An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and s...An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.展开更多
This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments wit... This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.展开更多
Purpose–The purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system(EHSS).Specifically,the adaptive neuro-fuzzy inference system(ANFIS)is sele...Purpose–The purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system(EHSS).Specifically,the adaptive neuro-fuzzy inference system(ANFIS)is selected to improve the control performance for EHSS.Design/methodology/approach–Two types of input–output data were chosen to train the ANFIS models.The inputs are the desired and actual forces,and the output is the current.The first type is to set a sinusoidal signal for the current to produce the actual driving force,and the desired force is chosen as same as the actual force.The other type is to give a sinusoidal signal for the desired force.Under the action of the PI controller,the actual force tracks the desired force,and the current is the output of the PI controller.Findings–The models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller.The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot,rise time and mean absolute error and show adaptivity to different tracking conditions,including sinusoidal signal tracking and sudden change signal tracking.Originality/value–This paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.展开更多
Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic genera...Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic generation control(AGC). To alleviate the system oscillation resulting from such load changes, implementation of flexible AC transmission systems(FACTSs) can be considered as one of the practical and effective solutions. In this paper, a thyristor-controlled series compensator(TCSC), which is one series type of the FACTS family, is used to augment the overall dynamic performance of a multi-area multi-source interconnected power system. To this end, we have used a hierarchical adaptive neuro-fuzzy inference system controller-TCSC(HANFISC-TCSC) to abate the two important issues in multi-area interconnected power systems, i.e., low-frequency oscillations and tie-line power exchange deviations. For this purpose, a multi-objective optimization technique is inevitable. Multi-objective particle swarm optimization(MOPSO) has been chosen for this optimization problem, owing to its high performance in untangling non-linear objectives. The efficiency of the suggested HANFISC-TCSC has been precisely evaluated and compared with that of the conventional MOPSO-TCSC in two different multi-area interconnected power systems, i.e., two-area hydro-thermal-diesel and three-area hydro-thermal power systems. The simulation results obtained from both power systems have transparently certified the high performance of HANFISC-TCSC compared to the conventional MOPSO-TCSC.展开更多
文摘An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.
文摘 This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.
基金This work was supported by the National Key R&D Program of China“The study on Load-bearing and Moving Support Exoskeleton Robot Key Technology and Typical Application”(2017YFB1300502)This work is also supported by the National Natural Science Foundation of China“Research on gait detection and recognition technology of Parkinson’s disease based on all-fiber composite sensors”under Grant 61903280Hubei Key Laboratory of Digital Textile Equipment Open fund“Research on intelligent monitoring clothing based on micro-nano fiber composite sensor”under Grant DTL2019011.
文摘Purpose–The purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system(EHSS).Specifically,the adaptive neuro-fuzzy inference system(ANFIS)is selected to improve the control performance for EHSS.Design/methodology/approach–Two types of input–output data were chosen to train the ANFIS models.The inputs are the desired and actual forces,and the output is the current.The first type is to set a sinusoidal signal for the current to produce the actual driving force,and the desired force is chosen as same as the actual force.The other type is to give a sinusoidal signal for the desired force.Under the action of the PI controller,the actual force tracks the desired force,and the current is the output of the PI controller.Findings–The models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller.The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot,rise time and mean absolute error and show adaptivity to different tracking conditions,including sinusoidal signal tracking and sudden change signal tracking.Originality/value–This paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.
文摘Suppression of the dynamic oscillations of tie-line power exchanges and frequency in the affected interconnected power systems due to loading-condition changes has been assigned as a prominent duty of automatic generation control(AGC). To alleviate the system oscillation resulting from such load changes, implementation of flexible AC transmission systems(FACTSs) can be considered as one of the practical and effective solutions. In this paper, a thyristor-controlled series compensator(TCSC), which is one series type of the FACTS family, is used to augment the overall dynamic performance of a multi-area multi-source interconnected power system. To this end, we have used a hierarchical adaptive neuro-fuzzy inference system controller-TCSC(HANFISC-TCSC) to abate the two important issues in multi-area interconnected power systems, i.e., low-frequency oscillations and tie-line power exchange deviations. For this purpose, a multi-objective optimization technique is inevitable. Multi-objective particle swarm optimization(MOPSO) has been chosen for this optimization problem, owing to its high performance in untangling non-linear objectives. The efficiency of the suggested HANFISC-TCSC has been precisely evaluated and compared with that of the conventional MOPSO-TCSC in two different multi-area interconnected power systems, i.e., two-area hydro-thermal-diesel and three-area hydro-thermal power systems. The simulation results obtained from both power systems have transparently certified the high performance of HANFISC-TCSC compared to the conventional MOPSO-TCSC.
