The uncertainty disturbance is one of the main disturbances that seriously influences the stabilization precision of an aerial inertially stabilized platform (ISP) system. In this paper, to improve the stabilization precision of the ISP under disturbance uncertainty, a robust H8 controller is designed in this paper. Then, the reduction order is carried out for high-order controllers generated by the robust H8 loop shaping control method. The application of the minimum implementation and balanced truncation algorithm in controller reduction is discussed. First, the principle of reduced order of minimum implementation and balanced truncation are analyzed. Then, the method is used to reduce the order of the high-order robust H8 loop shaping controller. Finally, the method is analyzed and verified by the simulations and experiments. The results show that by the reduced-order method of minimum implementation and balanced truncation, the stabilization precision of the robust H8 loop shaping controller is increased by about 10%.

The uncertainty disturbance is one of the main disturbances that seriously influences the stabilization precision of an aerial inertially stabilized platform (ISP) system. In this paper, to improve the stabilization precision of the ISP under disturbance uncertainty, a robust H8 controller is designed in this paper. Then, the reduction order is carried out for high-order controllers generated by the robust H8 loop shaping control method. The application of the minimum implementation and balanced truncation algorithm in controller reduction is discussed. First, the principle of reduced order of minimum implementation and balanced truncation are analyzed. Then, the method is used to reduce the order of the high-order robust H8 loop shaping controller. Finally, the method is analyzed and verified by the simulations and experiments. The results show that by the reduced-order method of minimum implementation and balanced truncation, the stabilization precision of the robust H8 loop shaping controller is increased by about 10%.

The Wireless Sensor Networks (WSNs) are widely utilized in various industrial and environmental monitoring applications. The process of data gathering within the WSN is significant in terms of reporting the environmental data. However, it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage. Therefore, the collected data may become inaccurate or incomplete. Focusing on the spatiotemporal correlation among sensor nodes, this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes. The Long-Short-Term-Memory Recurrent Neural Network (LSTM RNN) helps to more accurately derive the time-series data corresponding to the sets of past collected data, making the prediction results more reliable. It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.