Utilizing optimal control and physical measurements when developing operator assist, automatic functions and autonomous machines

Frank, Bobbie

doi:10.1109/iccsce.2016.7893555

Cited by 2 publications

(5 citation statements)

References 8 publications

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“…A secondary result is that the optimal control algorithm is ready to be used as input to operator assist systems, automatic functions and autonomous construction machine control [24]. Once the DEM simulations have been simulated in advance they can be used as a library, then the computation time for the optimization takes around 4 hours on a laptop.…”

Section: Discussionmentioning

confidence: 99%

“…This can be done by operator support systems, automatic functions or autonomous machines. Even if the method presented here will not directly deliver such systems, the results from the optimal control calculations can be used as input when developing them [24], hence the dashed circle in Figure 3. In addition, the "Work cycle optimization" will affect the "Operator optimization".…”

Section: Work Cyclementioning

confidence: 99%

“…The fuel efficiency gain distribution is highly dependent on the work cycle and the operator. More detailed studies on more operators, comparing to the optimum are presented in [24].…”

Section: Complete Work Cycle 14% 100%mentioning

confidence: 99%

“…In [22,23] dynamic programming is used to determine the size of the electrical energy storage in a diesel-electric hybrid machine, while [21] demonstrates how the method presented in this paper applied to all major subsystems in the complete machine. It was also shown in [24] to be possible to extract from the results of this method, the input required for operator assist systems, automatic functions, and autonomous construction machine control development. For a complex system such as a wheel loader, solving an optimal control problem, and ensuring the solution is a global optimum, requires thorough knowledge about the system.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal control of wheel loader actuators in gravel applications

Frank

Kleinert

Filla

2018

Automation in Construction

Self Cite

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Section: Discussionmentioning

confidence: 99%

Section: Work Cyclementioning

confidence: 99%

“…The fuel efficiency gain distribution is highly dependent on the work cycle and the operator. More detailed studies on more operators, comparing to the optimum are presented in [24].…”

Section: Complete Work Cycle 14% 100%mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal control of wheel loader actuators in gravel applications

Frank

Kleinert

Filla

2018

Automation in Construction

Self Cite

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“…Even though DP is not implementable in human-operated machines, given that the entire work cycle must be known in advance, it has been used to design and tune other types of controllers, 13,14 as well as to provide benchmark results for known work cycles. 15 Yet it must be noted that DP determines the optimum based on a discretised model and is therefore only as optimal as the modelling accuracy allows. In fact, the performance of a real system might be highly suboptimal with any model-based controller if the model itself has significant uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model predictive energy management of hydrostatic drive transmissions

Backas

Ghabcheloo

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, we devise a nonlinear model predictive control framework for the energy management of nonhybrid hydrostatic drive transmissions. The controller determines the optimal control commands of the actuators by minimising a cost function over a receding horizon. With our approach, the velocity-tracking error is minimised while keeping the fuel economy of the system high. The hydrostatic drive transmission system studied in this article is a typical commercial work machine, that is, there is no energy storage or alternative power source in the system (a nonhybrid hydrostatic drive transmission). We evaluate success with a validated simulation model of the hydrostatic drive transmission of a municipal tractor. In our experiments, a detailed system model is used both in the system simulation and in the prediction phase of the nonlinear model predictive control. The use of a detailed model in the nonlinear model predictive control framework places our design as a benchmark for controlling nonhybrid hydrostatic drive transmissions, when compared to solutions using simplified models or computationally less intensive control methods as in earlier work by the authors. Our nonlinear model predictive control approach enables numerically robust optimisation convergence with the utilised complex nonlinear model. Above all, this is accomplished with stabilising terminal constraints and distinctive terminal cost, both based on an optimal steady-state solution. In addition, a simple method to generate initial guesses for optimisation is introduced. When compared with the performance of a controller based on quasi-static models, our results show notable improvement in velocity tracking while maintaining high fuel economy. Furthermore, our experiments demonstrate that framing energy management as a nonlinear model predictive control provides a flexible and rigorous framework for fast velocity tracking and high energy efficiency. We also compare the results with those of an industrial baseline controller.

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Utilizing optimal control and physical measurements when developing operator assist, automatic functions and autonomous machines

Cited by 2 publications

References 8 publications

Optimal control of wheel loader actuators in gravel applications

Optimal control of wheel loader actuators in gravel applications

Nonlinear model predictive energy management of hydrostatic drive transmissions

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