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The basic diagram of two-DOF soft robotic arm containing the main control elements (left) and the dimensions of the robotic arm (right)
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Soft robotic arm actuated with pneumatic artificial muscles (PAMs) exhibits various interesting properties (static or dynamic) in contrast to conventional robots with electric motors. In addition to nonlinear relationship between various variables, PAMs are known for the hysteresis inevitably associated with their specific construction. In order to...
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... control of the system as well as all necessary processing of relevant data were carried out in Matlab/Simulink environment. We can see the basic diagram of the system depicting all important system components in Fig.3. In the right part of picture, the dimensions of the manipulator are shown. ...
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Citations
... Hysteresis characteristics usually play a role in evaluating the performance of soft actuators [36]. These characteristics, commonly identified in soft robotic systems, denote the delay or lag in the material's response to changes in input stimuli. ...
Soft robotics has emerged as a promising field due to the unique characteristics offered by compliant and flexible structures. Overcoming the challenge of precise position control is crucial in the development of such systems that require accurate modeling of soft robots. In response, a hybrid-actuated soft robot employing both air pressure and tendons was proposed, modeled, and validated using the dynamic Cosserat rod theory. This approach comprehensively addresses various aspects of deformation, including bending, torsion, shear, and extension. The designed robot was intended for robot-assisted cardiac ablation, a minimally invasive procedure that is used to treat cardiac arrhythmias. Within the framework of the Cosserat model, dynamic equations were discretized over time, and ordinary differential equations (ODEs) were solved at each time step. These equations of motion facilitated the prediction of the robot’s response to different control inputs, such as the air pressure and tension applied to the tendons. Experimental studies were conducted on a physical prototype to examine the accuracy of the model. The experiments covered a tension range of 0 to 3 N for each tendon and an air pressure range of 0 to 40 kPa for the central chamber. The results confirmed the accuracy of the model, demonstrating that the dynamic equations successfully predicted the robot’s motion in response to diverse control inputs.
... This behavior is interesting but it increases the complexity in the modeling and control process of the robot because the controller must keep track of whether the robot is inflating or deflating. There have been only a few papers working on the hysteresis of soft robots such as the work of Hošovský et al. (2016) where hysteretic behavior of a two-DOF soft robotic arm is analyzed, or the work of Vo-Minh et al. (2011) where the hysteresis is modeled using several Maxwell-slip elements each with different stiffness and saturation force. Our paper applies a modified generalized Duhem model to describe the hysteresis behavior of the robot. ...
This paper presents an observer architecture that can estimate a set of configuration space variables, their rates of change and contact forces of a fabric-reinforced inflatable soft robot. We discretized the continuum robot into a sequence of discs connected by inextensible threads; this allows great flexibility when describing the robot’s behavior. At first, the system dynamics is described by a linear parameter-varying (LPV) model that includes a set of subsystems, each of which corresponds to a particular range of chamber pressure. A real-world challenge we confront is that the physical robot prototype exhibits a hysteresis loop whose directions depend on whether the chamber is inflating or deflating. In this paper we transform the hysteresis model to a semilinear model to avoid backward-in-time definitions, making it suitable for observer and controller design. The final model describing the soft robot, including the discretized continuum and hysteresis behavior, is called the semilinear parameter-varying (SPV) model. The semilinear parameter-varying observer architecture includes a set of sub-observers corresponding to the subsystems for each chamber pressure range in the SPV model. The proposed observer is evaluated through simulations and experiments. Simulation results show that the observer can estimate the configuration space variables and their rate of change with no steady-state error. In addition, experimental results display fast convergence of generalized contact force estimates and good tracking of the robot’s configuration relative to ground-truth motion capture data.
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... Since F spring F(p,κ) (see Eq. (28)) and damping coefficient c depends on displacement x, Sárosi et al. introduced a dynamic model in [26] and as a result of that Eq. (33) can be rewritten as The c(κ(x)) of Eq. (34) can be derived from force-contraction hysteresis curves (Fig. 15). Reasons of hysteresis can be found in [24], [25]. Numerical results shown in Table 4 concern the upper branch of hysteresis curves, therefore it is necessary to optimize the lower branches using Eq. ...
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Fluidic muscles are pneumatic artificial muscles with specific construction and improved characteristics over the muscles with separate tube and outer fibre shell (McKibben type). In the paper we investigate the dynamics of DMSP-5 fluidic muscle, which is treated as a SISO system with voltage as an input variable and its displacement as an output variable. Based on the random step excitation, it is shown here that Hammerstein model, which is an attractive model from control viewpoint, can be a suitable model architecture for modeling its dynamics. Possible structure of this model with 2-zeros,3-poles linear transfer function and sigmoid neural network approximation of static nonlinearity is identified and used for initialization of the estimation algorithm. The resulting model is based on triangular excitation signal using Levenberg-Marquardt optimization algorithm and achieves 93% fit on test data with total of 12 parameters.
The present paper deals with the issues regarding a signal averaging device and its theoretical and practical properties and functions. The paper also deals with acceleration loop speed servos with a subordinate acceleration controller as a result of application of the signal averaging device. The use of an acceleration loop in a speed control circuit facilitates achieving better qualities of the circuit. This paper deals with the results of the application of a speed signal averaging device and the acceleration loop. This device is applied in electric servo systems as well as electric servo systems of industrial robots. The results of the experimental measurements are similar to the theoretical results.
