Browsing by Author "Morimoto, J."

Now showing 1 - 8 of 8

Metadata only
Assessments on the improved modelling for pneumatic artificial muscle actuators
(IEEE, 2015) Peternel, L.; Uğurlu, Regaip Barkan; Babic, J.; Morimoto, J.; Mechanical Engineering; UĞURLU, Regaip Barkan
In this paper, we present an analysis regarding the pneumatic air muscle modelling, with a particular emphasis on the exoskeleton robot control. We propose two calibration approaches for obtaining the model identification data. We used the measurement data acquired from the proposed approaches to identify different mathematical models of pneumatic muscles. These models specified the necessary muscle control pressure for the desired muscle force at a given muscle length value. We compared the performance between the different types of models identified by either of the calibration method. The identified model with the lowest validation error was implemented in pneumatic muscle control for an elbow exoskeleton system. As a result, the system exhibited satisfactory torque and position control tasks, adequately validating the proposed approach.
Open Access
Human-in-the-loop control and task learning for pneumatically actuated muscle based robots
(Frontiers Media, 2018-11-06) Teramae, T.; Ishihara, K.; Babič, J.; Morimoto, J.; Öztop, Erhan; Computer Science; Conradt, J.; ÖZTOP, Erhan
Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-To-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to biologically inspired control approaches. In spite of these advantages, they have not been widely adopted in human-in-The-loop control and learning applications. In this study, we propose a biologically inspired multimodal human-in-The-loop control system for driving a one degree-of-freedom robot, and realize the task of hammering a nail into a wood block under human control. We analyze the human sensorimotor learning in this system through a set of experiments, and show that effective autonomous hammering skill can be readily obtained through the developed human-robot interface. The results indicate that a human-in-The-loop learning setup with anthropomorphically valid multi-modal human-robot interface leads to fast learning, thus can be used to effectively derive autonomous robot skills for ballistic motor tasks that require modulation of impedance.
Metadata only
On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton
(IEEE, 2015) Ullauri, J. B.; Petenel, L.; Uğurlu, Regaip Barkan; Yamada, Y.; Morimoto, J.; Mechanical Engineering; UĞURLU, Regaip Barkan
Exoskeletons are successful at supporting human motion only when the necessary amount of power is provided at the right time. Exoskeleton control based on EMG signals can be utilized to command the required amount of support in real-time. To this end, one needs to map human muscle activity to the desired task-specific exoskeleton torques. In order to achieve such mapping, this paper analyzes two distinct methods to estimate the human-elbow-joint torque based on the related muscle activity. The first model is adopted from pneumatic artificial muscles (PAMs). The second model is based on a machine learning method known as Gaussian Process Regression (GPR). The performance of both approaches were assessed based on their ability to estimate the elbow-joint torque of two able-bodied subjects using EMG signals that were collected from biceps and triceps muscles. The experiments suggest that the GPR-based approach provides relatively more favorable predictions.
Metadata only
Shoulder glenohumeral elevation estimation based on upper arm orientation
(IEEE, 2018-10-26) Hamdan, Sara; Öztop, Erhan; Furukawa, J.-I.; Morimoto, J.; Uğurlu, Regaip Barkan; Computer Science; Mechanical Engineering; ÖZTOP, Erhan; UĞURLU, Regaip Barkan; Hamdan, Sara
In this paper, the shoulder glenohumeral displacement during the movement of the upper arm is studied. Four modeling approaches were examined and compared to estimate the humeral head elevation (vertical displacement) and translation (horizontal displacement). A biomechanics-inspired method was used firstly to model the glenohumeral displacement in which a least squares method was implemented for parameter identification. Then, three Gaussian process regression models were used in which the following variable sets were employed: i) shoulder adduction/abduction angle, ii) combination of shoulder adduction/abduction and flexion/extension angles, iii) overall upper arm orientation in the form of quaternions. In order to test the respective performances of these four models, we collected motion capture data and compared the models' representative capabilities. As a result, Gaussian process regression that considered the overall upper arm orientation outperformed the other modeling approaches; however, it should be noted that the other methods also provided accuracy levels that may be sufficient depending on task requirements.
Open Access
Stable control of force, position, and stiffness for robot joints powered via pneumatic muscles
(IEEE, 2019-12) Uğurlu, Regaip Barkan; Forni, P.; Doppmann, C.; Sarıyıldız, E.; Morimoto, J.; Mechanical Engineering; UĞURLU, Regaip Barkan
This paper proposes a novel controller framework for antagonistically driven pneumatic artificial muscle (PAM) actuators. The proposed controller can be stably configured in both torque-stiffness control and position-stiffness control modes. Three contributions are sequentially presented in constructing the framework: 1) A PAM force feedback controller with guaranteed stability is synthesized in a way so as to contend with nonlinear PAM characteristics; 2) a mathematical tool is developed to compute reference PAM forces, for a given set of desired joint torque and joint stiffness inputs; and 3) on top of the torque controller, a position control scheme is implemented and its stability analysis is given in the sense of Lyapunov. In order to test the controller framework, an extensive set of experiments are conducted using an actuator that is constructed using two antagonistically coupled PAMs. As a result, the actuator exhibits satisfactory tracking performances concerning both torque-stiffness control and position-stiffness control modes.
Open Access
Torque and variable stiffness control for antagonistically driven pneumatic muscle actuators via a stable force feedback controller
(IEEE, 2015) Uğurlu, Regaip Barkan; Forni, P.; Doppmann, C.; Morimoto, J.; Mechanical Engineering; UĞURLU, Regaip Barkan
This paper describes a novel controller that is capable of simultaneously controlling torque and variable stiffness in real-time, for actuators with antagonistically driven pneumatic artificial muscles (PAMs). To this end, two contributions are presented: i) A stable force feedback controller that can cope with inherent PAM nonlinearities is synthesized using the dissipativity theory, for each PAM unit. ii) On top of this force feedback controller, a mathematical formulation is developed to compute reference force inputs that correspond to desired joint torque and joint stiffness inputs, concerning both agonist and antagonist PAMs. This strategy enables us to introduce real-time sensory feedback; torque and stiffness control is addressed by means of PAM force feedback control with guaranteed stability. To validate the proposed control scheme, a series of experiments were conducted on an experimental setup. As the result, the controller exhibited favorable torque and stiffness tracking in real-time, demonstrating that it could meet the performance criteria to power exoskeleton systems.
Open Access
Towards balance recovery control for lower body exoskeleton robots with variable stiffness actuators: spring-loaded flywheel model
(IEEE, 2015) Doppmann, C.; Uğurlu, Regaip Barkan; Hamaya, M.; Teramae, T.; Noda, T.; Morimoto, J.; Mechanical Engineering; UĞURLU, Regaip Barkan
This paper presents a biologically-inspired real-time balance recovery control strategy that is applied to a lower body exoskeleton with variable physical stiffness actuators at its ankle joints. For this purpose, a torsional spring-loaded flywheel model is presented to encapsulate both approximated angular momentum and variable physical stiffness, which are crucial parameters in describing the postural balance. In particular, the incorporation of physical compliance enables us to provide three main contributions: i) A mathematical formulation is developed to express the relation between the dynamic balance criterion ZMP and the physical ankle joint stiffness. Therefore, balancing control can be interpreted in terms of ankle joint stiffness regulation. ii) `Variable physical' stiffness is utilized in the bipedal robot balance control task for the first time in the literature, to the authors' knowledge. iii) The variable physical stiffness strategy is compared with the optimal constant stiffness strategy by conducting experiments on our exoskeleton robot. The results indicate that the proposed method provides a favorable balancing control performance to cope with unperceived perturbations, in terms of center of mass position regulation, ZMP error and mechanical power.
Open Access
Variable ankle stiffness improves balance control: experiments on a bipedal exoskeleton
(IEEE, 2016-02) Uğurlu, Regaip Barkan; Doppmann, C.; Hamaya, M.; Forni, P.; Teramae, T.; Noda, T.; Morimoto, J.; Mechanical Engineering; UĞURLU, Regaip Barkan
This paper proposes a real-time balance control technique that can be implemented to bipedal robots (exoskeletons, humanoids) whose ankle joints are powered via variable physical stiffness actuators. To achieve active balancing, an abstracted biped model, torsional spring-loaded flywheel, is utilized to capture approximated angular momentum and physical stiffness, which are of importance in postural balancing. In particular, this model enables us to describe the mathematical relation between zero moment point (ZMP) and physical stiffness. The exploitation of variable physical stiffness leads to the following contributions: 1) Variable physical stiffness property is embodied in a legged robot control task, for the first time in the literature to the authors' knowledge. 2) Through experimental studies conducted with our bipedal exoskeleton, the advantages of variable physical stiffness strategy are demonstrated with respect to the optimal constant stiffness strategy. The results indicate that the variable stiffness strategy provides more favorable results in terms of external disturbance dissipation, mechanical power reduction, and ZMP/center of mass position regulation.