This paper is devoted to the problem of force sensorless disturbance rejection in robot manipulators. In the proposed approach, the control system uses position sensor signals and estimated values of external forces, instead of force sensor signals. The estimation process is performed via an adaptive force estimator. Then the estimated force vector is utilized to compensate for the force disturbance effect in order to achieve a better trajectory tracking performance. The force estimation is carried out directly using no environment model. Asymptotical stability of the proposed control system is analyzed by the invariant set and Lyapunov direct method establishing an appropriate theorem. Finally, the performance of the proposed control system is verified using numerical simulation.

Consideration of manipulator dynamics and external disturbances in robot control system design can enhance the stability and performance properties of the whole system. In this paper, we present an approach to solve the control problem when the inertia parameters of robot are unknown, and at the same time robot is subjected to external force disturbances. This approach is based on simultaneous estimation of force signal and inertia parameters and utilizing them in the control law. The update laws and the control law are derived based on a single time-varying Lyapunov function, so that the global convergence of the tracking error is ensured. A theorem with a detailed proof is presented to guarantee the global uniform asymptotic stability of the whole system. Some simulations are made for a number of external forces to illustrate the effectiveness of the proposed approach.

Fairness is one of the most important features of a rate allocation strategy. Proportional fairness criterion has been recently proposed by F. P. Kelly and his colleagues. In this paper, we have proposed a two-level hierarchical technique which allocates proportionally-fair rates to the network elastic users. Part of the network links which are used commonly by the end-users and are congestion prone, constitute the higher (first) level of the hierarchy. In this level, the users with common path in the network are grouped as virtual users. End-users and remaining network links constitute the lower (second) level of hierarchy. To improve the convergence rate of the algorithm, a combination of Jacobi method and fuzzy techniques is deployed in the higher level of hierarchy. Implementing such fast algorithms in the higher level (which is topologically simpler than the whole network), reduces the computational complexity with respect to the use of such algorithms in the whole network. Additionally, the lower level penalty function computation is done once in each N iterations, which reduces the computational complexity furthermore. The simulation results show that the proposed algorithm outperforms that of Kelly in the convergence speed.

The problem of designing a robust adaptive control for nonlinear systems with uncertain time-varying parameters is addressed. The upper bound of uncertain parameters, considered even in control coefficients, are not required to be known. An adaptive tracking controller is presented and, using the Lyapunov theory, the closed-loop stability and tracking error convergence is shown. In order to improve the performance of the method, a robust mechanism is incorporated into the adaptive controller yielding a robust adaptive algorithm. The proposed controller guarantees the boundedness of all closed-loop signals and robust convergence of tracking error in spite of time-varying parameter uncertainties with unknown bounds. The parametric uncertain systems under consideration describes a wide class of nonlinear circuits and systems. As an application, a novel parametric model is derived for nonlinear Chua's circuit and then, the proposed method is used for its control. The effectiveness of the method is demonstrated by some simulation results.