In this paper, a practical field of quantum technology known as “quantum technology, and weather prediction of smart grids”, and their future have been analyzed. Smart grids are among the significant fundamentals of a smart city and its supplying sources. So, factors such as weather prediction, or demand response are among important factors and have a crucial role. Also, experts should use highly accurate models to predict these factors such as weather prediction Therefore, when they are combined with Artificial Intelligence (AI), and Neural Networks (NN), they will be more efficient. However, in the smart grids of future Smart Sustainable Cities (SSCs) that the gathered big data has enormous amount, the classical AI, and ANN models will inefficient. Accordingly, using a higher technology known as “Quantum technology”, and its practical features, and potentials, has noticeable applications in smart grids, also, weather prediction. This paper presents weather prediction of smart grids, their regular methods, and quantum technology with its usage in the ladders. Respectively, it compares Quantum Neural Networks (QNN) method with other regular techniques that are ANN-based in the area of weather prediction. Finally, it is concluded that quantum technology and Quantum Neural Networks (QNN) have the potential to be combined with other techniques which leads to accurate models with ultrafast processing, and execution speed. Accordingly, they can be used in every field, and smart grids or weather predictions are not an exception.

This paper presents a practical field of quantum technology and its contribution to smart grids, and their future in smart sustainable cities (SSCs). Smart grids are among the important basis of an SSC, so its factors such as forecasting, optimization, turbine design have a significant role. Based on quantum technology's amazing features, it has conspicuous applications in smart grids including weather prediction, or demand response forecasting. This paper represents SSCs, smart grids, and quantum technology and its use in the ladders. Then, defines the areas of smart grids in which quantum technology can make a noticeable development. Respectively, it compares quantum technology with other regular supercomputing technologies. Ultimately, quantum technology is a kind of technology that has a noticeable contribution potentiality to other technologies to make the world to be processed in a faster, and much more accurate way, and smart grids, SSCs are, also, in this future.

SUMMARY: The possibility of operating in remote environments using teleoperation systems has been considered widely in the control literature. This paper presents a review on the discrete-time teleoperation systems, including issues such as stability, passivity and time delays. Using discrete-time methods for a master-slave teleoperation system can simplify control implementation. Varieties of control schemes have been proposed for these systems and major concerns such as passivity, stability and transparency have been studied. Recently, unreliable communication networks affected by packet loss and variable transmission delays have been received much attention. Thus, it is worth considering discrete-time theories for bilateral teleoperation architectures, which are formulated on the same lines as the continuous-time systems. Despite the extensive amount of researches concerning continuous-time teleoperation systems, only a few papers have been published on the analysis and controller design for discrete bilateral forms. This paper takes into account the challenges for the discrete structure of bilateral teleoperation systems and notifies the recent contributions in this area. The effect of sampling time on the stability-transparency trade-off and the task performance is taken into consideration in this review. These studies can help to design guidelines to have better transparency and stable teleoperation systems.

In this paper, a position and velocity estimation method for robotic manipulators which are affected by constant bounded disturbances is considered. The tracking control problem is formulated as a disturbance rejection problem, with all the unknown parameters and dynamic uncertainties lumped into disturbance. Using adaptive robust extended Kalman filter(AREKF) the movement and velocity of each joint is predicted to use in discontinuous Lyapunov-based controller structure. The parameters of the error dynamics have been validated off-line by real data. Computer simulation results given for a two degree of freedom manipulator demonstrate the efficacy of the improved Kalman Filter by comparing the performance of EKF and improved AREKF. Although it is shown that accurate trajectory tracking can be achieved by using the proposed controller.