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
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
Variable-air-volume (VAV) air-conditioning system is a multi-variable system and has multi coupling control loops. While all of the control loops are working together, they interfere and influence each other. A multiv...Variable-air-volume (VAV) air-conditioning system is a multi-variable system and has multi coupling control loops. While all of the control loops are working together, they interfere and influence each other. A multivariable decoupling PID controller is designed for VAV air-conditioning system. Diagonal matrix decoupling method is employed to eliminate the coupling between the loop of supply air temperature and that of thermal-space air temperature. The PID controller parameters are optimized by means of an improved genetic algorithm in floating point representations to obtain better performance. The population in the improved genetic algorithm mutates before crossover, which is helpful for the convergence. Additionally the micro mutation algorithm is proposed and applied to improve the convergence during the later evolution. To search the best parameters, the optimized parameters ranges should be amplified 10 times the initial ideal parameters. The simulation and experiment results show that the decoupling control system is effective and feasible. The method can overcome the strong coupling feature of the system and has shorter governing time and less over-shoot than non-optimization PID control.展开更多
A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller...A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.展开更多
A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustab...A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.展开更多
Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by co...Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and thesimulation results show that satisfactory performances are obtained.展开更多
Based on the ant colony system （ACS） algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scal...Based on the ant colony system （ACS） algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed controller, called the Fuzzy-ACS PID controller, was used to control the CIP-Ⅰ intelligent leg. The simulation experiments demonstrate that this controller has good control performance. Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing, it was verified that the Fuzzy-ACS PID controller has better control performance. Furthermore, the simulation results also verify that the proposed ACS algorithm has quick convergence speed, small solution variation, good dynamic convergence behavior, and high computation efficiency in searching for the optimal input/output scaling factors.展开更多
To get the satisfying performance of a PID controller, this paper presents a novel Pareto-based multi-objective genetic algorithm (MOGA), which can be used to find the appropriate setting of the PID controller by anal...To get the satisfying performance of a PID controller, this paper presents a novel Pareto-based multi-objective genetic algorithm (MOGA), which can be used to find the appropriate setting of the PID controller by analyzing the pareto optimal surfaces. Rated settings of the controller by two criteria, the error between output and reference signals and control moves, are listed on the pareto surface. Appropriate setting can be chosen under a balance between two criteria for different control purposes. A controller tuning problem for a plant with high order and time delay is chosen as an example. Simulation results show that the method of MOGA is more efficient compared with traditional tuning methods.展开更多
According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated ...According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated by MATLAB.As a result that it can improve the response speed and stability of the system,and meet the demand of the system.展开更多
Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging effects.That is ...Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging effects.That is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical industry.In this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain Kd.The controller is used to control Mobile Robot System(MRS)at the required set point.The FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system response.The FF is touted as providing an easy,reliable,and efficient tuning technique for PID controllers.The most suitable ideal performance is accomplished with FF-PID,according to the display in the time response.Further,the observed response is compared to those received by applying GA and conventional off-line tuning techniques.The comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.展开更多
This paper delineates a conventional buck converter controlled by optimized PID controller where Genetic Algorithm (GA) is employed with a view to enhancing the performance by analyzing the performance parameters. Gen...This paper delineates a conventional buck converter controlled by optimized PID controller where Genetic Algorithm (GA) is employed with a view to enhancing the performance by analyzing the performance parameters. Genetic Algorithm is a probabilistic search algorithm which is substantially used as an optimization technique in power electronics. A bunch of modifications have already been introduced to enhance the performance depending upon the applications. However, in this paper, modified genetic algorithm has been used in order to tune the key parameters in the converter. Hence, an analysis is carried out where the performance of the converter is illustrated in terms of rise time, settling time and percentage of overshoot by deploying GA based PID controller and the overall comparative study is presented. Responses of the overall system are accumulated through rigorous simulation in MATLAB environment.展开更多
Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)c...Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented flexibility.In the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search abilities.Both algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior results.Several analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller designs.The performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their success.It is shown that both proposed approaches obtain excellent and robust results among all compared ones.That is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the literature.On the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.展开更多
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.
基金Supported by Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education of China
文摘Variable-air-volume (VAV) air-conditioning system is a multi-variable system and has multi coupling control loops. While all of the control loops are working together, they interfere and influence each other. A multivariable decoupling PID controller is designed for VAV air-conditioning system. Diagonal matrix decoupling method is employed to eliminate the coupling between the loop of supply air temperature and that of thermal-space air temperature. The PID controller parameters are optimized by means of an improved genetic algorithm in floating point representations to obtain better performance. The population in the improved genetic algorithm mutates before crossover, which is helpful for the convergence. Additionally the micro mutation algorithm is proposed and applied to improve the convergence during the later evolution. To search the best parameters, the optimized parameters ranges should be amplified 10 times the initial ideal parameters. The simulation and experiment results show that the decoupling control system is effective and feasible. The method can overcome the strong coupling feature of the system and has shorter governing time and less over-shoot than non-optimization PID control.
文摘A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.
文摘A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x p, x i , and x d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.
文摘Enlightened by distribution of creatures in natural ecology environment, the distributionpopulation-based genetic algorithm (DPGA) is presented in this paper. The searching capability ofthe algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and thesimulation results show that satisfactory performances are obtained.
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProject(20040533035) supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject(05JJ40128) supported by the Natural Science Foundation of Hunan Province, China
文摘Based on the ant colony system （ACS） algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed controller, called the Fuzzy-ACS PID controller, was used to control the CIP-Ⅰ intelligent leg. The simulation experiments demonstrate that this controller has good control performance. Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing, it was verified that the Fuzzy-ACS PID controller has better control performance. Furthermore, the simulation results also verify that the proposed ACS algorithm has quick convergence speed, small solution variation, good dynamic convergence behavior, and high computation efficiency in searching for the optimal input/output scaling factors.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 60504033)
文摘To get the satisfying performance of a PID controller, this paper presents a novel Pareto-based multi-objective genetic algorithm (MOGA), which can be used to find the appropriate setting of the PID controller by analyzing the pareto optimal surfaces. Rated settings of the controller by two criteria, the error between output and reference signals and control moves, are listed on the pareto surface. Appropriate setting can be chosen under a balance between two criteria for different control purposes. A controller tuning problem for a plant with high order and time delay is chosen as an example. Simulation results show that the method of MOGA is more efficient compared with traditional tuning methods.
基金supported by the Chongqing Scientific and Technological Innovating Program under grant CSTC2008AC1014
文摘According to these characteristics of the movement of the special platform servo,a new improved grey predictive PID control algorithm was proposed based on the grey predictive PID,and then the algorithm was simulated by MATLAB.As a result that it can improve the response speed and stability of the system,and meet the demand of the system.
文摘Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging effects.That is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical industry.In this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain Kd.The controller is used to control Mobile Robot System(MRS)at the required set point.The FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system response.The FF is touted as providing an easy,reliable,and efficient tuning technique for PID controllers.The most suitable ideal performance is accomplished with FF-PID,according to the display in the time response.Further,the observed response is compared to those received by applying GA and conventional off-line tuning techniques.The comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.
文摘This paper delineates a conventional buck converter controlled by optimized PID controller where Genetic Algorithm (GA) is employed with a view to enhancing the performance by analyzing the performance parameters. Genetic Algorithm is a probabilistic search algorithm which is substantially used as an optimization technique in power electronics. A bunch of modifications have already been introduced to enhance the performance depending upon the applications. However, in this paper, modified genetic algorithm has been used in order to tune the key parameters in the converter. Hence, an analysis is carried out where the performance of the converter is illustrated in terms of rise time, settling time and percentage of overshoot by deploying GA based PID controller and the overall comparative study is presented. Responses of the overall system are accumulated through rigorous simulation in MATLAB environment.
文摘Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented flexibility.In the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search abilities.Both algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior results.Several analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller designs.The performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their success.It is shown that both proposed approaches obtain excellent and robust results among all compared ones.That is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the literature.On the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.
