Fuzzy-tuned PID control design for automatic gantry crane

Solihin, Mahmud Iwan; Wahyudi, Wahyudi

doi:10.1109/icias.2007.4658553

Cited by 23 publications

(27 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The parameters of the transfer function G 2 are listed in Table 1 and Table 2 where they are identified based on physical measurement. In addition, readers can refer to [5] for the procedure of experimental parametric identification in actuator's transfer function, G 1 . …”

Section: Generalized Model Of Pendulum-like Systemsmentioning

confidence: 99%

Robust controller design for uncertain parametric systems using modern optimization approach

Solihin

Akmeliawati

Legowo

2011

2011 4th International Conference on Mechatronics (ICOM)

Self Cite

View full text Add to dashboard Cite

This paper presents a robust feedback controller design for parametric uncertainty systems via constrained optimization. The proposed controller design employs modern optimization tools (Particle Swarm Optimization and Differential Evolution) in a single objective constrained optimization. The objective of the optimization is to search for a set of robust controller gains such that the stability radius of the closed-loop system is maximized. The constraint of the optimization is a closed-loop poles region which is directly related to the desired time-domain control performance. The proposed controller design is applied to control of pendulum-like systems (gantry crane, flexible joint and inverted pendulum). The results show the robust performance of the designed controller.

show abstract

Section: Generalized Model Of Pendulum-like Systemsmentioning

confidence: 99%

Robust controller design for uncertain parametric systems using modern optimization approach

Solihin

Akmeliawati

Legowo

2011

2011 4th International Conference on Mechatronics (ICOM)

Self Cite

View full text Add to dashboard Cite

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)

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Fuzzy-tuned PID control design for automatic gantry crane

Cited by 23 publications

References 8 publications

Robust controller design for uncertain parametric systems using modern optimization approach

Robust controller design for uncertain parametric systems using modern optimization approach

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

Contact Info

Product

Resources

About