Model-based Force Control of a Tendon-Sheath Actuated Slender Gripper Without Output Feedback

Enhanced Tracking in Legged Robots through Model Reduction and Hybrid Control Techniques: Addressing Disturbances, Delays, and Saturation

“…We assume that ν 1 (t) = ν c remains constant over the time interval t t + ∆t , and ∆t is the prediction horizon. Using Equation (15), we obtained the prediction model in Equation (16), where ϖ is the cost function for the system stability, e r1 (t + ∆t) = θ 1d − θ 1 (t + ∆t) is the predicted angle error, and . e r1 (t + ∆t) = 0 − .…”

“…Using Equation (15), we obtained the prediction model in Equation ( 16), where 𝜛 is the cost function for the system stability, 𝑒 (𝑡 + ∆𝑡) = 𝜃 − 𝜃 (𝑡 + ∆𝑡) is the predicted angle error, and 𝑒 (𝑡 + ∆𝑡) = 0 − 𝜃 (𝑡 + ∆𝑡) is the predicted velocity error. The horizontal time ∆𝑡 and weight factor 𝜇 are positive parameters.…”

“…Reference [14] utilized cross-coupling error control and SMC to track the trajectories of synchronous moving arms and demonstrated their efficacy through simulations and comparisons with alternative controllers. Reference [15] proposed an adaptive SMC method for an end gripper that accurately predicts forces at a distance using a transmission model and a forcecontrol methodology. In [16], an SMC technique was proposed to separate position and force.…”

A novel rigid-soft gripper for safe and reliable object handling

“…This method involved optimizing both the chosen parameters of the gripper mechanism and the parameters of the finger geometry by utilizing task-specific finger designs acquired through dynamic simulation. Several methods to control and optimize the grasping force were presented in [13][14][15]. However, these grippers have intricate structures, sensor systems, and advanced control algorithms, and are only suitable for gripping rigid objects.…”