首页|Qassim University Researcher Highlights Recent Research in Machine Learning (Mac hine Learning Structure for Controlling the Speed of Variable Reluctance Motor v ia Transitioning Policy Iteration Algorithm)
Qassim University Researcher Highlights Recent Research in Machine Learning (Mac hine Learning Structure for Controlling the Speed of Variable Reluctance Motor v ia Transitioning Policy Iteration Algorithm)
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2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on artificial intelligence is now ava ilable. According to news reporting originating from Buraydah, Saudi Arabia, by NewsRx correspondents, research stated, "This paper investigated a new speed reg ulator using an adaptive transitioning policy iteration learning technique for t he variable reluctance motor (VRM) drive." Financial supporters for this research include The Qassim University. Our news editors obtained a quote from the research from Qassim University: "A t ransitioning strategy is used in this unique scheme to handle the nonlinear beha vior of the VRM by using a series of learning centers, each of which is an indiv idual local learning controller at linear operational location that grows throug hout the system's nonlinear domain. This improved control technique based on an adaptive dynamic programming algorithm is developed to derive the prime solution of the infinite horizon linear quadratic tracker (LQT) issue for an unidentifie d dynamical configuration with a VRM drive. By formulating a policy iteration al gorithm for VRM applications, the speed of the motor shows inside the machine mo del, and therefore the local centers are directly affected by the speed. Hence, when the speed of the rotor changes, the parameters of the local centers grid wo uld be updated and tuned."
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