Self-adaptive fuzzy controller with formulary rule for servo control of discharge gap in Micro EDM

In micro electrical discharge machining （micro EDM）, it is difficult for servo controlling the narrow discharge gap with the characters of non-linear and quick change. In this paper, aiming at solving the problems above, a self-adaptive fuzzy controller with formulary rule （SAFCFR） is presented based on the dual feedbacks composed by gap electric signal and discharge-ratio statistics. To ensure the properties of self-optimizing and fast stabilization, the formulary rule was designed with a tuning factor. In addition, the fast-convergence algorithms were introduced to adjust control target center and output scale factor. In this way, the normal discharge ratio can tend to the highest value during micro-EDM process. Experimental results show that the proposed algorithms are effective in improving the servo-control performance. According to the drilling-micro-EDM experiments, the machining efficiency is improved by 20% through applying SAFCFR. Moreover, SAFCFR is a prompt way to optimize parameters of discharge-gap servo control.

Simulation and analysis of a Truck Model’s ride comfort based on fuzzy adaptive control theory

This paper tried to analyse and verify the fuzzy adaptive control strategy of electronic control air suspension system for heavy truck. Created the seven-freedoms vehicle suspension model, and the road input model; with Matlab/Simulink toolboxes and modules, built dynamical system simulation model for heavy truck with air suspension, fuzzy adaptive control model, height control model for air spring, and intelligent control and analyse on root mean square value of acceleration of gravity center of the vehicle under excitation of road. Results show that the fuzzy control had less help to the body vibration on the better pavement, but had the better benefit on the bad road, and the vehicle’s root mean square value of acceleration of gravity center is less than passive suspension’s obviously.

Research on fuzzy self-adaptive PI-Smith control in long time-delay system

The long time-delay often exists in industrial process. In order to overcome the big overshoot and long regulating time of the long time-delay system control, a new fuzzy self-adaptive PI-Smith control method is proposed. This method combines the Smith predictive control with fuzzy self-adaptive proportional-integral （PI） control. The traditional proportional-integral-derivative （PID） controller in Smith predictive control is replaced by fuzzy PI controller which utilizes the principle of fuzzy control to tune parameters of PI controller on-line. The results of simulation for electric furnace show that the method has the advantages of shortening regulating time, no overshoot, no steady-state error, excellent control accuracy, and good adaptive ability to the change of system model.

Fuzzy Self-Adaptation of Mission-Critical Software Under Uncertainty

Mission-critical software （MCS） must provide continuous, online services to ensure the successful accomplish- ment of critical missions. Self-adaptation is particularly desirable for assuring the quality of service （QoS） and availability of MCS under uncertainty. Few techniques have insofar addressed the issue of MCS self-adaptation, and most existing approaches to software self-adaptation fail to take into account uncertainty in the self-adaptation loop. To tackle this problem, we propose a fuzzy control based approach, i.e., Software Fuzzy Self-Adaptation （SFSA）, with a view to deal with the challenge of MCS self-adaptation under uncertainty. First, we present the SFSA conceptual framework, consisting of sensing, deciding and acting stages, and establish the formal model of SFSA to lay a rigorous and mathematical foundation of our approach. Second, we develop a novel SFSA implementation technology as well as its supporting tool, i.e., the SFSA toolkit, to automate the realization process of SFSA. Finally, we demonstrate the effectiveness of our approach through the development of an adaptive MCS application in process control systems. Validation experiments show that the fuzzy control based approach proposed in this work is effective and with low overheads.