The success in real applications of PID regulator has thought us that it is desirable a simple controller with clear and intuitive policy for tuning of feedback gains, as well as minimum or null dependency on the dynamics of the system. In this paper, theoretical and experimental results of PD-like structures for tracking of robots subject to friction and disturbances is presented. The salient feature of our proposal is a neuro-fuzzy schemes used to a single gain that multiplies the regressor-free PID structure. This bounded time-varying gain plays the role of enforcing a dissipative mapping to obtain exponential stability. Stability is obtained in the sense of Lyapunov and experimental study is presented and discussed, which highlights the viability and feasibility for real applications.

Manipulation with soft-fingertips is proposed as a tool for interacting with rigid objects, based on an online bilateral teleoperation fuzzy controller, without time delay. The fuzzy inference engine tunes continuously the force feedback gain to increase awareness of physical interaction so as to grasping is achieved. In contrast to the case of rigid fingertip, wherein infinitely small contact point is assumed, it is argued that our scheme allows safe and intuitive interaction, which has proved effective in an experimental study with 19 subjects. Results indicate success due to feedback of slave contact force to the human user. Subjects consistently judge comfort and easiness of manipulation, with an experimental two-hand prototype.

In this paper, we are interested in a scheme that proves effective use of soft fingertips for human-in-the-loop telemanipulation. Motion planning is carried out by the human-in-the-loop to grasp and manipulate an object using viscoelastic soft fingertips. In addition, to grasp an object firmly, an estimated torque is synthesized based on fuzzy inference. Experimental results show that the user confidence presents small variations using soft fingertips. Then, the soft fingertips in pHRI induce a more user dexterity than rigid fingertips.

Based on recent studies which establishes that skill acquisition requires not just specification of motor skills, learning and skill application but also intervention of human expert only in certain phases, we present an approach which encode the human expert demonstration into a teacher class based on Fuzzy ArtMap network. Then, the human novice trainee produces the approximate knowledge, which is in turn coded into student class. The evaluation function introduces a class metric which simultaneously allows the student to refine motor commands to increase the trainee pace while modifies accordingly the desired trajectory of the robot. Preliminary experiments indicates a high success rate in contact robotic tasks, in a deterministic setting.

In this paper, a new hybrid active mechanical technique is proposed, which nullifies the torque coming from gravitational acceleration by means of controlled displacements of the center of mass of each link. Modeling, design and control issues are presented as well as some preliminary experimental results illustrate this principle.