Automatic localization,aligning the measured points with the design model,is a basic task in free-form surface inspection.The main difficulty of current localization algorithms is how to define effective distance func...Automatic localization,aligning the measured points with the design model,is a basic task in free-form surface inspection.The main difficulty of current localization algorithms is how to define effective distance function and localization reliability index.This paper proposes a new method of calculating motion parameters and evaluating localization reliability.First,improved modified coefficient is defined and applied to weighted-iteration distance function,which better approximates the point-to-surface closest distance.It can control the contribution ratios of different measured points by considering the curvature feature and iterative residual.Second,the mapping relationship between localization error and geometric error is analyzed,from which a Lyapunov-test statistic is derived to define a frame-independence index.Then,the determination of localization reliability changes into a supposition examination problem.This can avoid rejecting correct motion parameters,which exists in the traditional judgment of absolute root-mean-square distance.In addition,two test experiments are implemented to demonstrate the proposed localization algorithm.展开更多
To determine whether a given deterministic nonlinear dynamic system is chaotic or periodic, a novel test approach named zero-one （0-1） test has been proposed recently. In this approach, the regular and chaotic motio...To determine whether a given deterministic nonlinear dynamic system is chaotic or periodic, a novel test approach named zero-one （0-1） test has been proposed recently. In this approach, the regular and chaotic motions can be decided by calculating the parameter K approaching asymptotically to zero or one. In this study, we focus on the 0-1 test algorithm and illustrate the selection of parameters of this algorithm by numerical experiments. To validate the reliability and the universality of this algorithm, it is applied to typical nonlinear dynamic systems, including fractional-order dynamic system.展开更多
The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base o...The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators. The rule base consists of only four rules per each de- gree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a lin- guistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the sys- tem nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode con- troller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 50835004 and 51105155)the China Postdoctoral Science Foundation (Grant No. 20110491145)
文摘Automatic localization,aligning the measured points with the design model,is a basic task in free-form surface inspection.The main difficulty of current localization algorithms is how to define effective distance function and localization reliability index.This paper proposes a new method of calculating motion parameters and evaluating localization reliability.First,improved modified coefficient is defined and applied to weighted-iteration distance function,which better approximates the point-to-surface closest distance.It can control the contribution ratios of different measured points by considering the curvature feature and iterative residual.Second,the mapping relationship between localization error and geometric error is analyzed,from which a Lyapunov-test statistic is derived to define a frame-independence index.Then,the determination of localization reliability changes into a supposition examination problem.This can avoid rejecting correct motion parameters,which exists in the traditional judgment of absolute root-mean-square distance.In addition,two test experiments are implemented to demonstrate the proposed localization algorithm.
基金Project supported by the National Natural Science Foundation of of China (Grant No. 60672041)
文摘To determine whether a given deterministic nonlinear dynamic system is chaotic or periodic, a novel test approach named zero-one （0-1） test has been proposed recently. In this approach, the regular and chaotic motions can be decided by calculating the parameter K approaching asymptotically to zero or one. In this study, we focus on the 0-1 test algorithm and illustrate the selection of parameters of this algorithm by numerical experiments. To validate the reliability and the universality of this algorithm, it is applied to typical nonlinear dynamic systems, including fractional-order dynamic system.
文摘The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators. The rule base consists of only four rules per each de- gree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a lin- guistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the sys- tem nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode con- troller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.
