to evaluate hepatic steatosis in patients with AILDs.Determination of LSM combined with CAP may help to identify patients with AIH concomitant with NAFLD from those with NAFLD with autoimmune phenomena.

Adaptive control of bifurcation and chaos in a time-delayed system

In this paper,the stabilization of a continuous time-delayed system is considered.To control the bifurcation and chaos in a time-delayed system,a parameter perturbation control and a hybrid control are proposed.Then,to ensure the asymptotic stability of the system in the presence of unexpected system parameter changes,the adaptive control idea is introduced,i.e.,the perturbation control parameter and the hybrid control parameter are automatically tuned according to the adaptation laws,respectively.The adaptation algorithms are constructed based on the Lyapunov-Krasovskii stability theorem.The adaptive parameter perturbation control and the adaptive hybrid control methods improve the corresponding constant control methods.They have the advantages of increased stability,adaptability to the changes of the system parameters,control cost saving,and simplicity.Numerical simulations for a well-known chaotic time-delayed system are performed to demonstrate the feasibility and superiority of the proposed control methods.A comparison of the two adaptive control methods is also made in an experimental study.

Planar morphology and controlling factors of the gullies in the Yuanmou Dry-hot Valley based on field investigation

The plane form of a gully can provide a basis for evaluating the gully volume and erosion rate, acting process, and evolutionary stage. For describing the planar characteristics of a permanent gully and understanding their controlling factors, this study, utilizing a total station and GPS RTK, measured the shoulder lines and channel curves of 112 gullies in six sites of the Yuanmou Dry-hot Valley and then mapped them by Arc GIS software and calculated nine parameters. The results showed that the channel lengths range from 10.88 to 249.11 m; the widths range from 6.20 to 40.99 m; the perimeters range from 54.11 to 541.67 m; the gully areas range from 153.02 to 6,930.30 m2; the left-side areas range from 92.93 to 4,027.20 m2; and the right-side areas range from 63.65 to 3,539.77 m2. The slightly sinuous and straight gullies account for 73.21% of the total gullies; the quantity of the right skewed gullies is 8.93% greater than that of the left skewed ones based on the symmetry ratio; the shape ratios range from 1.12 to 1.40 and the morphology ratios from 0.038 to 1.294; the fractal dimension is 1.192. Gullies in different sites have diverse planar characteristics. Except for the symmetry index, which was close to a negatively skewed distribution, all of the other parameters had the characteristic of positively skewed distribution. The gully area is related to the length and width, but the gully length has a weak correlation with the width. The evolutionary stage, topographic conditions, strata, soil properties, and piping erosion played very important roles in the gully planar morphology. This study could provide useful information for controlling gully erosion and safeguarding human habitation and engineering buildings.

Advances in structural vibration control application of magneto-rheological visco-elastomer

Magneto-rheological visco-elastomer （MRVE） as a new smart material developed in recent years has several significant advantages over magneto-rheological liquid. The adjustability of structural dynamics to random environmental excitations is required in vibration control. MRVE can supply considerably adjustable damping and stiffness for structures, and the adjustment of dynamic properties is achieved only by applied magnetic fields with changeless structure design. Increasing researches on MRVE dy- namic properties, modeling, and vibration control application are presented. Recent advances in MRVE dynamic properties and structural vibration control application including composite structural vibration mitigation under uniform magnetic fields, vibration response characteristics improvement through harmonic parameter distribution, and optimal bounded parametric control design based on the dynamical programming principle are reviewed. Relevant main methods and results introduced are beneficial to understanding and researches on MRVE application and development.

Robust H_∞ control for uncertain Markovian jump systems with mixed delays

We scrutinize the problem of robust H∞control for a class of Markovian jump uncertain systems with interval timevarying and distributed delays. The Markovian jumping parameters are modeled as a continuous-time finite-state Markov chain. The main aim is to design a delay-dependent robust H∞control synthesis which ensures the mean-square asymptotic stability of the equilibrium point. By constructing a suitable Lyapunov–Krasovskii functional（LKF）, sufficient conditions for delay-dependent robust H∞control criteria are obtained in terms of linear matrix inequalities（LMIs）. The advantage of the proposed method is illustrated by numerical examples. The results are also compared with the existing results to show the less conservativeness.