A Kalman filter(KF)-based identification, internal model-based controller for accurate tracking a specified trajectory despite the sensor errors, and fault tolerance is proposed. This study was mainly motivated by the need for precision, resolution and accuracy required in robotic applications such as robotic surgery. The computed torque approach is used to map a nonlinear model into a linear one. The sensor errors of the orientation input and the position corrupted by unknown input and output stochastic disturbance and measurement noise. Predictive analytics is used to estimate the true input by exploiting its smoothness and the randomness of the noisy input. The system is described using the Box-Jenkins(BJ) model, which is an augmented model of the true output, termed signal and the disturbance. The BJ model and the associated KF are identified without the a priori knowledge of the statistics of the disturbance and measurement noise. Using the key properties of KF the signal, the output error, the signal model, and the disturbance models, the KF associated with the signal model is accurately identified. An internal model-based state-feedback and feedforward controller is designed to accurately track the desired trajectory. The hardware sensors are replaced by KF-based sensors. The KF ensures fault tolerance. The proposed scheme was successfully evaluated on a physical robot.