Control of robotic systems is an interesting subject due to their wide spectrum applications in medicine, aerospace and other industries. This paper proposes a novel continuous control mechanism for tracking problem of a 5-DOF upper-limb exoskeleton robot. The proposed method is a combination of a recently developed robust integral of the sign of the error (RISE) feedback and neural network (NN) feed-forward terms. The feed-forward NN learns nonlinear dynamics of the system and compensates for uncertainties while the NN approximation error and nonlinear bounded disturbances are overcome by the RISE term. Typical NN-based controllers generally result in uniformly ultimately bounded (UUB) stability due to the NN reconstruction error. In this paper to eliminate this error and achieve asymptotic tracking, the RISE feedback term is integrated into the NN compensator. Finally, a comparative study on the system performance is conducted between the proposed control strategy and two other conventional control methods. Simulation results illustrate the effectiveness of the proposed method. Manuscript profile

In this paper, a Robust Integral of the Sign Error (RISE) feedback controller is designed for a Rigid-Link Electrically Driven (RLED) robot manipulator actuated by direct current DC motor in presence of parametric uncertainties and additive disturbances. RISE feedback with implicitly learning capability is a continuous control method based on the Lyapunov stability analysis to compensate an additive bounded disturbance and linear in the parametric (LP) and non-linear in parametric (non-LP) uncertain dynamics through the use of a sufficiently large gain multiplied by an integral signum term. A proper selection of controller gains in predefined permitted areas for gains leads to reduce convergence time, control effort and improve the performance. The Bees Algorithm that is a search procedure inspired by the foraging behaviour of honey bees is used to tune the parameters of the controller to achieve the convergence. The performance of the proposed controller is compared with a PD and neural network based controllers. Simulation results of a two Rigid-Link Electrically-Driven Robot Manipulator show the advantages of the proposed controller in terms of the transient and steady state performances in comparison with some conventional controllers. Manuscript profile