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1

Robust predictive control of an overhead crane

Smoczek J., Szpytko J.

Journal of KONES

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2017

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Vol. 24, No. 2

231--238

EN

The predictive control scheme is developed for an overhead crane using the generalized predictive procedure applied for the discrete time linear parameter-varying model of a crane dynamic. The robust control technique is developed with respect to the constraints of sway angle of a payload and control input signal. The two predictive strategies are presented and compared experimentally. In the first predictive control scheme, the online estimation of the parameters of a crane dynamic model is performed using the recursive least square algorithm. The second approach is a sensorless anti-sway control strategy. The sway angle feedback signal is estimated by a linear parameter-varying model of an unactuated pendulum system with the parameters interpolated using a quasi-linear fuzzy model designed through utilizing the P1-TS fuzzy theory. The fuzzy interpolator is applied to approximate the parameters of a crane discrete-time dynamic model within the range of scheduling variables changes: the rope length and mass of a payload. The experiments carried out on a laboratory scaled overhead crane confirmed effectiveness and feasibility of the proposed solutions. The implementation of control systems was performed using the PAC system with RX3i controller. The series of experiments carried out for different operating points proved robustness of the control approaches presented in the article.

2

Robust fuzzy model predictive control of an overhead crane

Smoczek J., Szpytko J.

Journal of KONES

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2015

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Vol. 22, No. 2

205--212

EN

The method of controlling an overhead crane with respect to the variation of operating conditions and control constraints is developed using a model predictive control (MPC) and fuzzy interpolation applied in linear parameter varying (LPV) approach to crane dynamic modelling. The proposed control approach is based on the assumption that operating conditions vary within the known range of scheduling variables, and the parameters of a crane dynamic model can be interpolated by a quasi-linear fuzzy model designed through utilizing the P1-TS fuzzy theory. Hence, a crane dynamic is approximated through interpolation between a set of local linear models determined through identification experiments at the local operating points selected within the bounded intervals of scheduling variables. For the modelling assumptions, the control algorithm is developed based on a generalized predictive control (GPC) procedure taking into consideration the constraints on sway angle of a payload and control signal. Feasibility and applicability of the proposed control technique were confirmed during experiments carried out on a laboratory-scaled overhead crane. The results of experiments are presented and compared with performances of a fuzzy logic-based scheduling control scheme.

3

River water quality assessment with fuzzy interpolation

Spinella S., Ostoich M., Anile A.M.

Ecological Chemistry and Engineering. S

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2008

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Vol. 15, nr 2

245-262

EN

This work concerns the interpolation of environmental data using fuzzy splines as alternative to statistical analysis in order to monitor water quality in a river. A fuzzy interpolated model representing the river water quality is constructed and then queried in order to retrieve information useful for planning precautionary measures. The results are compared with statistical model to evaluate significance of the quality classification. Geographical data concerning environment pollution consist of a large set of temporal measurements (representing, eg monthly measurements for several years) at a few scattered spatial sites. In this case the temporal data at a given site must be summarized in some form in order to employ it as input to build a spatial model. Summarizing the temporal data (data reduction) will necessarily introduce some form of uncertainty which must be taken into account. Fuzzy numbers can represent this uncertainty in a conservative way without any statistical a priori hypothesis. This method bas been employed for ocean floor geographical data by Patrikalakis (1995), in the interval case, and Anile et al. (2000), for fuzzy numbers, and to environmental pollution data by Anile et al. (2004). Fuzzy interpolation is carried out with splines to get a deterministic model for environmental pollution data. Then the model is interrogated by fuzzy queries to find the sites exceeding a quality threshold.

PL

Do analizy danych środowiskowych (w celu monitorowania jakości wody w rzekach), zamiast tradycyjnej analizy statystycznej, zastosowano interpolację rozmytą (z użyciem fuzzy splines). Został skonstruowany i przetestowany odpowiedni model, przydatny do prac planistycznych, określający jakość wody. Uzyskane wyniki porównano z danymi modelu statystycznego. Model z interpolacją rozmytą pozwala na przewidywania wartości wielu chwilowych parametrów (np. średnie miesięczne wartości poziomu zanieczyszczeń z kilku lat) oraz ich niepewności pomiarowych, z wielu oddalonych od siebie miejsc pobierania próbek. Metoda ta była już stosowana przez Patrikalakis (1995) do badań dna oceanu, a także przez Anile i in. (2004) do opracowania danych, dotyczących zanieczyszczenia środowiska.