The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimiz...The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.展开更多
This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric u...This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.展开更多
This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide...This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.展开更多
The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point d...The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.展开更多
To control the robot and track the designed trajectory with uncertain disturbances in a specified precision range, an adaptive fuzzy control scheme for the robot arm manipulator is discussed. The controller output err...To control the robot and track the designed trajectory with uncertain disturbances in a specified precision range, an adaptive fuzzy control scheme for the robot arm manipulator is discussed. The controller output error method （COEM） is used to design the adaptive fuzzy controller. A few or all of the parameters of the controller are adjusted by using the gradient descent algorithm to minimize the output error. COEM is adopted in the adaptive control system for the robot arm manipulator with 5-DOF. Simulation results show the effectiveness of the method and the real time adjustment of the parameters.展开更多
An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuz...An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.展开更多
To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership functi...To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.展开更多
Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution. A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while kee...Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution. A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.展开更多
To eliminate the node traction coupling during wind turbine blade full-scale static testing,a model free adaptive control algorithm is presented based on fuzzy control performance function compensation. Based on the u...To eliminate the node traction coupling during wind turbine blade full-scale static testing,a model free adaptive control algorithm is presented based on fuzzy control performance function compensation. Based on the universal model theory,the fuzzy model free adaptive control( FMFAC) algorithm is designed by configuring the spot static testing experiences as compensation function F( ·). Then the algorithm implementation process is provided and its quick convergence is proved. Using software to establish static load coupling model of multi-nodes,simulate and verify the validity of FMFAC algorithm,which is applied to wind turbines blade full-scale static testing. The results show that the adaptive decoupling ability of FMFAC is better. The traction of four load points can stay steady and change coordinately. Process error is not over ± 6 k N. The error rate is lower than 1% in special phase.This algorithm effectively eliminates the traction coupling of the static testing process,and makes wind turbine blade testing steadily.展开更多
Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to contro...Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to control GA parameters.The self-learning ability of the cerebellar modelariculation controller (CMAC) neural network makes it possible for on-line learning the knowledge onGAs throughout the run.Automatically designing and tuning the fuzzy knowledge-base system,neuro-fuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learningmethod.The Results from initial experiments show a Dynamic Parametric AGA system designed by theproposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a widerange of combinatorial optimization.展开更多
With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have ev...With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy.展开更多
This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key...This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.展开更多
A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimiz...A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.展开更多
提出了一种无刷直流电机的 F uzzy-PI转速调节器的设计方法。在无刷直流电机的高性能速度跟踪中 ,若仅采用传统的 PI调节器 ,则难以克服系统超调和短时振荡问题。采用复合 F uzzy-PI的控制方法 ,并且采用自适应的权值修正方法来修正 F u...提出了一种无刷直流电机的 F uzzy-PI转速调节器的设计方法。在无刷直流电机的高性能速度跟踪中 ,若仅采用传统的 PI调节器 ,则难以克服系统超调和短时振荡问题。采用复合 F uzzy-PI的控制方法 ,并且采用自适应的权值修正方法来修正 F uzzy回路与 PI回路的连接权值。最后 ,在 Matlab与 Sim ulink下进行了仿真 ,结果表明 ,运用这种设计方法很好地抑制了超调和振荡。展开更多
文摘The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.
基金This work was supported by the National Natural Science Foundation of China (No. 50375001)
文摘This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.
基金supported by the National Natural Science Foundation of China(61973105,62373137)。
文摘This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.
文摘The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.
文摘To control the robot and track the designed trajectory with uncertain disturbances in a specified precision range, an adaptive fuzzy control scheme for the robot arm manipulator is discussed. The controller output error method （COEM） is used to design the adaptive fuzzy controller. A few or all of the parameters of the controller are adjusted by using the gradient descent algorithm to minimize the output error. COEM is adopted in the adaptive control system for the robot arm manipulator with 5-DOF. Simulation results show the effectiveness of the method and the real time adjustment of the parameters.
基金National Natural Science Foundation of China (No.60774023)
文摘An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.
文摘To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.
基金Supported by Basic Research Foundation of National Defence (No. B0203-031)
文摘Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution. A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.
基金National Natural Science Foundation of China(No.51567018)
文摘To eliminate the node traction coupling during wind turbine blade full-scale static testing,a model free adaptive control algorithm is presented based on fuzzy control performance function compensation. Based on the universal model theory,the fuzzy model free adaptive control( FMFAC) algorithm is designed by configuring the spot static testing experiences as compensation function F( ·). Then the algorithm implementation process is provided and its quick convergence is proved. Using software to establish static load coupling model of multi-nodes,simulate and verify the validity of FMFAC algorithm,which is applied to wind turbines blade full-scale static testing. The results show that the adaptive decoupling ability of FMFAC is better. The traction of four load points can stay steady and change coordinately. Process error is not over ± 6 k N. The error rate is lower than 1% in special phase.This algorithm effectively eliminates the traction coupling of the static testing process,and makes wind turbine blade testing steadily.
文摘Novel neuro-fuzzy techniques are used to dynamically control parameter settings ofgenetic algorithms (GAs).The benchmark routine is an adaptive genetic algorithm (AGA) that uses afuzzy knowledge-based system to control GA parameters.The self-learning ability of the cerebellar modelariculation controller (CMAC) neural network makes it possible for on-line learning the knowledge onGAs throughout the run.Automatically designing and tuning the fuzzy knowledge-base system,neuro-fuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learningmethod.The Results from initial experiments show a Dynamic Parametric AGA system designed by theproposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a widerange of combinatorial optimization.
基金supported by the Science and Technology Project of the Headquarters of the State Grid Corporation(project code:5400-202323233A-1-1-ZN).
文摘With the increasing integration of emerging source-load types such as distributed photovoltaics,electric vehicles,and energy storage into distribution networks,the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source,multi-load systems.This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches-primarily focused on economic objectives-insufficient to meet the growing demands for flexible scheduling and dynamic response.To address these challenges,this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational requirements of distribution networks with a high penetration of new-type source-loads.The goal is to establish a comprehensive energy management framework that optimally balances energy efficiency,carbon reduction,and economic performance in modern distribution networks.To enhance classification accuracy,the strategy constructs amulti-dimensional scenario classification model that integrates environmental and climatic factors by analyzing the operational characteristics of new-type distribution networks and incorporating expert knowledge.An improved split-coupling K-means preclustering algorithm is employed to classify distribution networks effectively.Based on the classification results,fuzzy logic control is then utilized to dynamically optimize the weighting of each objective,allowing for an adaptive adjustment of priorities to achieve a flexible and responsivemulti-objective energy management strategy.The effectiveness of the proposed approach is validated through practical case studies.Simulation results indicate that the proposed method improves classification accuracy by 18.18%compared to traditional classification methods and enhances energy savings and carbon reduction by 4.34%and 20.94%,respectively,compared to the fixed-weight strategy.
文摘This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.
基金the National Natural Science Foundation of China (No.50579007)
文摘A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.
