首页|Study Findings on Robotics Published by a Researcher at Taif University (Kriging -based Model Predictive Control for Lower-limb Rehabilitation Robots)
Study Findings on Robotics Published by a Researcher at Taif University (Kriging -based Model Predictive Control for Lower-limb Rehabilitation Robots)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on robotics is the subjec t of a new report. According to news reporting out of Taif University by NewsRx editors, research stated, “Model predictive control (MPC) has emerged as a predo minant method in the realm of control systems; yet, it faces distinct challenges .” The news journalists obtained a quote from the research from Taif University: “F irst, MPC often hinges on the availability of a precise and accurate system mode l, where even minor deviations can drastically affect the control performance. S econd, it entails a high computational load due to the need to solve complex opt imization problems in real time. This study introduces an innovative method that harnesses the probabilistic nature of Gaussian processes (GPs), offering a solu tion that is robust, adaptive, and computationally efficient for optimal control . Our methodology commences with the collection of data to learn optimal control policies. We then proceed with offline training of GPs on these data, which ena bles these processes to accurately grasp system dynamics, establish input-output relationships, and, crucially, identify uncertainties, thereby informing the MP C framework. Utilizing the mean and uncertainty estimates derived from GPs, we h ave crafted a controller that is capable of adapting to system deviations and ma intaining consistent performance, even in the face of unforeseen disturbances or model inaccuracies.”
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