Consistency retention method for CNC machine tool digital twin model

Computer Numerical Control Machine Tool (CNCMT) Digital Twin (DT) model is a carrier for complex, time-varying, coupled data of CNCMT, which can theoretically provide a time-varying high-fidelity model. However, there are still many difficulties in its implementation process. And the key issue is how to realize the updated DT model with performance attenuation and validate it. In order to solve this problem, a model consistency retention method for CNCMT DT model is studied and proposed in this paper. Firstly, the framework of consistency retention method for DT model is designed including both digital space and physical space. The principles of data management and performance attenuation update in digital space are elaborated. Then, the implementation method for consistency retention of CNCMT DT model is studied in terms of performance attenuation update workflow for wear and other damage separately. Finally, a case study for the establishment and application of high-fidelity test bench DT model that focusing on rolling guide-rail is carried out to show the implementation flow of the proposed method and verify its operability and effectiveness.     

Safe and intuitive manual guidance of a robot manipulator using adaptive admittance control towards robot agility

Key areas of robot agility include methods that increase capability and flexibility of industrial robots and facilitate robot re-tasking. Manual guidance can achieve robot agility effectively, provided that a safe and smooth interaction is guaranteed when the user exerts an external force on the end effector. We approach this by designing an adaptive admittance law that can adjust its parameters to modify the robot compliance in critical areas of the workspace, such as near and on configuration singularities, joint limits, and workspace limits, for a smooth and safe operation. Experimental validation was done with two tests: a constraint activation test and a 3D shape tracing task. In the first one, we validate the proper response to constraints and in the second one, we compare the proposed approach with different admittance parameter tuning strategies using a drawing task where the user is asked to guide the robot to trace a 3D profile with an accuracy or speed directive and evaluate performance considering path length error and execution time as metrics, and a questionnaire for user perception. Results show that appropriate response to individual and simultaneous activation of the aforementioned constraints for a safe and intuitive manual guidance interaction is achieved and that the proposed parameter tuning strategy has better performance in terms of accuracy, execution time, and subjective evaluation of users.     

引入关联分析的用户行为信任评估在云计算平台中的研究

关联规则是数据挖掘的重要方法。随着国内外云计算应用及研究的不断推进,云计算的安全性成为云计算的核心问题之一。通过进行用户的状态行为关联分析,提取信任信息,结合可信云的思想,提出一个云计算下基于信任的防御系统模型。     

Adaptive fuzzy dynamic surface control for a class of perturbed nonlinear time-varying delay systems with unknown dead-zone

In this paper,adaptive dynamic surface control(DSC) is developed for a class of nonlinear systems with unknown discrete and distributed time-varying delays and unknown dead-zone.Fuzzy logic systems are used to approximate the unknown nonlinear functions.Then,by combining the backstepping technique and the appropriate Lyapunov-Krasovskii functionals with the dynamic surface control approach,the adaptive fuzzy tracking controller is designed.Our development is able to eliminate the problem of "explosion of complexity" inherent in the existing backstepping-based methods.The main advantages of our approach include:1) for the n-th-order nonlinear systems,only one parameter needs to be adjusted online in the controller design procedure,which reduces the computation burden greatly.Moreover,the input of the dead-zone with only one adjusted parameter is much simpler than the ones in the existing results;2) the proposed control scheme does not need to know the time delays and their upper bounds.It is proven that the proposed design method is able to guarantee that all the signals in the closed-loop system are bounded and the tracking error is smaller than a prescribed error bound,Finally,simulation results demonstrate the effectiveness of the proposed approach.     

On‐off iterative adaptive controller for low‐power micro‐robotic step regulation

A novel model‐free iterative adaptive controller is presented for low‐power control of piezoelectric actuators. The controller uses simple adaptation rules based on known general behavior of piezoelectric actuators to adjust on‐off switching times to drive piezoelectric actuators through a desired transient step motion. Adaptation rules are based on small numbers of measurements taken during each iteration of the actuator movement. Combined with the use of only on‐off control inputs, controller implementation can be possible at much lower overall power levels than would be needed to implement a conventional control strategy such as through pulse‐width‐modulation (PWM) with real‐time feedback. Such power savings are particularly important for the intended controller application to piezoelectric microactuators driving autonomous terrestrial micro‐robots. A method for predicting convergence of systems with nominally linear dynamics and unknown, bounded nonlinearities is described, and applied to a sample target piezoelectric actuator. The controller is tested in simulation and experimentally on a piezoelectric cantilever actuator, and shows predicted convergence to the desired response. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Direct adaptive fuzzy control with membership function tuning

In this paper, a direct self‐structured adaptive fuzzy control is introduced for the class of nonlinear systems with unknown dynamic models. Control is accomplished by an adaptive fuzzy system with a fixed number of rules and adaptive membership functions. The reference signal and state errors are used to tune the membership functions and update them instantaneously. The Lyapunov synthesis method is also used to guarantee the stability of the closed loop system. The proposed control scheme is applied to an inverted pendulum and a magnetic levitation system, and its effectiveness is shown via simulation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

Modal logic as metalogic

The goal of this paper is to show how modal logic may be conceived as recording the derived rules of a logical system in the system itself. This conception of modal logic was propounded by Dana Scott in the early seventies. Here, similar ideas are pursued in a context less classical than Scott's.First a family of propositional logical systems is considered, which is obtained by gradually adding structural rules to a variant of the nonassociative Lambek calculus. In this family one finds systems that correspond to the associative Lambek calculus, linear logic, relevant logics, BCK logic and intuitionistic logic. Above these basic systems, sequent systems parallel to the basic systems are constructed, which formalize various notions of derived rules for the basic systems. The deduction theorem is provable for the basic systems if, and only if, they are at least as strong as systems corresponding to linear logic, or BCK logic, depending on the language, and their deductive metalogic is not stronger than they are.However, though we do not always have the deduction theorem, we may always obtain a modal analogue of the deduction theorem for conservative modal extensions of the basic systems. Modal postulates which are necessary and sufficient for that are postulates of S4 plus modal postulates which mimic structural rules. For example, the modal postulates which Girard has recently considered in linear logic are necessary and sufficient for the modal analogue of the deduction theorem.All this may lead towards results about functional completeness in categories. When functional completeness, which is analogous to the deduction theorem, fails, we may perhaps envisage a modal analogue of functional completeness in a modal category, of which our original category is a full subcategory.     

Supporting inconsistent rules in database systems

When a set of rules generates (conflicting) values for a virtual attribute of some tuple, the system must resolve the inconsistency and decide on a unique value that is assigned to that attribute. In most current systems, the conflict is resolved based on criteria that choose one of the rules in the conflicting set and use the value that it generated. There are several applications, however, where inconsistencies of the above form arise, whose semantics demand a different form of resolution. We propose a general framework for the study of the conflict resolution problem, and suggest a variety of resolution criteria, which collectively subsume all previously known solutions. With several new criteria being introduced, the semantics of several applications are captured more accurately than in the past. We discuss how conflict resolution criteria can be specified at the schema or the rule-module level. Finally, we suggest some implementation techniques based on rule indexing, which allow conflicts to be resolved efficiently at compile time, so that at run time only a single rule is processed.An earlier version of this work appeared under the title Conflict Resolution of Rules Assigning Values to Virtual Attributes inProceedings of the 1989 ACM-Sigmod Conference, Portland, OR, June 1989, pp. 205–214.Partially supported by the National Science Foundation under Grant IRI-9157368 (PYI Award) and by grants from DEC, HP, and AT&T.Partially supported by the National Science Foundation under Grant IRI-9057573 (PYI Award), IBM, DEC, and the University of Maryland Institute for Advanced Computer Studies (UMIACS).     

一种高效挖掘冠心病医案关联规则的仿真研究

研究中医冠心病医案,高效挖掘有益信息规则问题,由于中医医案数据量大、关联性强,针对传统的关联规则挖掘算法处理中医医案数据时存在效率低、收敛速度慢及漏报规则等问题,提出一种小生境技术和基因表达式编程相结合的挖掘关联规则的方法。通过惩罚函数设置支持度阈值,利用小生境技术执行小生境演化、融合算法,结合基因表达式编程算法操作简单、鲁棒性强的优势搜索强关联规则,有效避免了算法早熟,解决了规则冗余。针对治疗冠心病的中医医案进行了验证性实验,实验结果表明,改进算法在提取有效信息的效率上有较大的提高,挖掘结果对冠心病中医临床诊治具有一定的参考价值。