High‐gain nonlinear observer‐based impedance control for deformable object cooperative teleoperation with nonlinear contact model

Abstract

Summary Unmeasurable object deformation and local communication time delays between the slave robots influence the manipulation effect for multirobot multioperator teleoperation. In this article, a distributed control method based on high‐gain nonlinear observer, interactive identification, and impedance control is proposed for this problem. First, we use Hunt‐Crossley contact model and deduce the desired synchronizing object state in cooperative teleoperation. Second, an impedance item expressed by the internal position errors is presented to decrease object position tracking errors. For the unmeasurable object deformation, an interactive identification method is proposed for estimating unknown variables. Third, we consider both varying communication time delays and local time delays in the slave side. Two mirror high‐gain nonlinear observers are designed for estimating other slave robots' real‐time state. Finally, we build the system controllers and prove the stability of the closed‐loop system and the boundless of estimating errors using Lyapunov functions. Comparable simulation results executed by the physical system present that the position and internal force tracking errors of the object decrease in the designated cooperative tasks.

Adaptive neural learning fixed‐time control for uncertain teleoperation system with time‐delay and time‐varying output constraints

“…The time-varying candidate boundary is k c (t) = [0.095 − 0.01 cos(t), 0.085 + 0.01 sin(t), 0.06 − 0.01 cos(t)] T m. T f = 0.35 + 0.001 sin(18t) + 0.01 sin(16t) + 0.015 sin(13t) s, T b = 0.35 + 0.008 sin(17t) + 0.012 sin(12t) s are considered as the forward and backward delays, respectively, as shown in Figure 3. The reference trajectory of the leader x r F (t) can be generated by setting the human force as [0.14 sin(t) + 0.1; 0.16 cos(t) + 0.05; 0.07 sin(t) + 0.04] N and the environmental force as [0.05 sin(t); 0.04 cos(t); 0.03 sin(t)] N, the parameters for the virtual control term and fixed-time controller are selected as k j1 = diag(0.55, 0.55, 0.55), k j2 = diag(1, 1, 1), K j3 = diag (10,12,9), K j4 = diag(0.8, 0.8, 0.8), 𝜉 j1 = 0.65, 𝜉 j2 = 4.…”

“…Stability is the precondition for teleoperation systems to perform tasks. To this end, academics have proposed many control algorithms, such as passive control, 6 wave-variable control, 7 PD control, 8 adaptive control, 9 impedance control, 10 and intelligent control, 11 to cope with time delay and reduce its negative effects as much as possible. However, the preceding control solutions emphasize stability under time-dealy while neglecting the transparency of teleoperation system.…”

Observer‐based adaptive control of cooperative multiple manipulators using the Mastroianni operators as uncertainty approximator

“…For example, high speed and accuracy make industrial manipulators widely used in the production processes. With the advancement of technology and the need to increase the quality of products, cooperating robots, including arms, mobile robots, or UAVs, have become the center of attention 1–4 . Performing industrial tasks such as welding, transportation, painting, packaging, and assembly require high maneuverability, so they can not be done by a single robot and require the use of a team of robots.…”

“…With the advancement of technology and the need to increase the quality of products, cooperating robots, including arms, mobile robots, or UAVs, have become the center of attention. [1][2][3][4] Performing industrial tasks such as welding, transportation, painting, packaging, and assembly require high maneuverability, so they can not be done by a single robot and require the use of a team of robots. On the other hand, increasing the number of robots raises the system complexity and challenges their control.…”

Grasp Planning Pipeline for Robust Manipulation of 3D Deformable Objects with Industrial Robotic Hand + Arm Systems

“…In order to solve all these limitations of reactive strategies, it is crucial to use a suitable contact model ( [29][30][31][32]) that explains the coupling between the contact forces and the object deformations. A precise contact model can be exploited to provide a set of grasping forces and torques that can be applied to maintain equilibrium before and after the deformation [33].…”