1,721,221 research outputs found
A data-driven approach to mixed-sensitivity control with application to an active suspension system
No full textIn this paper, a data-driven approach is proposed to tune fixed-order controllers for unknown stable LTI plants in a mixed-sensitivity loop-shaping framework. The method requires a single set of input-output samples and it is based on convex optimization techniques; moreover, it asymptotically guarantees the internal stability of the closed-loop system. The effectiveness of the method is illustrated with application to the control of an active suspension system
Toward eXplainabile Data-Driven Control (XDDC): The Property-Preserving Framework
No full textAs Artificial Intelligence (AI) techniques continue to advance, the need for explainability becomes increasingly crucial, especially in sensitive or safety-critical domains. eXplainable AI (XAI) has emerged to address this need, aiming to enhance transparency in complex models. While XAI has gained traction in mainstream machine learning, its application in data-driven control systems remains relatively unexplored. This letter introduces a novel concept of explainability tailored for data-driven control, allowing one to design feedback loops from data incorporating prior knowledge and preserving important system properties. Through two case studies, we demonstrate the efficacy of this property-preserving framework in direct and indirect data-driven control system design. This letter lays the foundation for further research at the intersection of AI and data-driven control, offering insights into enhancing transparency in complex control systems
An insight into noise covariance estimation for Kalman Filter design
No full textIn Kalman filtering applications, the variance of the estimation error is guaranteed to
be minimized only if a complete description of the plant dynamics is available. While nowadays
there are several established methods to find an accurate model of a physical system, the
evaluation of the covariance matrices of the disturbances is still a hard task. Therefore, such
matrices are usually parameterized as diagonal matrices and their entries are estimated from a
preliminary set of measurements. In this paper, we analyze the properties of the Kalman filter for
linear time-invariant systems and we show that, for some classes of systems, the number of design
parameters can be significantly reduced. Moreover, we prove that, under some assumptions, the
covariance of the process noise can be parameterized as a full matrix without increasing the
complexity of the tuning procedure. The above theoretical results are tested on two numerical
examples
A Data-Driven Approach to Mixed-Sensitivity Control With Application to an Active Suspension System
Full text linkIn this paper, a data-driven approach is proposed to tune fixed-order controllers for unknown stable LTI plants in a mixed-sensitivity loop-shaping framework. The method requires a single set of input-output samples and it is based on convex optimization techniques; moreover, it asymptotically guarantees the internal stability of the closed-loop system. The effectiveness of the method is illustrated with application to the control of an active suspension system.L
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