Torque Ripple Minimization in a Switched Reluctance Drive by Neuro-Fuzzy Compensation

Simple power electronic drive circuit and fault tolerance of converter are specific advantages of SRM drives, but excessive torque ripple has limited its use to special applications. It is well known that controlling the current shape adequately can minimize the torque ripple. This paper presents a new method for shaping the motor currents to minimize the torque ripple, using a neuro-fuzzy compensator. In the proposed method, a compensating signal is added to the output of a PI controller, in a current-regulated speed control loop. Numerical results are presented in this paper, with an analysis of the effects of changing the form of the membership function of the neuro-fuzzy compensator.Comment: To be published in IEEE Trans. on Magnetics, 200

A real-time articulatory visual feedback approach with target presentation for second language pronunciation learning

International audienceArticulatory information can support learning or remediating pronunciation of a second language (L2). This paper describes an electromagnetic articulometer-based visual-feedback approach using an articulatory target presented in real-time to facilitate L2 pronunciation learning. This approach trains learners to adjust articulatory positions to match targets for a L2 vowel estimated from productions of vowels that overlap in both L1 and L2. Training of Japanese learners for the American English vowel /ae/ that included visual training improved its pronunciation regardless of whether audio training was also included. Articulatory visual feedback is shown to be an effective method for facilitating L2 pronunciation learning

Solving the Task Variant Allocation Problem in Distributed Robotics

We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system’s quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16, 31 and 56% respectively

Letters to the Editor________________________________________________________________ Fuzzy Logic Torque Ripple Reduction by Turn-Off Angle Compensation for Switched Reluctance Motors

Abstract—A fuzzy-logic-based turn-off angle compensator for torque ripple reduction in a switched reluctance motor is proposed. The turn-off angle, as a complex function of motor speed and current, is automatically changed for a wide motor speed range to reduce torque ripple. Experimental results are presented that show ripple reduction when the turn-off angle compensator is used. Index Terms—Compensation, control system synthesis, electric motors, fuzzy control, fuzzy logic, fuzzy systems, machine control, machine theory, nonlinear control systems, reluctance motor drives, reluctance motors, torque control. I