Analyzing the Influence of Measurements in Dynamical Parameter Identification Using Parametric Sensitivities

Wiesner, M.; Schäfer, Kai; Bergmann, Wiebke; Berger, Arne; Shulpyakov, P.; Dittert, C.; Büskens, Christof

doi:10.1016/j.ifacol.2021.10.320

Cited by 2 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results of the parameter identification procedure for the slip-free model are shown in Table 3, where 𝑓 0 is the final value of the cost function of the optimal problem as formulated in Equation (10) defining the prediction accuracy relative to the training set and 𝑓 v is the validation score. It can be observed that while the single shooting method and the full discretizazion problem yields equivalent results, the gradient matching approach appears to be slightly more robust while at the same time showing a worse training accuracy as expressed by the final value of the cost function 𝑓 0 .…”

Section: Slip-free Model Resultsmentioning

confidence: 99%

“…The optimization problem is solved using the Sequential Quadratic Programming (SQP) implementation of the WORHP solver for nonlinear programming [9]. In Sections 4.1–4.3, the WORHP‐based single shooting , full discretization , and gradient matching methods are, respectively, shown with an outline of their mathematical formulation [10]. In Figure 4, the overall procedure for the identification of the unknown parameters and the subsequent validation is illustrated.…”

Section: Methods For Parameter Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and parameter identification for agricultural machines

Simonelli,

Büskens

2023

Proc Appl Math and Mech

View full text Add to dashboard Cite

A series of technical and social aspects have led the pressure to increase energy efficiency and reduce the level of environmental impact in agricultural production. Crop producers and equipment manufacturers have identified computerized assistance in operation management as an indispensable tool to optimize agricultural processes. A central place is occupied in this regard by the so called digital twins, computer models of a specific instance of an object or system, which are also capable of exchanging data with the physical counterpart and updating their state accordingly. The core of a digital twin is given by the digital mock‐up, or offline digital twin, that is the computer model of the system's physics. The goal of this work is to present the development of an offline digital twin for a novel gantry‐tractor type autonomous agricultural machine. The peculiarity of this system resides in its until now scarcely adopted mechanical design, and its ability to perform a range of processes by attaching different modules to its frame. After a brief introduction into the characteristics of a gantry‐type tractor and its role in the context of precision autonomous agriculture and controlled traffic farming, different components of the machine and dynamic phenomena are separately investigated and mathematically modeled. Particular focus is placed thereby on the study of the vehicle's longitudinal dynamics. Based on the software implementation of the mathematical models, the unknown parameters of the system model are estimated by means of nonlinear optimization techniques, using sensor data collected during field operation as the measurement reference. The accuracy of different estimation methods is then evaluated against experimental samples collected by sensors.

show abstract

Section: Slip-free Model Resultsmentioning

confidence: 99%

Section: Methods For Parameter Identificationmentioning

confidence: 99%

Modeling and parameter identification for agricultural machines

Simonelli,

Büskens

2023

Proc Appl Math and Mech

View full text Add to dashboard Cite

show abstract

“…The model's state derivative values depend on the optimization parameters p ∈ R np , current state values, and on the controls applied (usually supplied in the measurement data), which have to be approximated to the time point of model evaluation as u app (t) ∈ R nu . Solving this problem is possible in a number of ways, in the following the approach of full discretization [7] as implemented in the software tool Topas Model Fitting [8] will be used, relying on the nonlinear solver WORHP [9]. With the correct parameters found, the model function can be applied to generate a forecast of the respective propertyfor a dynamical system, that means integrating the ODE (here using the odeint-function of Python Library SciPy).…”

Section: Theoretical Background 21 Data-based Modelling and Parameter...mentioning

confidence: 99%

Towards modelling of energy storages for use in an intelligent energy management system

Kappertz

Büskens

2023

Proc Appl Math and Mech

View full text Add to dashboard Cite

With the transition towards renewable energy underway, demand‐side management together with the local generation of renewable energy is receiving growing attention. Optimizing the self‐consumption of locally produced renewable energy can not only have financial benefits for the respective household or business (and improve their autarky from increasingly unstable energy markets), but also help improve grid stability against the volatility of some sources of renewable energy. In order to optimize the self‐consumption of a given household or enterprise with an energy management system, the energy demand and generation, as well as the behaviour of all controlled devices have to be forecast. There are many different methods of mathematically modelling these different properties, but the task becomes especially challenging for dynamic systems like energy storage systems. Here, the state of charge at a specific time point depends on both external influences and the previous time point's state of charge, thus modelling errors quickly accumulate. In the context of small and medium agricultural enterprises, examples of such dynamic systems can be electrical storages, like a battery, or thermal storages like a milk cooling tank or a heat pump. In this work, a common way for the modelling of these systems is explored, namely parameter identification. Here, the parameters of an ordinary differential equation representing the assumed physical behaviour of the system are identified from measured data of states and controls in an optimization problem. The success of this approach, and with it the performance of the derived model depends on how well the physical equations actually describe the system, but also on the quality, quantity and content of the measurement data used. In the case of a household battery storage already a simple physical model with parameters identified from measurement data of state of charge and battery power provides useful results. The example of a milk cooling tank provides a situation where external forcing has an important influence on the system's state of charge, its temperature. The cleaning of the tank generates a large heat influx, and raises the temperature, while the filling and emptying of milk influences the heat capacity. While these influences are shown to also be representable by equations based on the physical processes, parameter identification becomes difficult when they are not included in the measurement data. Thus, different approaches to derive these external forcings from the available data are outlined.

show abstract

Analyzing the Influence of Measurements in Dynamical Parameter Identification Using Parametric Sensitivities

Cited by 2 publications

References 10 publications

Modeling and parameter identification for agricultural machines

Modeling and parameter identification for agricultural machines

Towards modelling of energy storages for use in an intelligent energy management system

Contact Info

Product

Resources

About