This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain ...This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain controller. When the frequency-domain LMS step size is allowed to vary as a function of frequency,the frequency-domain algorithm exhibits a better vibration reduction than the time-domain algorithm for the weaker frequencies in the energy spectrum.展开更多
This paper describes empirical research on the model, optimization and supervisory control of beer fermentation.Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathe...This paper describes empirical research on the model, optimization and supervisory control of beer fermentation.Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results.The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs).Successful finding of optimal ways to drive these processes were reported.Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.展开更多
On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in t...On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.展开更多
To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole con...To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole configuration self tuning control algorithm, were proposed. The former can make the variance of the output minimum while the latter can make dynamic behavior satisfying. The stability of the two schemes was analyzed. Simulations of them show that the acceleration in the vertical direction has been reduced greatly. The purpose of reducing vibration is realized. The two schemes can reduce the vibration in the suspension and have some practicability.展开更多
Based on the analysis of the mechanism of wire tension control by using torque motors in the multi-wire saw machining process, some mathematical models of a tension control system are studied, and an adaptive algo- ri...Based on the analysis of the mechanism of wire tension control by using torque motors in the multi-wire saw machining process, some mathematical models of a tension control system are studied, and an adaptive algo- rithm is designed for controlling the wire tension. In this algorithm of tension control, the rotation speeds and waving angle of motors are measured and fed back to the controller, and the NLMS（ normalized least mean squares） algorithm is used to calculate the adaptive correction value and control the wire tension accurately. The computer simulation results in Matlab software validate the high accuracy for controlling the system of the wire tension with the NLMS algorithm in the multi-wire saw machining process.展开更多
We use cellular automata for simulating a series of topology control algorithms in Wireless Sensor Networks (WSNs) using various programming environments. A cellular automaton is a decentralized computing model provid...We use cellular automata for simulating a series of topology control algorithms in Wireless Sensor Networks (WSNs) using various programming environments. A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computations using only local information. WSNs are composed of a large number of distributed wireless sensor nodes operating on batteries. The objective of the topology control problem in WSNs is to select an appropriate subset of nodes able to monitor a region at a minimum energy consumption cost and, therefore, extend network lifetime. Herein, we present topology control algorithms based on the selection—in a deterministic or randomized way—of an appropriate subset of sensor nodes that must remain active. We use cellular automata for conducting simulations in order to evaluate the performance of these algorithms and investigate the effect/role of the neighbourhood selection in the efficient application of our algorithms. Furthermore, we implement our simulations in Matlab, Java and Python in order to investigate in which ways the selection of an appropriate programming environment can facilitate experimentation and can result in more efficient application of our algorithms.展开更多
For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to in...For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.展开更多
Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorith...Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control （AVC） algorithm for a flexible beam system simulated using the finite difference （FD） method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.展开更多
The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freed...The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed. The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance. For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band. The simulation results show that LMS adaptive control is simple and remarkably effective. It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.展开更多
Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach wa...Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.展开更多
This paper presents an analysis of the KM （Karnik-Mendel） algorithms performance under real time implementation using 3 types： the non-iterative, the iterative and the enhanced, and their feasibility for real-time ...This paper presents an analysis of the KM （Karnik-Mendel） algorithms performance under real time implementation using 3 types： the non-iterative, the iterative and the enhanced, and their feasibility for real-time interval type 2 fuzzy logic control system applications. The results are also compared against NT （Nie-Tan） method that is one of the fastest and simplest defuzzification methods. Because the DC （direct current） servo-motor is one of the most used motors in different industrial applications and the model of the motor is nonlinear, this motor was selected for validating the implementation in real time hardware. This DC motor is a perfect option for studying the real time performance of KM algorithms in order to show up its limits and possibilities for real-time control system applications. These methodologies are implemented in National Instruments LabVIEW FPGA （field programmable gate array） module hardware which is one of the most used platforms in the industry. The results show that the E-KM （enhanced KM） algorithm and the NT method present good results for implementing real-time control applications in real time hardware. Although fuzzy logic type 2 is a good option for working with nonlinear and noise from the sensors, the defuzzification method has to react in a short period of time in order to allow good control response. Hence, a complete study of defuzzification is needed for improving the real time implementations of fuzzy type 2.展开更多
A switched reluctance machine （SRM） drive is a time-varying, strongly nonlinear system. High performance control can no longer be achieved by using linear techniques. This paper describes the back-propagation （BP）...A switched reluctance machine （SRM） drive is a time-varying, strongly nonlinear system. High performance control can no longer be achieved by using linear techniques. This paper describes the back-propagation （BP） neural network-based proportional-integral-derivative （PID） speed control of the SRM. It＇s the interest of this paper to explore the utilization of the prior empirical knowledge as guidance in the initializing and training of the neural networks. The purpose is to make the networks less sensitive on the initial weights. Two modified algorithms are presented and simulation experiments show some interesting findings about their control effects and their corresponding sensitivity on the initial weights of the networks.展开更多
The quadrotor unmanned aerial vehicle is a great platform for control systems research as its nonlinear nature and under-actuated configuration make it ideal to synthesize and analyze control algorithms. After a brief...The quadrotor unmanned aerial vehicle is a great platform for control systems research as its nonlinear nature and under-actuated configuration make it ideal to synthesize and analyze control algorithms. After a brief explanation of the system, several algorithms have been analyzed including their advantages and disadvantages: PID, Linear Quadratic Regulator (LQR), Sliding mode, Backstepping, Feedback linearization, Adaptive, Robust, Optimal, L1, H∞, Fuzzy logic and Artificial neutral networks. The conclusion of this work is a proposal of hybrid systems to be considered as they combine advantages from more than one control philosophy.展开更多
To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency, we propose a cooperative algorithm to analyze and design the controller, in which el...To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency, we propose a cooperative algorithm to analyze and design the controller, in which elements of conventional mathematical optimization algorithms are combined with adaptive dynamic elements drawn from intelligent control theory. In our design, the sequential quadratic programming algorithm was first utilized to obtain an optimal solution for power distribution among multiple units. Fuzzy system was then developed to implement the optimal strategies on the basis of optimal solution. In addition, parameters of the fuzzy system were adapted via a genetic algorithm. Tbe simulation results illustrate that the methodology described is useful for a range of control system designs.展开更多
For a gantry crane system, this paper presents a comparison between four control algorithms. These algo-rithms are being compared on simplicity, stability and robustness. Goal for the controller is to move the load on...For a gantry crane system, this paper presents a comparison between four control algorithms. These algo-rithms are being compared on simplicity, stability and robustness. Goal for the controller is to move the load on a gantry crane to a new position with minimal overshoot of the load and maximal speed of the load. An-other goal is to provide an insight in the behaviour of the possible controllers. In this article a parallel P-controller, cascade P-controller, fuzzy controller and an internal model controller are used. To be able to validate and design the controllers a model is derived from the gantry crane. The controllers and the model are being implemented in Matlab Simulink. Finally the controllers are validated and tuned in Labview on a laboratory gantry scrane scale model. Main conclusion is that all presented controllers can be used as a con-troller for the gantry crane system but the fuzzy controller is showing the best performance.展开更多
In the last five years,there has been a V-shaped recovery in the number of papers on congestion control algorithms on the Internet.In this paper,congestion problems on the Internet are discussed,such as congestion col...In the last five years,there has been a V-shaped recovery in the number of papers on congestion control algorithms on the Internet.In this paper,congestion problems on the Internet are discussed,such as congestion collapse and bufferbloat from the perspective of the necessity of congestion control algorithms.The typical congestion control algorithms are introduced,and the research areas and methods of congestion control algorithms are described.Recent research trends and future prospects of congestion control algorithms are also presented.展开更多
The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and wi...The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.展开更多
This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linea...This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linear model(SGLM),elastic net(ENET),partial least square(PLS),ridge regression,support vector machine(SVM),classification and regression trees(CART),bagged CART,and random forest(RF)for gully erosion susceptibility mapping(GESM)in Iran.The location of 462 previously existing gully erosion sites were mapped through widespread field investigations,of which 70%(323)and 30%(139)of observations were arbitrarily divided for algorithm calibration and validation.Twelve controlling factors for gully erosion,namely,soil texture,annual mean rainfall,digital elevation model(DEM),drainage density,slope,lithology,topographic wetness index(TWI),distance from rivers,aspect,distance from roads,plan curvature,and profile curvature were ranked in terms of their importance using each MLA.The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE(root mean square error),MAE(mean absolute error),and R-squared.Based on the comparisons among MLA,the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared,and was therefore selected as the best model.The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance.According to the GESM generated using RF,most of the study area is predicted to have a low(53.72%)or moderate(29.65%)susceptibility to gully erosion,whereas only a small area is identified to have a high(12.56%)or very high(4.07%)susceptibility.The outcome generated by RF model is validated using the ROC(Receiver Operating Characteristics)curve approach,which returned an area under the curve(AUC)of 0.985,proving the excellent forecasting ability of the model.The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.展开更多
This paper presents the automatic drug administration for the regulation of bispectral (BIS) index in the anesthesia process during the clinical surgery by controlling the concentration target of two drugs, namely, pr...This paper presents the automatic drug administration for the regulation of bispectral (BIS) index in the anesthesia process during the clinical surgery by controlling the concentration target of two drugs, namely, propofol and remifentanil. To realize the automatic drug administration, real clinical data are collected for 42 patients for the construction of patients’ models consisting of pharmacokinetic and pharmacodynamic models describing the dynamics reacting to the input drugs. A nominal anesthesia model is obtained by taking the average of 42 patients’ models for the design of control scheme. Three PID controllers are employed, namely linear PID controller, type-1 (T1) fuzzy PID controller and interval type-2 (IT2) fuzzy PID controller, to regulate the BIS index using the nominal patient’s model. The PID gains and membership functions are obtained using genetic algorithm (GA) by minimizing a cost function measuring the control performance. The best trained PID controllers are tested under different scenarios and compared in terms of control performance. Simulation results show that the IT2 fuzzy PID controller offers the best control strategy regulating the BIS index while the T1 fuzzy PID controller comes the second.展开更多
文摘This paper describes the implementation of frequency-domain least mean squares (LMS) and Filtered-X algorithms and compares the performance of the frequencydomain adaptive control algorithm to a comparable timedomain controller. When the frequency-domain LMS step size is allowed to vary as a function of frequency,the frequency-domain algorithm exhibits a better vibration reduction than the time-domain algorithm for the weaker frequencies in the energy spectrum.
文摘This paper describes empirical research on the model, optimization and supervisory control of beer fermentation.Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results.The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs).Successful finding of optimal ways to drive these processes were reported.Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.
文摘On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.
文摘To reduce the vibration in the suspension, semi active suspension system was employed. And its CARMA model was built. Two adaptive control schemes, the minimum variance self tuning control algorithm and the pole configuration self tuning control algorithm, were proposed. The former can make the variance of the output minimum while the latter can make dynamic behavior satisfying. The stability of the two schemes was analyzed. Simulations of them show that the acceleration in the vertical direction has been reduced greatly. The purpose of reducing vibration is realized. The two schemes can reduce the vibration in the suspension and have some practicability.
基金supported by the National Natural Science Fundproject (50775207)Key Laboratory of E & M (Zhejiang University of Technology) Open Fund project (2009EP017)
文摘Based on the analysis of the mechanism of wire tension control by using torque motors in the multi-wire saw machining process, some mathematical models of a tension control system are studied, and an adaptive algo- rithm is designed for controlling the wire tension. In this algorithm of tension control, the rotation speeds and waving angle of motors are measured and fed back to the controller, and the NLMS（ normalized least mean squares） algorithm is used to calculate the adaptive correction value and control the wire tension accurately. The computer simulation results in Matlab software validate the high accuracy for controlling the system of the wire tension with the NLMS algorithm in the multi-wire saw machining process.
文摘We use cellular automata for simulating a series of topology control algorithms in Wireless Sensor Networks (WSNs) using various programming environments. A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computations using only local information. WSNs are composed of a large number of distributed wireless sensor nodes operating on batteries. The objective of the topology control problem in WSNs is to select an appropriate subset of nodes able to monitor a region at a minimum energy consumption cost and, therefore, extend network lifetime. Herein, we present topology control algorithms based on the selection—in a deterministic or randomized way—of an appropriate subset of sensor nodes that must remain active. We use cellular automata for conducting simulations in order to evaluate the performance of these algorithms and investigate the effect/role of the neighbourhood selection in the efficient application of our algorithms. Furthermore, we implement our simulations in Matlab, Java and Python in order to investigate in which ways the selection of an appropriate programming environment can facilitate experimentation and can result in more efficient application of our algorithms.
基金supported by the National Natural Science Foundation of China (62173333, 12271522)Beijing Natural Science Foundation (Z210002)the Research Fund of Renmin University of China (2021030187)。
文摘For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.
文摘Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control （AVC） algorithm for a flexible beam system simulated using the finite difference （FD） method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.
文摘The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed. The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance. For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band. The simulation results show that LMS adaptive control is simple and remarkably effective. It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.
文摘Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.
文摘This paper presents an analysis of the KM （Karnik-Mendel） algorithms performance under real time implementation using 3 types： the non-iterative, the iterative and the enhanced, and their feasibility for real-time interval type 2 fuzzy logic control system applications. The results are also compared against NT （Nie-Tan） method that is one of the fastest and simplest defuzzification methods. Because the DC （direct current） servo-motor is one of the most used motors in different industrial applications and the model of the motor is nonlinear, this motor was selected for validating the implementation in real time hardware. This DC motor is a perfect option for studying the real time performance of KM algorithms in order to show up its limits and possibilities for real-time control system applications. These methodologies are implemented in National Instruments LabVIEW FPGA （field programmable gate array） module hardware which is one of the most used platforms in the industry. The results show that the E-KM （enhanced KM） algorithm and the NT method present good results for implementing real-time control applications in real time hardware. Although fuzzy logic type 2 is a good option for working with nonlinear and noise from the sensors, the defuzzification method has to react in a short period of time in order to allow good control response. Hence, a complete study of defuzzification is needed for improving the real time implementations of fuzzy type 2.
基金the Programme of Introducing Talents of Discipline to Universities(No.B06012)
文摘A switched reluctance machine （SRM） drive is a time-varying, strongly nonlinear system. High performance control can no longer be achieved by using linear techniques. This paper describes the back-propagation （BP） neural network-based proportional-integral-derivative （PID） speed control of the SRM. It＇s the interest of this paper to explore the utilization of the prior empirical knowledge as guidance in the initializing and training of the neural networks. The purpose is to make the networks less sensitive on the initial weights. Two modified algorithms are presented and simulation experiments show some interesting findings about their control effects and their corresponding sensitivity on the initial weights of the networks.
文摘The quadrotor unmanned aerial vehicle is a great platform for control systems research as its nonlinear nature and under-actuated configuration make it ideal to synthesize and analyze control algorithms. After a brief explanation of the system, several algorithms have been analyzed including their advantages and disadvantages: PID, Linear Quadratic Regulator (LQR), Sliding mode, Backstepping, Feedback linearization, Adaptive, Robust, Optimal, L1, H∞, Fuzzy logic and Artificial neutral networks. The conclusion of this work is a proposal of hybrid systems to be considered as they combine advantages from more than one control philosophy.
基金supported by National Natural Science Foundation of China(61573194,61374180,61573096)China Postdoctoral Science Foundation Funded Project(2013M530229)+3 种基金China Postdoctoral Science Special Foundation Funded Project(2014T70463)Six Talent Peaks High Level Project of Jiangsu Province(ZNDW-004)Science Foundation of Nanjing University of Posts and Telecommunications(NY213095)Australian Research Council(DP120104986)
基金Sponsored by the Indiana 21st Century Research and Technology Fund
文摘To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency, we propose a cooperative algorithm to analyze and design the controller, in which elements of conventional mathematical optimization algorithms are combined with adaptive dynamic elements drawn from intelligent control theory. In our design, the sequential quadratic programming algorithm was first utilized to obtain an optimal solution for power distribution among multiple units. Fuzzy system was then developed to implement the optimal strategies on the basis of optimal solution. In addition, parameters of the fuzzy system were adapted via a genetic algorithm. Tbe simulation results illustrate that the methodology described is useful for a range of control system designs.
文摘For a gantry crane system, this paper presents a comparison between four control algorithms. These algo-rithms are being compared on simplicity, stability and robustness. Goal for the controller is to move the load on a gantry crane to a new position with minimal overshoot of the load and maximal speed of the load. An-other goal is to provide an insight in the behaviour of the possible controllers. In this article a parallel P-controller, cascade P-controller, fuzzy controller and an internal model controller are used. To be able to validate and design the controllers a model is derived from the gantry crane. The controllers and the model are being implemented in Matlab Simulink. Finally the controllers are validated and tuned in Labview on a laboratory gantry scrane scale model. Main conclusion is that all presented controllers can be used as a con-troller for the gantry crane system but the fuzzy controller is showing the best performance.
基金supported by JSPS Grants-in-Aid for Scientific Research JP20K11786 and JP21KK0202.
文摘In the last five years,there has been a V-shaped recovery in the number of papers on congestion control algorithms on the Internet.In this paper,congestion problems on the Internet are discussed,such as congestion collapse and bufferbloat from the perspective of the necessity of congestion control algorithms.The typical congestion control algorithms are introduced,and the research areas and methods of congestion control algorithms are described.Recent research trends and future prospects of congestion control algorithms are also presented.
基金Project(51090385) supported by the Major Program of National Natural Science Foundation of ChinaProject(2011IB001) supported by Yunnan Provincial Science and Technology Program,China+1 种基金Project(2012DFA70570) supported by the International Science & Technology Cooperation Program of ChinaProject(2011IA004) supported by the Yunnan Provincial International Cooperative Program,China
文摘The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.
基金supported by the College of Agriculture,Shiraz University(Grant No.97GRC1M271143)funding from the UK Biotechnology and Biological Sciences Research Council(BBSRC)funded by BBSRC grant award BBS/E/C/000I0330–Soil to Nutrition project 3–Sustainable intensification:optimisation at multiple scales。
文摘This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linear model(SGLM),elastic net(ENET),partial least square(PLS),ridge regression,support vector machine(SVM),classification and regression trees(CART),bagged CART,and random forest(RF)for gully erosion susceptibility mapping(GESM)in Iran.The location of 462 previously existing gully erosion sites were mapped through widespread field investigations,of which 70%(323)and 30%(139)of observations were arbitrarily divided for algorithm calibration and validation.Twelve controlling factors for gully erosion,namely,soil texture,annual mean rainfall,digital elevation model(DEM),drainage density,slope,lithology,topographic wetness index(TWI),distance from rivers,aspect,distance from roads,plan curvature,and profile curvature were ranked in terms of their importance using each MLA.The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE(root mean square error),MAE(mean absolute error),and R-squared.Based on the comparisons among MLA,the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared,and was therefore selected as the best model.The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance.According to the GESM generated using RF,most of the study area is predicted to have a low(53.72%)or moderate(29.65%)susceptibility to gully erosion,whereas only a small area is identified to have a high(12.56%)or very high(4.07%)susceptibility.The outcome generated by RF model is validated using the ROC(Receiver Operating Characteristics)curve approach,which returned an area under the curve(AUC)of 0.985,proving the excellent forecasting ability of the model.The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.
文摘This paper presents the automatic drug administration for the regulation of bispectral (BIS) index in the anesthesia process during the clinical surgery by controlling the concentration target of two drugs, namely, propofol and remifentanil. To realize the automatic drug administration, real clinical data are collected for 42 patients for the construction of patients’ models consisting of pharmacokinetic and pharmacodynamic models describing the dynamics reacting to the input drugs. A nominal anesthesia model is obtained by taking the average of 42 patients’ models for the design of control scheme. Three PID controllers are employed, namely linear PID controller, type-1 (T1) fuzzy PID controller and interval type-2 (IT2) fuzzy PID controller, to regulate the BIS index using the nominal patient’s model. The PID gains and membership functions are obtained using genetic algorithm (GA) by minimizing a cost function measuring the control performance. The best trained PID controllers are tested under different scenarios and compared in terms of control performance. Simulation results show that the IT2 fuzzy PID controller offers the best control strategy regulating the BIS index while the T1 fuzzy PID controller comes the second.
