Tracking Control of Overhead Crane Using Output Feedback With Adaptive Unscented Kalman Filter and Condition-Based Selective Scaling

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 load sway and its velocity. The flatness property of crane-like systems can be exploited to solve both the motion planning and the tracking problems so that the load (whose coordinates are included in the set of the flat outputs) exponentially follows a rapid reference trajectory. However, unmodelled 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.

Frequency-Modulation Input-Shaping Strategy for Double-Pendulum Overhead Cranes Undergoing Simultaneous Hoist and Travel Maneuvers

“…Others used the concept of equivalent frequency and the equivalent damping ratio to take into account the variable hoist length for optimal path planning [40]. Most recently, an adaptive Kalman filter has been used to account for unmodeled disturbances such as friction in a model-based feedback crane control system [41]. Arabasi and Masoud [42] presented a frequencymodulation input-shaping control scheme that allows for simultaneous hoist and travel maneuvers of simple-pendulum overhead cranes.…”

Control of Flexible Robot by Harmonic Functions

“…An approach with optimal control is used in [7]. The adaptive approach used for tracking control is presented in [8]. Partly stable controllers derived using the dynamic model of the manipulator create basis for the optimal switching sequence control in [9].…”