H_∞ Output-Feedback Anti-Swing Control for a Nonlinear Overhead Crane System With Disturbances Based on T-S Fuzzy Model

Abstract

In this paper, the output-feedback controller for a nonlinear overhead crane system with external disturbances was developed. Firstly, the Takagi-Sugeno fuzzy model was used to represent the overhead crane system nonlinearity. A fuzzy-based state observer was then built to estimate the values of immeasurable variables. Secondly, a novel control design called virtual-desired variable synthesis was used to converting the tracking control into a stabilization problem. It was primarily used to define the internal desired states, making the design procedure clear and easy. The performance criterion was used to attenuate external disturbances, and the closed-loop model stability was investigated using the quadratic Lyapunov function. Finally, three simulations were conducted to verify the feasibility and effectiveness of the proposed method. The results have shown that there is practically no positioning error and residual payload swing. Thus, in theory, any type of bounded external disturbances can be eliminated using the proposed method. Additionally, the convergence time is half of its model predictive control method counterpart and one-third of the standard controller. Hence, it provides a reference for actual control of the overhead crane systems, mostly due to its good performance.INDEX TERMS Overhead crane, Output feedback, T-S fuzzy model, Virtual-desired variable synthesis.

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“…Under the circumstance where the current best predator E i (blue circle) is unexpectedly entrapped within the local optima amidst operationalization of Phase 2, employment of a conventional MPA approach would present an immerse possibility where E i retains its trapped position, whilst worsening the situation shall drag the prey P i (red circle) consequently towards the same location. Despite absence of graphical illustration for equation (5), similar circumstance would prevail for the equation following a lofty percentage where the current prey P i would be entrapped within the local optima region, whilst its continuous probe for alternative search track may be jeopardized. Represented by RA i (green circle) via Figure 1, the complication as encountered in both cases is, nonetheless, solvable through the employment of random average between both E i and P i within the updating mechanism.…”

“…Hopping on the academic bandwagon, a generous number of literature investigating the control precision of crane systems were published, which enclosed numerous control algorithms such as finite time sliding mode controller for fuzzy control, 4 H-infinity output feedback based on fuzzy model, 5 precision-positioning adaptive controller, 6 nonlinear sliding mode controls, 7 backstepping controller, 8 time-varying sliding mode control, 9 dual sliding mode control, 10 and LMI fuzzy control. 11 Upon recognizing the sole proficiency of aforementioned control schemes towards the handling of a single-input-multi-output (SIMO) crane systems by regulation of a single input (i.e., force, voltage), they have fundamentally neglected the influence of extreme coupling across disparate input channels within a real-time crane system which substantially hinders the performance robustness of implemented controller.…”

A Novel Separation Principle-Based Stabilization for 6-Dof Overhead Crane Under Fault Injection Data Onslaught

“…The controller utilized all the sway-related state variables which were approximated by an Unscented Kalman Filter. Another improved algorithm compare to that proposed in [36] for 2D overhead crane is the H ∞ control algorithm developed in [37] by Chengcheng Li et al Without linearization 2D overhead crane's model, a H ∞ output feedback controller, which was designed by the connection between a virtual-desired variable synthesis controller and a nonlinear Luenberger observer, demonstrated its effectiveness in each simulation scenario with the influence of external disturbances. In addition, this controller in [37] also integrated the Takagi-Sugeno fuzzy rules to represent the overhead crane system nonlinearities to enhance the closed-loop's adaption.…”

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