Creep is a critical specification of load cell. Based on the analysis of creep, a new compensation technique, fuzzy creep compensation, is presented in this paper. It firstly introduces the fuzzy recognition to determ...Creep is a critical specification of load cell. Based on the analysis of creep, a new compensation technique, fuzzy creep compensation, is presented in this paper. It firstly introduces the fuzzy recognition to determine loading situations. Compared to the other compensation methods, fuzzy creep compensation can avoid the complicated modeling of creep performance, and it is also proved to be an efficient and simple approach to improve the accuracy of load cell by experiments.展开更多
A novel fuzzy logic compensating (FLC) scheme is proposed to enhance theconventional computed-torque control (CTC) structure of manipulators The control scheme is based onthe combination of a classical CTC and FLC, an...A novel fuzzy logic compensating (FLC) scheme is proposed to enhance theconventional computed-torque control (CTC) structure of manipulators The control scheme is based onthe combination of a classical CTC and FLC, and the resulting control scheme has a simple structurewith improved robustness. Further improvement of the performance of the FLC scheme is achievedthrough automatic tuning of a weight parameter a leading to a self-tuning fuzzy logic compensator,so the system uncertainty can be compensated very well. By taking into account the full nonlinearnature of the robotic dynamics, the overall closed-loop system is shown to be asymptotically stable.Experimental results demonstrate the effectiveness of the computed torque and fuzzy compensationscheme to control a manipulator during a trajectory tracking task.展开更多
A new approach is provided to estimate the state of arbitrarily maneuvering target. In this approach a fuzzy compensator is used to tackle the uncertainty which results from the targets arbitrarily maneuvering. To des...A new approach is provided to estimate the state of arbitrarily maneuvering target. In this approach a fuzzy compensator is used to tackle the uncertainty which results from the targets arbitrarily maneuvering. To design the observer of the nonlinear system, the fuzzy T S model and the receding horizon control strategy are employed. Besides, the design depends on tracking the output error of the model. Compared with the technique used in other articles, the errors between the first estimated value and the true state value of the estimated variable are not strictly required. Numerical simulating results show that the proposed approach can estimate the states of the random maneuvering targets on line so as to obtain the exact tracking of the target.展开更多
The paper presents a robust parallel distributed compensation （PDC） fuzzy controller for a nonlinear and certain system in continuous time described by the Tal^gi-Sugeno （T-S） fuzzy model. This controller is based...The paper presents a robust parallel distributed compensation （PDC） fuzzy controller for a nonlinear and certain system in continuous time described by the Tal^gi-Sugeno （T-S） fuzzy model. This controller is based on a new type of time-varying fuzzy sets （TVFS）. These fuzzy sets are characterized by displacement of the kernels to the right or left of the universe of discourse, and they are directed by a well-defined criterion. In this work, we only focused on the movement of midpoint of the universe. The movements of this midpoint are optimized by particle swarm optimization （PSO） approach.展开更多
文摘Creep is a critical specification of load cell. Based on the analysis of creep, a new compensation technique, fuzzy creep compensation, is presented in this paper. It firstly introduces the fuzzy recognition to determine loading situations. Compared to the other compensation methods, fuzzy creep compensation can avoid the complicated modeling of creep performance, and it is also proved to be an efficient and simple approach to improve the accuracy of load cell by experiments.
文摘A novel fuzzy logic compensating (FLC) scheme is proposed to enhance theconventional computed-torque control (CTC) structure of manipulators The control scheme is based onthe combination of a classical CTC and FLC, and the resulting control scheme has a simple structurewith improved robustness. Further improvement of the performance of the FLC scheme is achievedthrough automatic tuning of a weight parameter a leading to a self-tuning fuzzy logic compensator,so the system uncertainty can be compensated very well. By taking into account the full nonlinearnature of the robotic dynamics, the overall closed-loop system is shown to be asymptotically stable.Experimental results demonstrate the effectiveness of the computed torque and fuzzy compensationscheme to control a manipulator during a trajectory tracking task.
文摘A new approach is provided to estimate the state of arbitrarily maneuvering target. In this approach a fuzzy compensator is used to tackle the uncertainty which results from the targets arbitrarily maneuvering. To design the observer of the nonlinear system, the fuzzy T S model and the receding horizon control strategy are employed. Besides, the design depends on tracking the output error of the model. Compared with the technique used in other articles, the errors between the first estimated value and the true state value of the estimated variable are not strictly required. Numerical simulating results show that the proposed approach can estimate the states of the random maneuvering targets on line so as to obtain the exact tracking of the target.
文摘The paper presents a robust parallel distributed compensation （PDC） fuzzy controller for a nonlinear and certain system in continuous time described by the Tal^gi-Sugeno （T-S） fuzzy model. This controller is based on a new type of time-varying fuzzy sets （TVFS）. These fuzzy sets are characterized by displacement of the kernels to the right or left of the universe of discourse, and they are directed by a well-defined criterion. In this work, we only focused on the movement of midpoint of the universe. The movements of this midpoint are optimized by particle swarm optimization （PSO） approach.
