In this research, first, kinematic and dynamic equations of a 4-DOF 3-link robotic finger are derived using Denavit-Hartenberg convention and Lagrange’s formulation. To model the muscles, several springs and dampers are placed between the finger links. Then, two advanced controllers, namely adaptive-robust and adaptive-neural, which can control the robotic finger in presence of parametric uncer...

An improved spccd estimator using paraiiicter uncertainty cancellation and its application to an indirect tield-oriented induction motor drive arc presented. First, the speed estimation errors of a V-l model-based adaptive speed observer due to parameter variations are analysed. Then, accordingly, a novel compensation scheme is dcvclopcd to cancel the effects of parameter variations on the esti...

We consider the question of optimally estimating a time-varying multivariate stochastic process, in order to minimize the expected squared estimation error. This is motivated by adaptive control problems under uncertainty in a changing environment. A distinguishing feature of our approach is that we do not need a completely specified model for the stochastic process under consideration. Instead...

an asymptotically stable direct adaptive fuzzy pi sliding modecontroller is proposed for a class of nonlinear uncertain systems. in contrast toother existing approaches of handling disturbances, the proposed approachdoes not require this bound to be known, only requiring that it exists.moreover, a pi control structure is used to attenuate chattering. the approachis applied to stabilize an open-...

this paper proposes a discrete-time repetitive optimal control of electrically driven robotic manipulators using an uncertainty estimator. the proposed control method can be used for performing repetitive motion, which covers many industrial applications of robotic manipulators. this kind of control law is in the class of torque-based control in which the joint torques are generated by permanen...

In this research, we propose to use the smooth adaptive estimator to estimate the process mean. Since the smooth adaptive estimator is robust for non-normal processes and outliers, it can be used in the estimator of the Process Capability Indices (PCIs). The resulting indices are called smooth adaptive PCIs, which will also be robust estimators. We simu late extensively the biases and mean squa...