Hardware-In-the-Loop Equipment for the Development of an Automatic Perturbator for Clinical Evaluation of Human Balance Control

Ferraresi, Carlo; Maffiodo, Daniela; Franco, Walter; Muscolo, Giovanni Gerardo; Benedictis, Carlo De; Paterna, Maria; Pica, Oliviero Walter; Genovese, Marco; Quiñones, Daniel Pacheco; Roatta, Silvestro; Dvir, Zeevi

doi:10.3390/app10248886

Cited by 6 publications

(14 citation statements)

References 45 publications

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“…The control robustness is verified by recruiting 10 untrained operators (7 males; 3 females; 24-52 years) to hit a fixed rigid target. In this series, the perturbations had the same duration (250 ms) but different force magnitudes [20,30,40, 50] N. Each operator provided 22 perturbations, the first two to familiarize with the device and then five perturbations for each force level in random order. D. Control flexibility test.…”

Section: Testing Setup and Evaluation Criteriamentioning

confidence: 99%

“…D. Control flexibility test. Finally, the control flexibility is verified by evaluating the device performance for different perturbation magnitudes (rectangular pulses of [20,30,40,50] N and 250 ms) in both HIL and HuIL configurations. The latter involved only one operator using the device against a fixed target and on a healthy subject.…”

Section: Testing Setup and Evaluation Criteriamentioning

confidence: 99%

“…Each of them handles the prototype to hit a fixed target to assess the effect of operator behavior on device performance. Posing the same reference duration of 250 ms, reference magnitudes f r and force impulses FI r are, respectively, equal to (20,30,40,50) N and (5.0,7.5,10.0,12.5) Ns. The 10 averaged contact force profiles, shown in Fig.…”

Section: C: Control Robustnessmentioning

confidence: 99%

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Contact force regulation in physical human-machine interaction based on model predictive control

Pacheco Quiñones,

Paterna,

De Benedictis

et al. 2023

Robotica

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With increasing attention to physical human-machine interaction (pHMI), new control methods involving contact force regulation in collaborative and coexistence scenarios have spread in recent years. Thanks to its internal robustness, high dynamic performance, and capabilities to avoid constraint violations, a Model Predictive Control (MPC) action can pose a viable solution to manage the uncertainties involved in those applications. This paper uses an MPC-driven control method that aims to apply a well-defined and tunable force impulse on a human subject. After describing a general control design suitable to achieve this goal, a practical implementation of such a logic, based on an MPC controller, is shown. In particular, the physical interaction considered is the one occurring between the body of a patient and an external perturbation device in a dynamic posturography trial. The device prototype is presented in both its hardware architecture and software design. The MPC-based main control parameters are thus tuned inside hardware-in-the-loop and human-in-the-loop environments to get optimal behaviors. Finally, the device performance is analyzed to assess the MPC algorithm’s accuracy, repeatability, flexibility, and robustness concerning the several uncertainties due to the specific pHMI environment considered.

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Section: Testing Setup and Evaluation Criteriamentioning

confidence: 99%

Section: Testing Setup and Evaluation Criteriamentioning

confidence: 99%

Section: C: Control Robustnessmentioning

confidence: 99%

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Contact force regulation in physical human-machine interaction based on model predictive control

Pacheco Quiñones,

Paterna,

De Benedictis

et al. 2023

Robotica

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“…The valves, modelled by mathematical equations which describe the air flow G passing through them, are considered as pneumatic resistances characterised by a conductance C and a critical ratio b . 33–35 The valves connect two environments a and b with pressures p a and p b respectively. Taking the direction a − b as positive, the equations are:…”

Section: Seat Cushion: Modelling and Designmentioning

confidence: 99%

Modelling, design and control of a new seat-cushion for pressure ulcers prevention

Mannella

Bellusci

Graziani

et al. 2022

Proc Inst Mech Eng H

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Pressure ulcers are a frequent complication in patients having limited activity and mobility (e.g., elderly people, spinal cord injury patients, people with disabilities, etc.). The aim of this work is the conceptual design, modelling and control of a new seat cushion for pressure ulcers prevention. The whole system (constituted by the seat cushion equipped with a real-time pressure mapping with closed-loop control) is designed to identify the critical points on the human skin, before pressure ulcers creation, and to be able to distribute the contact pressure between the human and cushion avoiding wound creation. The seat cushion is constituted by soft air-cells actuated by air flow. To define the shape and size of the soft air-cells, finite element simulations are carried out, analysing the internal volume reduction with external loads application to reproduce the variable stiffness. The data obtained by finite element analysis are used to simulate inflation and deflation of the soft bubble air-cells. Finally, the control systems of a single air-cell and of the whole cushion are designed and simulated. The novelty of our work consists in the conception of a seat cushion able to recognise higher and lower risk zones of pressure ulcer generation on the human skin and to provide compensation automatically. This work can therefore be considered in line with the sustainable development goals recently launched by the EU Commission.

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“…HIL (hardware-in-the-loop) techniques are being increasingly used for testing and simulation of complex systems, such as power electronics [1][2][3][4] and mechanical elements [5][6][7][8]. These techniques consist in replacing an element of the real system with a model.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Deadtime Events in Power Converters with Half-Bridge Modules for a Highly Accurate Hardware-in-the-Loop Fixed Point Implementation in FPGA

2021

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Hardware-in-the-loop (HIL) simulations of power converters must achieve a truthful representation in real time with simulation steps on the order of microseconds or tens of nanoseconds. The numerical solution for the differential equations that model the state of the converter can be calculated using the fourth-order Runge–Kutta method, which is notably more accurate than Euler methods. However, when the mathematical error due to the solver is drastically reduced, other sources of error arise. In the case of converters that use deadtimes to control the switches, such as any power converter including half-bridge modules, the inductor current reaching zero during deadtimes generates a model error large enough to offset the advantages of the Runge–Kutta method. A specific model is needed for such events. In this paper, an approximation is proposed, where the time step is divided into two semi-steps. This serves to recover the accuracy of the calculations at the expense of needing a division operation. A fixed-point implementation in VHDL is proposed, reusing a block along several calculation cycles to compute the needed parameters for the Runge–Kutta method. The implementation in a low-cost field-programmable gate arrays (FPGA) (Xilinx Artix-7) achieves an integration time of 1μs. The calculation errors are six orders of magnitude smaller for both capacitor voltage and inductor current for the worst case, the one where the current reaches zero during the deadtimes in 78% of the simulated cycles. The accuracy achieved with the proposed fixed point implementation is very close to that of 64-bit floating point and can operate in real time with a resolution of 1μs. Therefore, the results show that this approach is suitable for modeling converters based on half-bridge modules by using FPGAs. This solution is intended for easy integration into any HIL system, including commercial HIL systems, showing that its application even with relatively high integration steps (1μs) surpasses the results of techniques with even faster integration steps that do not take these events into account.

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Hardware-In-the-Loop Equipment for the Development of an Automatic Perturbator for Clinical Evaluation of Human Balance Control

Cited by 6 publications

References 45 publications

Contact force regulation in physical human-machine interaction based on model predictive control

Contact force regulation in physical human-machine interaction based on model predictive control

Modelling, design and control of a new seat-cushion for pressure ulcers prevention

Modeling of Deadtime Events in Power Converters with Half-Bridge Modules for a Highly Accurate Hardware-in-the-Loop Fixed Point Implementation in FPGA

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