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In this paper, a novel dynamical model is proposed for the multi-input multi-output electrically driven robot manipulators, by an observer-based robust adaptive fuzzy controller. The proposed control scheme utilizes current control effort, which is more efficient than the torque control approach. The proposed method is very simple, accurate and robust. Based on the adaptive fuzzy system an observer-based estimator is presented that uses feedback error function as the input of fuzzy system to approximate and adaptively compensate the unknown uncertainties and external disturbance of the system under control. Although the proposed controller scheme requires the uncertainties to be bounded, it does not require this bound to be known. An H_∞ robust controller is employed to an attenuate the residual error to the desired level and recompenses the both fuzzy approximation errors and observer errors. The proposed method guarantees the stability of the closed-loop system based on the Strictly Positive Real (SPR) condition and Lyapunov theory. The proposed control scheme is not limited only for controlling of robotics vehicles, it can be applied for a class of nonlinear MIMO systems. Finally, in simulation study, to demonstrate the usefulness and effectiveness of the proposed technique, a two-link robot manipulator system is employed.

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Deriving the accurate dynamic model of robots is pivotal for robot design, control, calibration, and fault detection. To derive an accurate dynamic model of robots, all the terms affecting the robotchr('39')s dynamics are necessary to be considered, and the dynamic parameters of the robot must be identified with appropriate physical insight. In this paper, first, the kinematics of the ARAS-Diamond spherical parallel robot, which has been developed for vitreoretinal ophthalmic surgery, are investigated, then by presenting a formulation based on the principle of virtual work, a linear form of robot dynamics is derived, and the obtained results are validated in SimMechanics environment. Furthermore, other terms affecting the robot dynamics are modeled, and by using the linear regression form of the robot dynamics with the required physical bounds on the parameters, the identification process is accomplished adopting the least-squares method with appropriate physical consistency. Finally, by using the criteria of the normalized root mean squared error (NRMSE) and using different trajectories, the accuracy of the identified dynamic parameters is evaluated. The experimental validation results demonstrate a good fitness for the actuator torques (about 75 percent),  and a positive mass matrix in the entire workspace, which allows us to design the common model-based controllers such as the computer torque method, for precise control of the robot in vitreoretinal ophthalmic surgery.