Active disturbance rejection controller (ADRC) has good performance in induction motor (IM) control system, but controller parameter is difficult to tune. A method of tuning ADRC parameter by time scale is analyzed. T...Active disturbance rejection controller (ADRC) has good performance in induction motor (IM) control system, but controller parameter is difficult to tune. A method of tuning ADRC parameter by time scale is analyzed. The IM time scale is obtained by theoretical analysis. Combining the relations between scale time and ADRC parameters, ADRC parameter tuning in IM vector control based stator flux oriented is obtained. This parameter tuning method is validated by simulations and it provides a new technique for tuning of ADRC parameters of IM.展开更多
a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic...a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic algorithms, namely rapid convergence and attainment of the global optimum. Utilization of an orthogonal experiment method solves the determination of the genetic factors. Combination with an expert system can make best use of the actual experience of the plant operators. Simulation results of typical process systems examples show a good control performance and robustness.展开更多
Based on the evaluation of dynamic performance for feed drives in machine tools, this paper presents a two-stage tuning method of servo parameters. In the first stage, the evaluation of dynamic performance, parameter ...Based on the evaluation of dynamic performance for feed drives in machine tools, this paper presents a two-stage tuning method of servo parameters. In the first stage, the evaluation of dynamic performance, parameter tuning and optimization on a mechatronic integrated system simulation platform of feed drives are performed. As a result, a servo parameter combination is acquired. In the second stage, the servo parameter combination from the first stage is set and tuned further in a real machine tool whose dynamic performance is measured and evaluated using the cross grid encoder developed by Heidenhain GmbH. A case study shows that this method simplifies the test process effectively and results in a good dynamic performance in a real machine tool.展开更多
To improve welding quality, a method of proportional-integral-differential （PlD） parameters tuning based on pulsed gas metal arc welding （P-GMAW） control was put forward. Aiming at the request of dynamic responsiv...To improve welding quality, a method of proportional-integral-differential （PlD） parameters tuning based on pulsed gas metal arc welding （P-GMAW） control was put forward. Aiming at the request of dynamic responsiveness of PGMA W constant current control, a self-developed welding waveform wavelet analyzer was employed. By tuning the proportional parameter, integration time and differential time in sequence, the optimal PID parameters could be achieved. The results showed that, due to the PID parameters tuned by this method, the welding process was stable and the weld bead appearance was nice. The requirement of dynamic responsiveness of P-GMAW constant current control was fully met.展开更多
To overcome the deficiencies addressed in the conventional PID control and improve the dynamic performance and robustness of the system, a simple design and parameters tuning approach of internal model control-PID （I...To overcome the deficiencies addressed in the conventional PID control and improve the dynamic performance and robustness of the system, a simple design and parameters tuning approach of internal model control-PID （IMC-PID） controller was proposed for the first order plus time-delay （FOPTD） process and the second order plus time-delay （SOPTD） process. By approximating the time-delay term of the process model with the first-order Taylor series, the expressions for IMC-PID controller parameters were derived, and they had only one adjustable parameter 2 which was directly related to the dynamic performance and robustness of the system. Moreover, an analytical approach of selecting 2 was given based on the maximum sensitivity Ms. Then, the robust tuning of the system could be achieved according to the value of Ms. In addition, the proposed method could be extended to the integrator plus time-delay （IPTD） process and the first order delay integrating （FODI） process. Simulation studies were carried out on various processes with time-delay, and the results show that the proposed method could provide a better dynamic performance of both the set-point tracking and disturbance rejection and robustness against parameters perturbation.展开更多
This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an e...This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an efficient parameter tuning procedure(based on minimization of radius/margin bound for SVM's leave-one-out errors)into a multi-class classification strategy using a fuzzy decision factor,which is named fuzzy support vector machine(FSVM).The datasets generated from the Tennessee Eastman process(TEP)simulator were used to evaluate the clas-sification performance.To decrease the negative influence of the auto-correlated and irrelevant variables,a key vari-able identification procedure using recursive feature elimination,based on the SVM is implemented,with time lags incorporated,before every classifier is trained,and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation.Performance comparisons are implemented among several kinds of multi-class decision machines,by which the effectiveness of the proposed approach is proved.展开更多
High-speed pick-and-place parallel robot is a system where the inertia imposed on the motor shafts is real-time changing with the system configurations.High quality of computer control with proper controller parameter...High-speed pick-and-place parallel robot is a system where the inertia imposed on the motor shafts is real-time changing with the system configurations.High quality of computer control with proper controller parameters is conducive to overcoming this problem and has a significant effect on reducing the robot＇s tracking error.By taking Delta robot as an example,a method for parameter tuning of the fixed gain motion controller is presented.Having identifying the parameters of the servo system in the frequency domain by the sinusoidal excitation,the PD＋feedforward control strategy is proposed to adapt to the varying inertia loads,allowing the controller parameters to be tuned by minimizing the mean square tracking error along a typical trajectory.A set of optimum parameters is obtained through computer simulations and the effectiveness of the proposed approach is validated by experiments on a real prototype machine.Let the traveling plate undergoes a specific trajectory and the results show that the tracking error can be reduced by at least 50%in comparison with the conventional auto-tuning and Z-N methods.The proposed approach is a whole workspace optimization and can be applied to the parameter tuning of fixed gain motion controllers.展开更多
The high-purity distillation column system is strongly nonlinear and coupled,which makes it difficult to control.Active disturbance rejection control(ADRC)has been widely used in distillation systems,but it has limita...The high-purity distillation column system is strongly nonlinear and coupled,which makes it difficult to control.Active disturbance rejection control(ADRC)has been widely used in distillation systems,but it has limitations in controlling distillation systems with large time delays since ADRC employs ESO and feedback control law to estimate the total disturbance of the system without considering the large time delays.This paper designs a proportion integral-type active disturbance rejection generalized predictive control(PI-ADRGPC)algorithm to control the distillation column system with large time delay.It replaces the PD controller in ADRC with a proportion integral-type generalized predictive control(PI-GPC),thereby improving the performance of control systems with large time delays.Since the proposed controller has many parameters and is difficult to tune,this paper proposes to use the grey wolf optimization(GWO)to tune these parameters,whose structure can also be used by other intelligent optimization algorithms.The performance of GWO tuned PI-ADRGPC is compared with the control performance of GWO tuned ADRC method,multi-verse optimizer(MVO)tuned PI-ADRGPC and MVO tuned ADRC.The simulation results show that the proposed strategy can track reference well and has a good disturbance rejection performance.展开更多
In the practical application of pneumatic control devices, the nonlinearity of a pneumatic control valve become the main factor affecting the control effect, which comes mainly from the dynamic friction force. The dyn...In the practical application of pneumatic control devices, the nonlinearity of a pneumatic control valve become the main factor affecting the control effect, which comes mainly from the dynamic friction force. The dynamic friction inside the valve may cause hysteresis and a dead zone. In this paper, a dither compensation mechanism is proposed to reduce negative effects on the basis of analyzing the mechanism of friction force. The specific dither signal（using a sinusoidal signal） was superimposed on the control signal of the valve. Based on the relationship between the parameters of the dither signal and the inherent characteristics of the proportional servo valve, a parameter tuning method was proposed, which uses a displacement sensor to measure the maximum static friction inside the valve. According to the experimental results, the proper amplitude ranges are determined for different pressures. In order to get the optimal parameters of the dither signal, some dither compensation experiments have been carried out on different signal amplitude and gas pressure conditions. Optimal parameters are determined under two kinds of pressure conditions. Using tuning parameters the valve spool displacement experiment has been taken. From the experiment results, hysteresis of the proportional servo valve is significantly reduced. And through simulation and experiments, the cut-off frequency of the proportional valve has also been widened. Therefore after adding the dither signal, the static and dynamic characteristics of the proportional valve are both improved to a certain degree. This research proposes a parameter tuning method of dither signal, and the validity of the method is verified experimentally.展开更多
The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of th...The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response,a novel tuning method based on machine learning and improved particle swarm optimization(PSO)is proposed.In this method,the relationship between MPC controller parameters and time domain performance indices is established via machine learning.Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices.In addition,the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method.Finally,the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.展开更多
Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local s...Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.展开更多
Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a nov...Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed.First,two novel path tracking performance indices,i.e.,steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second,the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique,and then a novel controller structure which can automatically tune the control parameters online is further designed.Finally,experimental verification with an actual wheeled mobile robot is conducted,which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity,accuracy and adaptability of the robot path tracking.展开更多
Artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms. ABC has been developed by being inspired foraging and waggle dance behaviors of real bee colonies in 2005. Since its invention...Artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms. ABC has been developed by being inspired foraging and waggle dance behaviors of real bee colonies in 2005. Since its invention in 2005, many ABC models have been proposed in order to solve different optimization problems. In all the models proposed, there are only one scout bee and a constant limit value used as control parameters for the bee population. In this study, the performance of ABC algorithm on the numeric optimization problems was analyzed by using different number of scout bees and limit values. Experimental results show that the results obtained by using more than one scout bee and different limit values, are better than the results of basic ABC. Therefore, the control parameters of the basic ABC should be tuned according to given class of optimization problems. In this paper, we propose reasonable value ranges of control parameters for the basic ABC in order to obtain better results on the numeric optimization problems.展开更多
This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging...This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.展开更多
The study of exoskeletons has been a popular topic worldwide.However,there is still a long way to go before exoskeletons can be widely used.One of the major challenges is control,and there is no specific research tren...The study of exoskeletons has been a popular topic worldwide.However,there is still a long way to go before exoskeletons can be widely used.One of the major challenges is control,and there is no specific research trend for controlling exoskeletons.In this paper,we propose a novel exoskeleton control strategy that combines Active Disturbance Rejection Control(ADRC)and Deep Reinforcement Learning(DRL).The dynamic model of the exoskeleton is constructed,followed with the design of the ADRC.To automatically adjust the control parameters of the ADRC,the Twin-Delayed Deep Deterministic Policy Gradient(TD3)is utilized.Then a reward function is defined in terms of the joint angle,angular velocity,and their errors to the desired values,to maximize the accuracy of the joint angle.In the simulations and experiments,a conventional ADRC,and ADRC based on Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)were carried out for comparison with the proposed control method.The results of the tests show that TD3-ADRC has a rapid response,small overshoot,and low Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)followed with the desired,demonstrating the superiority of the proposed control method for the self-learning control of exoskeleton.展开更多
Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters...Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,展开更多
Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning thes...Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.展开更多
To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID co...To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.展开更多
The IMC(Internal Model Control) controller based on robust tuning can improve the robustness and dynamic performance of the system.In this paper,the robustness degree of the control system is investigated based on Max...The IMC(Internal Model Control) controller based on robust tuning can improve the robustness and dynamic performance of the system.In this paper,the robustness degree of the control system is investigated based on Maximum Sensitivity(Ms) in depth.And the analytical relationship is obtained between the robustness specification and controller parameters,which gives a clear design criterion to robust IMC controller.Moreover,a novel and simple IMC-PID(Proportional-Integral-Derivative) tuning method is proposed by converting the IMC controller to PID form in terms of the time domain rather than the frequency domain adopted in some conventional IMC-based methods.Hence,the presented IMC-PID gives a good performance with a specific robustness degree.The new IMC-PID method is compared with other classical IMC-PID rules,showing the flexibility and feasibility for a wide range of plants.展开更多
A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm ( ASA) . For a given control system with this kind of PID controller, a group of optimal ...A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm ( ASA) . For a given control system with this kind of PID controller, a group of optimal PID controller parameters K p * , T i * , and T d * can be obtained by taking the overshoot, settling time, and steady-state error of the system's unit step response as the performance indexes and by use of our improved ant system algorithm. K p * , T i * , and T d * can be used in real-time control. This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters, including the Ziegler-Nichols method (ZN),the genetic algorithm (GA),and the simulated annealing (SA), were adopted for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better performance compared with the ZN-PID and GA-PID controllers, and comparable performance compared with the SA-PID controller.展开更多
文摘Active disturbance rejection controller (ADRC) has good performance in induction motor (IM) control system, but controller parameter is difficult to tune. A method of tuning ADRC parameter by time scale is analyzed. The IM time scale is obtained by theoretical analysis. Combining the relations between scale time and ADRC parameters, ADRC parameter tuning in IM vector control based stator flux oriented is obtained. This parameter tuning method is validated by simulations and it provides a new technique for tuning of ADRC parameters of IM.
文摘a new strategy combining an expert system and improved genetic algorithms is presented for tuning proportional-integral-derivative （PID） parameters for petrochemical processes. This retains the advantages of genetic algorithms, namely rapid convergence and attainment of the global optimum. Utilization of an orthogonal experiment method solves the determination of the genetic factors. Combination with an expert system can make best use of the actual experience of the plant operators. Simulation results of typical process systems examples show a good control performance and robustness.
基金This paper is supported by the Major State Basic Research Development Program of China under Grant No2005CB724101the Key Items Program of International Science and Technology Cooperation of China under Grant No2003DF000021
文摘Based on the evaluation of dynamic performance for feed drives in machine tools, this paper presents a two-stage tuning method of servo parameters. In the first stage, the evaluation of dynamic performance, parameter tuning and optimization on a mechatronic integrated system simulation platform of feed drives are performed. As a result, a servo parameter combination is acquired. In the second stage, the servo parameter combination from the first stage is set and tuned further in a real machine tool whose dynamic performance is measured and evaluated using the cross grid encoder developed by Heidenhain GmbH. A case study shows that this method simplifies the test process effectively and results in a good dynamic performance in a real machine tool.
文摘To improve welding quality, a method of proportional-integral-differential （PlD） parameters tuning based on pulsed gas metal arc welding （P-GMAW） control was put forward. Aiming at the request of dynamic responsiveness of PGMA W constant current control, a self-developed welding waveform wavelet analyzer was employed. By tuning the proportional parameter, integration time and differential time in sequence, the optimal PID parameters could be achieved. The results showed that, due to the PID parameters tuned by this method, the welding process was stable and the weld bead appearance was nice. The requirement of dynamic responsiveness of P-GMAW constant current control was fully met.
基金Project(2007011049) supported by the Natural Science Foundation of Shanxi Province,China
文摘To overcome the deficiencies addressed in the conventional PID control and improve the dynamic performance and robustness of the system, a simple design and parameters tuning approach of internal model control-PID （IMC-PID） controller was proposed for the first order plus time-delay （FOPTD） process and the second order plus time-delay （SOPTD） process. By approximating the time-delay term of the process model with the first-order Taylor series, the expressions for IMC-PID controller parameters were derived, and they had only one adjustable parameter 2 which was directly related to the dynamic performance and robustness of the system. Moreover, an analytical approach of selecting 2 was given based on the maximum sensitivity Ms. Then, the robust tuning of the system could be achieved according to the value of Ms. In addition, the proposed method could be extended to the integrator plus time-delay （IPTD） process and the first order delay integrating （FODI） process. Simulation studies were carried out on various processes with time-delay, and the results show that the proposed method could provide a better dynamic performance of both the set-point tracking and disturbance rejection and robustness against parameters perturbation.
基金Supported by the Special Funds for Major State Basic Research Program of China (973 Program,No.2002CB312200)the Na-tional Natural Science Foundation of China (No.60574019,No.60474045)+1 种基金the Key Technologies R&D Program of Zhejiang Province (No.2005C21087)the Academician Foundation of Zhejiang Province (No.2005A1001-13).
文摘This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an efficient parameter tuning procedure(based on minimization of radius/margin bound for SVM's leave-one-out errors)into a multi-class classification strategy using a fuzzy decision factor,which is named fuzzy support vector machine(FSVM).The datasets generated from the Tennessee Eastman process(TEP)simulator were used to evaluate the clas-sification performance.To decrease the negative influence of the auto-correlated and irrelevant variables,a key vari-able identification procedure using recursive feature elimination,based on the SVM is implemented,with time lags incorporated,before every classifier is trained,and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation.Performance comparisons are implemented among several kinds of multi-class decision machines,by which the effectiveness of the proposed approach is proved.
基金Supported by National Natural Science Foundation of China(Grant Nos.51305293,51135008)
文摘High-speed pick-and-place parallel robot is a system where the inertia imposed on the motor shafts is real-time changing with the system configurations.High quality of computer control with proper controller parameters is conducive to overcoming this problem and has a significant effect on reducing the robot＇s tracking error.By taking Delta robot as an example,a method for parameter tuning of the fixed gain motion controller is presented.Having identifying the parameters of the servo system in the frequency domain by the sinusoidal excitation,the PD＋feedforward control strategy is proposed to adapt to the varying inertia loads,allowing the controller parameters to be tuned by minimizing the mean square tracking error along a typical trajectory.A set of optimum parameters is obtained through computer simulations and the effectiveness of the proposed approach is validated by experiments on a real prototype machine.Let the traveling plate undergoes a specific trajectory and the results show that the tracking error can be reduced by at least 50%in comparison with the conventional auto-tuning and Z-N methods.The proposed approach is a whole workspace optimization and can be applied to the parameter tuning of fixed gain motion controllers.
基金funded by the National Natural Science Foundation of China(61973175,62073177 and 61973172)South African National Research Foundation(132797)+2 种基金South African National Research Foundation Incentive(114911)Eskom Tertiary Education Support Programme Grant of South AfricaTianjin Research Innovation Project for Postgraduate Students(2021YJSB018,2020YJSB003)。
文摘The high-purity distillation column system is strongly nonlinear and coupled,which makes it difficult to control.Active disturbance rejection control(ADRC)has been widely used in distillation systems,but it has limitations in controlling distillation systems with large time delays since ADRC employs ESO and feedback control law to estimate the total disturbance of the system without considering the large time delays.This paper designs a proportion integral-type active disturbance rejection generalized predictive control(PI-ADRGPC)algorithm to control the distillation column system with large time delay.It replaces the PD controller in ADRC with a proportion integral-type generalized predictive control(PI-GPC),thereby improving the performance of control systems with large time delays.Since the proposed controller has many parameters and is difficult to tune,this paper proposes to use the grey wolf optimization(GWO)to tune these parameters,whose structure can also be used by other intelligent optimization algorithms.The performance of GWO tuned PI-ADRGPC is compared with the control performance of GWO tuned ADRC method,multi-verse optimizer(MVO)tuned PI-ADRGPC and MVO tuned ADRC.The simulation results show that the proposed strategy can track reference well and has a good disturbance rejection performance.
基金Supported by National Natural Science Foundation of China(Grant No.51375045)the State Key Laboratory Program(Grant No.GZKF-201214)
文摘In the practical application of pneumatic control devices, the nonlinearity of a pneumatic control valve become the main factor affecting the control effect, which comes mainly from the dynamic friction force. The dynamic friction inside the valve may cause hysteresis and a dead zone. In this paper, a dither compensation mechanism is proposed to reduce negative effects on the basis of analyzing the mechanism of friction force. The specific dither signal（using a sinusoidal signal） was superimposed on the control signal of the valve. Based on the relationship between the parameters of the dither signal and the inherent characteristics of the proportional servo valve, a parameter tuning method was proposed, which uses a displacement sensor to measure the maximum static friction inside the valve. According to the experimental results, the proper amplitude ranges are determined for different pressures. In order to get the optimal parameters of the dither signal, some dither compensation experiments have been carried out on different signal amplitude and gas pressure conditions. Optimal parameters are determined under two kinds of pressure conditions. Using tuning parameters the valve spool displacement experiment has been taken. From the experiment results, hysteresis of the proportional servo valve is significantly reduced. And through simulation and experiments, the cut-off frequency of the proportional valve has also been widened. Therefore after adding the dither signal, the static and dynamic characteristics of the proportional valve are both improved to a certain degree. This research proposes a parameter tuning method of dither signal, and the validity of the method is verified experimentally.
基金supported by the National Natural Science Foundation of China(No.61903291)Key Research and Development Program of Shaanxi Province(No.2022NY-094)。
文摘The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response,a novel tuning method based on machine learning and improved particle swarm optimization(PSO)is proposed.In this method,the relationship between MPC controller parameters and time domain performance indices is established via machine learning.Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices.In addition,the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method.Finally,the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.
文摘Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.
基金the National Natural Science Foundation of China(No.61903291)the Key Research and Development Program of Shaanxi Province(No.2022NY-094)。
文摘Model predictive control(MPC)is a model-based optimal control strategy widely used in robot systems.In this work,the MPC controller tuning problem for the path tracking of the wheeled mobile robot is studied and a novel self-tuning approach is developed.First,two novel path tracking performance indices,i.e.,steadystate time ratio and steady-state distance ratio are proposed to more accurately reflect the control performance.Second,the mapping relationship between the proposed indices and the MPC parameters is established based on machine learning technique,and then a novel controller structure which can automatically tune the control parameters online is further designed.Finally,experimental verification with an actual wheeled mobile robot is conducted,which shows that the proposed method could outperform the existing method via achieving significant improvement in the rapidity,accuracy and adaptability of the robot path tracking.
基金supported by the Selcuk University Scientific Projects Coordinatorship
文摘Artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms. ABC has been developed by being inspired foraging and waggle dance behaviors of real bee colonies in 2005. Since its invention in 2005, many ABC models have been proposed in order to solve different optimization problems. In all the models proposed, there are only one scout bee and a constant limit value used as control parameters for the bee population. In this study, the performance of ABC algorithm on the numeric optimization problems was analyzed by using different number of scout bees and limit values. Experimental results show that the results obtained by using more than one scout bee and different limit values, are better than the results of basic ABC. Therefore, the control parameters of the basic ABC should be tuned according to given class of optimization problems. In this paper, we propose reasonable value ranges of control parameters for the basic ABC in order to obtain better results on the numeric optimization problems.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00222536).
文摘This study provides an in-depth comparative evaluation of landslide susceptibility using two distinct spatial units:and slope units(SUs)and hydrological response units(HRUs),within Goesan County,South Korea.Leveraging the capabilities of the extreme gradient boosting(XGB)algorithm combined with Shapley Additive Explanations(SHAP),this work assesses the precision and clarity with which each unit predicts areas vulnerable to landslides.SUs focus on the geomorphological features like ridges and valleys,focusing on slope stability and landslide triggers.Conversely,HRUs are established based on a variety of hydrological factors,including land cover,soil type and slope gradients,to encapsulate the dynamic water processes of the region.The methodological framework includes the systematic gathering,preparation and analysis of data,ranging from historical landslide occurrences to topographical and environmental variables like elevation,slope angle and land curvature etc.The XGB algorithm used to construct the Landslide Susceptibility Model(LSM)was combined with SHAP for model interpretation and the results were evaluated using Random Cross-validation(RCV)to ensure accuracy and reliability.To ensure optimal model performance,the XGB algorithm’s hyperparameters were tuned using Differential Evolution,considering multicollinearity-free variables.The results show that SU and HRU are effective for LSM,but their effectiveness varies depending on landscape characteristics.The XGB algorithm demonstrates strong predictive power and SHAP enhances model transparency of the influential variables involved.This work underscores the importance of selecting appropriate assessment units tailored to specific landscape characteristics for accurate LSM.The integration of advanced machine learning techniques with interpretative tools offers a robust framework for landslide susceptibility assessment,improving both predictive capabilities and model interpretability.Future research should integrate broader data sets and explore hybrid analytical models to strengthen the generalizability of these findings across varied geographical settings.
基金funded by Zhiyuan Laboratory(Grant NO.ZYL2024017a).
文摘The study of exoskeletons has been a popular topic worldwide.However,there is still a long way to go before exoskeletons can be widely used.One of the major challenges is control,and there is no specific research trend for controlling exoskeletons.In this paper,we propose a novel exoskeleton control strategy that combines Active Disturbance Rejection Control(ADRC)and Deep Reinforcement Learning(DRL).The dynamic model of the exoskeleton is constructed,followed with the design of the ADRC.To automatically adjust the control parameters of the ADRC,the Twin-Delayed Deep Deterministic Policy Gradient(TD3)is utilized.Then a reward function is defined in terms of the joint angle,angular velocity,and their errors to the desired values,to maximize the accuracy of the joint angle.In the simulations and experiments,a conventional ADRC,and ADRC based on Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)were carried out for comparison with the proposed control method.The results of the tests show that TD3-ADRC has a rapid response,small overshoot,and low Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)followed with the desired,demonstrating the superiority of the proposed control method for the self-learning control of exoskeleton.
文摘Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,
文摘Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.
基金the National Natural Science Foundation of China (60375001)the Scientific Research Foundation of Hunan Provincial Education Department (05B016).
文摘To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.
基金Supported by the National Natural Science Foundation of China（61273132）Doctoral Fund of Ministry of Education of China（20110010110010）
文摘The IMC(Internal Model Control) controller based on robust tuning can improve the robustness and dynamic performance of the system.In this paper,the robustness degree of the control system is investigated based on Maximum Sensitivity(Ms) in depth.And the analytical relationship is obtained between the robustness specification and controller parameters,which gives a clear design criterion to robust IMC controller.Moreover,a novel and simple IMC-PID(Proportional-Integral-Derivative) tuning method is proposed by converting the IMC controller to PID form in terms of the time domain rather than the frequency domain adopted in some conventional IMC-based methods.Hence,the presented IMC-PID gives a good performance with a specific robustness degree.The new IMC-PID method is compared with other classical IMC-PID rules,showing the flexibility and feasibility for a wide range of plants.
基金This work was supported by the National Natural Science Foundation of China (No. 50275150)the Foundation of Robotics Laboratory, Chinese Academy of Sciences( No. RL200002).
文摘A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm ( ASA) . For a given control system with this kind of PID controller, a group of optimal PID controller parameters K p * , T i * , and T d * can be obtained by taking the overshoot, settling time, and steady-state error of the system's unit step response as the performance indexes and by use of our improved ant system algorithm. K p * , T i * , and T d * can be used in real-time control. This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters, including the Ziegler-Nichols method (ZN),the genetic algorithm (GA),and the simulated annealing (SA), were adopted for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better performance compared with the ZN-PID and GA-PID controllers, and comparable performance compared with the SA-PID controller.
