Abstract
In this paper, a real-time optimal attitude controller is designed for staring imaging, and the output command is based on future prediction. First, the mathematical model of staring imaging is established. Then, the structure of the optimal attitude controller is designed. The controller consists of a preprocessing algorithm and a neural network. Constructing the neural network requires training samples generated by optimization. The objective function in the optimization method takes the future control effect into account. The neural network is trained after sample creation to achieve real-time optimal control. Compared with the PID (proportional-integral-derivative) controller with the best combination of parameters, the neural network controller achieves better attitude pointing accuracy and pointing stability.
Highlights
Staring mode of satellites can be used to collect the dynamic information of ground targets
The performances are tested for the neural network controller and the PID controller with the optimal coefficient combination when the satellite stares at the central ground target
The neural network controller performs better than the PID controller according to the results shown in Table 8 and the error curves
Summary
Staring mode of satellites can be used to collect the dynamic information of ground targets. Basu et al [12] deployed a neural network to set initial conditions for the PSO (particle swarm optimization), which reduced the planning time of reorientation for a space telescope. Zou [15] proposed a scheme for limited time attitude tracking control of spacecraft using the terminal sliding mode and Chebyshev neural network. The neural network is trained to achieve optimal control and real-time control simultaneously. (i) In the aspect of staring imaging, a controller which considers a future period of time and makes the optimal control decision is proposed, and it performs better than the traditional controller (ii) An algorithm for compressing the satellite state information is proposed.
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