Application of Model Predictive Control in Physical Human-Machine Interaction

Paterna, Maria; Quiñones, Daniel Pacheco; Benedictis, Carlo De; Maffiodo, Daniela; Franco, Walter; Ferraresi, Carlo

doi:10.1007/978-3-031-04870-8_67

Cited by 1 publication

(5 citation statements)

References 13 publications

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“…A previous work [38] focused on the identification of the best set of parameters for the handheld configuration. The tuning parameters were R u , Q , H p , Sdt , and k a as the operator stiffness k a is not known a priori.…”

Section: Hardware and Human-in-the-loop Testing And Discussionmentioning

confidence: 99%

“…The presented MPC algorithm can be used to control the mechanical disturbances (i.e., perturbations) provided to a patient’s body [37, 38, 40] to investigate balance and posture issues. Preliminary studies highlighted that the force impulse (force-time integral, FI) resulting from the contact should range within 2–10 Ns to elicit a detectable postural response and, at the same time, to keep the subject in the standing position without any risk of falling [41, 42].…”

Section: Model Predictive Control To Manage Human-machine Interactionmentioning

confidence: 99%

“…The application of the mechanical disturbances is performed by means of the perturbation device, as shown in Fig. 2a, whose architecture has already been outlined in previous works [37, 38]. It includes a tubular electric linear motor (1) ( GD160Q, NiLAB GmbH, Klagenfurt am Woörthersee, Austria ), allowing accurate rod motion control while developing the high acceleration necessary to meet the specifications of the contact force profile.…”

Section: Model Predictive Control To Manage Human-machine Interactionmentioning

confidence: 99%

“…The switching behavior of the proposed control logic led to an undesired delay between human-device contact and the actual control of the force signal. A renewed control logic able to bypass the issue is preliminarily presented in Paterna et al [38], of which the present paper represents an extended version. The proposed solution's control performance, flexibility, and robustness are verified in a wider scenario, including HIL and HuIL trials.…”

Section: Introductionmentioning

confidence: 99%

“…In previous works from the authors [37, 38], an MPC algorithm is developed to impress on a target human body a well-defined and tunable force impulse during posturography trials, in which human balance is assessed for clinical purposes. The system consists of an electric actuator and a hybrid force/speed control architecture.…”

Section: Introductionmentioning

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: Hardware and Human-in-the-loop Testing And Discussionmentioning

confidence: 99%