Abstract
Most of the advanced nonlinear control strategies reported in the literature for underactuated mechanisms, such as overhead cranes, require the knowledge of all state variables. For cranes, the state vector includes variables related to the load sway and its velocity. The flatness property of crane-like systems can be exploited to solve both motion planning and tracking problems, so that the load (whose coordinates are included in the set of the flat outputs) exponentially follows a rapid reference trajectory. However, unmodeled friction phenomena and limitations on the direct measurement of sway-related state variables usually impede the practical implementation of flatness-based control laws. This paper proposes the use of an adaptive unscented Kalman filter to estimate friction forces and unmeasured state variables. The convergence of the filter is improved using a novel technique, called condition-based selective scaling. The performance of the suggested scheme is verified through a set of computer simulations on a 2D overhead crane system.
Highlights
Automatic control of cranes generally aims to quickly transport heavy loads from one place to another with the least sways possible and accurately position them at a target point to improve productivity and safety
We propose an adaptive unscented Kalman filter with a condition-based selective scaling (AUKF-CSS) technique that can handle nonlinear equations and adapt to the process uncertainty worsened by unmodeled disturbance inputs
In this paper, an observer-based motion control scheme is proposed for an underactuated overhead crane system to achieve anti-sway and precise positioning of the load
Summary
Automatic control of cranes generally aims to quickly transport heavy loads from one place to another with the least sways possible and accurately position them at a target point to improve productivity and safety. Both design and tracking of aggressive load trajectories require knowledge of the nonlinear model of the crane For both tracking and sway elimination, knowledge (measurement or estimation) of the load position, sway angles, and frictional forces is necessary. When inaccurate transient estimates are VOLUME 9, 2021 used for the feedback, the control mechanism results in an unexpected state transition from the filter viewpoint, which leads to excessive scaling of Q and prevents the filter from reaching a steady state This problem can be resolved by the conditional usage of the selective scaling technique. The proposed filtering technique is highlighted by the fact that the state variables and unknown input disturbances can be simultaneously estimated, considering the nonlinear model and a conditional selective scaling-based adaptive estimation technique.
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