An Adaptive Unscented Kalman Filter With Selective Scaling (AUKF-SS) for Overhead Cranes

Kim, Jae-Hoon; Lee, Dongik; Kiss, Bálint; Kim, Donggil

doi:10.1109/tie.2020.2996150

Cited by 40 publications

(9 citation statements)

References 38 publications

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“…where the above noted subdeterminants M ij for i = 1, 2, 3, 4 and j = 1, 2, 3, 4 are defined as For the dynamic model of the robotic crane that was initially written in the form M(q)q + C(q, q)q + G(q) + d = T. ( 7 ) It holds that…”

Section: State-space Model Of the Tower Cranementioning

confidence: 99%

“…Tower cranes can accomplish accurate transport and positioning of heavy items in construction sites, in multi-storey building erection,in loading and unloading of cargos in ports and in storage tasks for the logistics industry [4][5][6]. Tower cranes exhibit the advantages of simple structure, convenient installation, low cost, high payload capacity and low energy consumption [7][8][9]. Tower cranes usually consist of an actuated jib arm, an actuated translational trolley and an unactuated payload below the trolley [3,10,11].…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear optimal control for the 4-DOF underactuated robotic tower crane

Rigatos

Abbaszadeh

Pomares

2022

Auton. Intell. Syst.

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Tower cranes find wide use in construction works, in ports and in several loading and unloading procedures met in industry. A nonlinear optimal control approach is proposed for the dynamic model of the 4-DOF underactuated tower crane. The dynamic model of the robotic crane undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system a stabilizing optimal (H-infinity) feedback controller is designed. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed control approach is advantageous because: (i) unlike the popular computed torque method for robotic manipulators, the new control approach is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs, (ii) it achieves fast and accurate tracking of reference setpoints under minimal energy consumption by the robot’s actuators, (iii) unlike the popular Nonlinear Model Predictive Control method, the article’s nonlinear optimal control scheme is of proven global stability and convergence to the optimum.

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Section: State-space Model Of the Tower Cranementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear optimal control for the 4-DOF underactuated robotic tower crane

Rigatos

Abbaszadeh

Pomares

2022

Auton. Intell. Syst.

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show abstract

“…Note that the (selective) scaling of Q tends to increase a posteriori error covariance matrix P (k) [45]. This is material to a quick convergence property of the filter, but at the same time, it implies that the filter is in a transient phase since the estimates may not be sufficiently accurate in the mean squared error sense.…”

Section: ) Condition-based Selective Scaling Lawmentioning

confidence: 99%

“…From a control point of view, the performance of AUKF-CSS will be compared with that of several filters: the conventional UKF and the AUKF-SS [45] of applying a selective scaling technique unconditionally. Configuration variables and their initial values of the UKFs considered are identical except for additional variables in adaptation phase.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…The adaptation technique presented in this paper is technically a mix of estimation and selective-scaling techniques for the process noise covariance matrix Q, and its concept is primally suggested in [45]. Indeed, the estimation of Q is necessary in systems with unknown input disturbances, because the statistical property (covariance) of process noise is usually changing over time.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Freight train speed active disturbance rejection tracking and wheel anti-slip based on fuzzy neural network with DBO optimization

Yi,

Jiang,

et al. 2023

Electr Eng

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An Adaptive Unscented Kalman Filter With Selective Scaling (AUKF-SS) for Overhead Cranes

Cited by 40 publications

References 38 publications

Nonlinear optimal control for the 4-DOF underactuated robotic tower crane

Nonlinear optimal control for the 4-DOF underactuated robotic tower crane

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

Freight train speed active disturbance rejection tracking and wheel anti-slip based on fuzzy neural network with DBO optimization

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