Ramil Khusainov

Adaptive control and parameter estimation have been widely employed in robotics to deal with parametric uncertainty. However, these techniques may suffer from parameter drift, dependence on acceleration estimates and conservative requirements for system excitation. To overcome these limitations, composite adaptation laws can be used. In this paper, we propose an enhanced composite adaptive control approach for robotic systems that exploits the accelerationfree momentum dynamics and regressor extensions to offer faster parameter and tracking convergence while relaxing excitation conditions and providing a clear physical interpretation. The effectiveness of the proposed approach is validated through experimental evaluation on a 3-DoF robotic leg.

In this work, a nonminimal coordinate representation of tensegrity structures with explicit constraints is introduced. A method is proposed for representation of results on tensegrity structures in sparse models of generalized forces, providing advantages for code generation for symbolic or autodifferentiation derivation tasks, and giving diagonal linear models with constant inertia matrices, allowing one not only to simplify computations and matrix inversions, but also to lower the number of elements that need to be stored when the linear model is evaluated along a trajectory.

The paper deals with one of the modern challenges in walking robotics: moving across a rough terrain where the geometry of the terrain is unknown and hence it is impossible to plan precise trajectories for the robot feet in advance, before a collision with the supporting surface occurs. In this paper, an algorithm for the dynamics correction of the foot trajectory based on the compliant control is employed to deal with the problem. Additionally, to solve the problem of dynamic correction of the foot trajectory, it also provides a biomorphic reaction force profile, which might be a desired property for some applications.