The switching algorithm for the control of overhead crane

Chang, Cheng-Yuan

doi:10.1007/s00521-006-0036-z

Cited by 20 publications

(12 citation statements)

References 21 publications

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“…Assuming the desired poles intervals (9) for each operating point (l 1 , m 1 ), (l 1 , m r ), (l n , m 1 ) and (l n , m r ), the vectors of controller parameters are derived from (12), and RB is formulated as:…”

Section: 2mentioning

confidence: 99%

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Interval arithmetic-based fuzzy discrete-time crane control scheme design

Smoczek

2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. In many manufacturing segments, container terminals and shipping yards the automation of material handling systems is an important element of enhancing productivity, safety and efficiency. The fast, precise and safe transfer of goods in crane operations requires a control application solving the problems, including non-collision trajectory planning and limitation of payload oscillations. The paper presents the interval arithmetic-based method of designing a discrete-time closed-loop anti-sway crane control system based on the fuzzy interpolation of linear controller parameters. The interval analysis of a closed-loop control system characteristic polynomial coefficients deviation from their nominal values is proposed to define a minimum number of fuzzy sets on the scheduling variables universe of discourse and to determine the distribution of triangular-shaped membership functions parameters, which satisfy the acceptable range of performances deterioration in the presence of the system's parameters variation. The effectiveness of this method was proved in experiments conducted using the PAC system on the laboratory scaled overhead crane.

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Section: 2mentioning

confidence: 99%

“…Furthermore, the soft computing techniques, especially fuzzy logic, are widely employed to the considered problem. The linguistic-rule-based fuzzy controllers are reported in [10][11][12], as well as proposed for PID gains tuning [13,14], or sliding mode control [15]. TSK-type fuzzy controllers are proposed in [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Interval arithmetic-based fuzzy discrete-time crane control scheme design

Smoczek

2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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show abstract

“…Furthermore, the soft computing techniques, especially fuzzy logic, are widely employed to the considered problem. The linguistic-rule-based fuzzy controllers are reported in [10][11][12][13], as well as proposed for PID gains tuning [14,15], or sliding mode control [16]. TSK-type fuzzy controllers are proposed for example in [17,18].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic-based adaptive control system prototypying for laboratory scaled overhead crane

Smoczek

Szpytko

2013

2013 18th International Conference on Methods &Amp; Models in Automation &Amp; Robotics (MMAR)

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The prototyping process of a fuzzy logic-based antisway control scheme for the laboratory scaled overhead crane is described. The software-hardware equipment of control system, as well as the contact and contactless techniques of sway angle of a payload sensing which have been implemented on the laboratory stand are presented. Proposed anti-sway control system is based on a set of the linear controllers determined at the selected operating points using a pole placement method (PPM) and the fuzzy interpolation of controllers parameters within the range of scheduling variables: rope length and mass of a payload. The control algorithm, a linear dynamic model of a planar crane, as well as the fuzzy schedulers of controllers and model's parameters were implemented using structured text (ST) on the PAC system with RX3i controller. The softwarehardware tools allows to automate the crane's models identifications for the selected operating points, determine the controllers parameters, validate the closed-loop system through real-time simulating on the PAC system and finally pass to conducting the experiments on the laboratory device.

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“…Moon et al (1996) applied fuzzy logic to perform an optimal control scheme, while Liu et al (2005) incorporated a fuzzy system into a sliding mode control strategy. Linguistic-rule-based fuzzy controllers are reported by Benhidjeb and Gissinger (1995), Mahfouf et al (2000), Yi et al (2003) and Chang (2006), and proposed for tuning gains of a PID controller by Li and Yu (2012) or Solihin et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary optimization of interval mathematics-based design of a TSK fuzzy controller for anti-sway crane control

Smoczek¹

2013

International Journal of Applied Mathematics and Computer Science

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A hybrid method combining an evolutionary search strategy, interval mathematics and pole assignment-based closed-loop control synthesis is proposed to design a robust TSK fuzzy controller. The design objective is to minimize the number of linear controllers associated with rule conclusions and tune the triangular-shaped membership function parameters of a fuzzy controller to satisfy stability and desired dynamic performances in the presence of system parameter variation. The robust performance objective function is derived based on an interval Diophantine equation. Thus, the objective of a fuzzy logic-based control scheme is to place all the closed-loop control system characteristic polynomial coefficients within desired intervals. The reproduction process in the proposed Evolutionary Algorithm (EA) is based on the arithmetical crossover, uniform and non-uniform mutation along with gene deletion/insertion mutation ensuring a diversity of genomes sizes, as well as a diversity in the parameter space of membership functions. The proposed algorithm was implemented to design a fuzzy logic-based anti-sway crane control system taking into consideration the rope length and the mass of a payload variation. The results of experiments conducted using the EA for different conditions assumed for system parameter intervals and desired closed-loop system performances are compared with results achieved using the iterative procedure which is also described in the paper.

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The switching algorithm for the control of overhead crane

Cited by 20 publications

References 21 publications

Interval arithmetic-based fuzzy discrete-time crane control scheme design

Interval arithmetic-based fuzzy discrete-time crane control scheme design

Fuzzy logic-based adaptive control system prototypying for laboratory scaled overhead crane

Evolutionary optimization of interval mathematics-based design of a TSK fuzzy controller for anti-sway crane control

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