In this paper, a new approach for modeling the static force characteristic of Festo pneumatic muscle actuators (PMAs) will be presented. The model is physically motivated and therefore gives a deeper understanding of the Festo PMA. After introducing the new model, it will be validated through a comparison to a measured force map of a Festo DMSP-10-250 and a DMSP-20-300, respectively. It will be shown that the error between the new model and the measured data is below 4.4% for the DMSP-10-250 and below 2.35% for the DMSP-20-300. In addition, the quality of the presented model will be compared to the quality of existing models by comparing the maximum error. It can be seen that the newly introduced model is closer to the measured force characteristic of a Festo PMA than any existing model.

In this paper, two different torque control approaches for PMA-driven (PMA = Pneumatic muscle actuator) revolute joints are presented and tested. In previous work controllers for PMA-driven robots are typically customized for the use on a specific robotic system. In contrast, the proposed controllers define a general control interface for every robot that is actuated by PMA-driven joints. It will be shown that controlling the torque of a PMA-driven joint enables the use of standard robotic motion control frameworks, because the torque represents the natural input of the robotic equation of motion. Therefore, both proposed torque control approaches are interconnecting PMAs and their challenging characteristics on the one hand and “conventional” motion control strategies for robots on the other hand. After a detailed discussion of two different torque control approaches, we show that a torque controller handles all characteristics and dynamics of a PMA-driven joint internally, which implies that only its bandwidth and its static torque characteristic must be taken into account for the design of the outer motion control loop. This feature simplifies the integration of PMA-driven joints in robotic systems enormously, as will be demonstrated by a design of a cascade-structured, flatness-based motion controller for an exemplary robot with one degree of freedom.

This paper explores the adaptation of pseudo-rigid-body models (PRBMs) for simulating large geometric nonlinear deflections in passive exoskeletons, expanding upon their traditional application in small compliant systems. Utilizing the AnyBody modeling system, this study employs force-dependent kinematics to reverse the conventional simulation process, enabling the calculation of forces from the deformation of PRBMs. A novel approach, termed “Constraint Force”, is introduced to facilitate this computation. The approach is thoroughly validated through comparative analysis with laboratory trials involving a beam under bending loads. To demonstrate the functionality, the final segment of this study conducts a biomechanical simulation incorporating motion capture data from a lifting test, employing a novel passive exoskeleton equipped with flexible spring elements. The approach is meticulously described to enable easy adaptation, with an example code for practical application. The findings present a user-friendly and visually appealing simulation solution capable of effectively modeling complex mechanical load cases. However, the validation process highlights significant systematic errors in the direction and amplitude of the calculated forces (20% and 35%, respectively, in the worst loading case) compared to the laboratory results. These discrepancies emphasize the inherent accuracy challenges of the “Constraint Force” approach, pointing to areas for ongoing research and enhancement of PRBM methods.

The authors would like to correct the content of Table 2 and the text above Table 2 in their article

Highly compliant Pneumatic Soft Actuators (PSAs) have the potential to perform challenging tasks in a broad range of applications that require shape-adaptive capabilities. Achieving accurate tracking control for such actuators with complex geometries and material compositions typically involves many time-consuming and laborious engineering steps. In this work, we propose a data-driven learning-based control approach to address reference tracking tasks, incorporating self-adaptation in situ. We utilize a short interaction maneuver, recorded a priori, to collect the quasi-static data affected by severe hysteresis. Besides a linear feedback controller, we use two Gaussian process models to predict the feedforward control input to compensate for the nonlinearity in a one-shot learning setting. The proposed control approach demonstrates accurate tracking performance even under realistic varying configurations, such as alterations in mass and orientation, without any parameter tuning. Notably, training was achieved with only 25–50 s of experimental interaction, which emphasizes the plug-and-play capabilities in diverse real-world applications.