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
A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibratio...A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares(FLS)-support vector machine(SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.展开更多
A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. T...A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control(IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.展开更多
A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, th...A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noige or step disturbance indicate that the T2 controller is better than the T1 controller by 0--18%, depending on the experiment condition and performance measure.展开更多
There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fi...There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.展开更多
Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined...Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.展开更多
In this paper, a direct adaptive fuzzy tracking control is proposed for a class of uncertain single-input single-output nonlinear semi-strict feedback systems. Based on Takagi-Sugeno type fuzzy systems, a direct adapt...In this paper, a direct adaptive fuzzy tracking control is proposed for a class of uncertain single-input single-output nonlinear semi-strict feedback systems. Based on Takagi-Sugeno type fuzzy systems, a direct adaptive fuzzy tracking controller is developed by using the backstepping approach. The main advantage of the developed method is that for an n-th order system, only one parameter is needed to be adjusted online. It is proven that, under the appropriate assumptions, the developed scheme can achieve that the output system converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. The efficacy of the proposed algorithm is investigated by an illustrative simulation example of one link robot.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Project(2012BAK09B02-05) supported by the National Key Technology R&D Program of China during the Twelfth Five-year PeriodProject(51274250) supported by the National Natural Science Foundation of China
文摘A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares(FLS)-support vector machine(SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.
文摘A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control(IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.
基金Project(51005253) supported by the National Natural Science Foundation of ChinaProject(2007AA04Z344) supported by the National High Technology Research and Development Program of China
文摘A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noige or step disturbance indicate that the T2 controller is better than the T1 controller by 0--18%, depending on the experiment condition and performance measure.
基金Project(60574030) supported by the National Natural Science Foundation of ChinaKey Project(60634020) supported by the National Natural Science Foundation of China
文摘There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.
基金Supported by the National Natural Science Foundation of China（20776042） the National High Technology Research and Development Program of China（2007AA04Z164）+3 种基金 the Doctoral Fund of Ministry of Education of China（20090074110005） the Program for New Century Excellent Talents in University（NCET-09-0346） the＂Shu Guang＂Project（095G29） Shanghai Leading Academic Discipline Project（B504）
文摘Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.
文摘In this paper, a direct adaptive fuzzy tracking control is proposed for a class of uncertain single-input single-output nonlinear semi-strict feedback systems. Based on Takagi-Sugeno type fuzzy systems, a direct adaptive fuzzy tracking controller is developed by using the backstepping approach. The main advantage of the developed method is that for an n-th order system, only one parameter is needed to be adjusted online. It is proven that, under the appropriate assumptions, the developed scheme can achieve that the output system converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded. The efficacy of the proposed algorithm is investigated by an illustrative simulation example of one link robot.
