How scientist developed an intelligent fuzzy logical control to stabilize solar sail?

Solar sail, a form of longevous spacecraft without propellant demand, attracts numerous aerospace researchers' attention. Its prolongable peculiarity enables its tremendous potential in diverse interplanetary missions. Due to the harsh space environment, it is inevitable that the spacecraft with long time on-orbit suffers the performance degradation and accident. Especially, the force model will be variational and make attitude stabilization of solar sail failed.

The remote distance between earth and solar sail also brings difficulties to the solar sail working in abominable space environment. All of the above problems require solar sail to possess the self-adaptive and unmanned capacity. As an intelligent, robust, and fault-tolerant control algorithm, the fuzzy logical controller (FLC) can deal with the control of nonlinear, random, and complicated systems in real scenarios.

Thus, researches on the application of FLC in solar sail attitude control have been developed. However, the existing methods usually design FLC by learning a large number of existing FLCs or modeling the controlled system. The former still needs unacceptable manual workload, while the latter is not practical in attitude control.

Moreover, the automatic design method was hardly used in the design of time-varying FLC, which means the design of FLC without a priori knowledge or under time-varying situation is unpractical. In a research paper recently published in Space: Science and Technology, Ming Xu from Beihang University, considering the lack of a priori knowledge and the unacceptable manual workload in the design of the fuzzy logical controller (FLC), developed an intelligent FLC designer (IFLCD) by introducing neural network modelling and automatic design method.

First of all, a novel solar sail, SSICE was introduced. Its dynamic decomposition simplified the design of FLC. Four components structurally constitute SSICE: Component A is the spacecraft kernel consisting of effective loads and security devices for loads. Component B is an inflatable frame that links Component A and Component C. Component C is an individual control unit that contains a motor and structure for connection, which is rigidly fixed with the corresponding Component D. Component D is the blade that can reflect solar radiation for generating solar radiation pressure (SRP) to drive SSICE.

A pair of Component C and the connected Component D between them composes an individually controllable element, and Component D can be driven by rotating the motors. Because of the decentralized layout and three-axis control torque, SSICE possessed the potentials of self-adaptation and attitude control without fuel. Besides, the dynamical model of SSICE is proposed. With intermediate control variables, solar radiation pressure (SRP) torque, the dynamics of SSICE can be divided into invariable and variational parts.

Then, the author demonstrated the general scheme of IFLCD. The function of IFLCD was to produce FLC with given design parameters by utilizing the fuzzy rules and a priori knowledge source. By executing numerous zero-order oracles of variational part, enough data can be used to train a neural network as an a priori knowledge source. The priori knowledge would provide the relationship among control inputs, environment variables, and intermediate control variables. On the other hand, fuzzy rules are used to obtain the intermediate control variables and make target variables approach zero, where the type of fuzzy rules is selected as bang-bang control. Moreover, the stability of fuzzy rules was demonstrated.

Besides, a self-adaption factor was employed in FLC design for better control accuracy and an updating approach was employed by introducing the method of automatic design, which can update the a priori knowledge source when the real system had changed. In summary, the decomposition of controlled system and the introduction of automatic design method enabled FLC to solve the black-box and time-varying control problem automatically, where a mathematical model of the solar sail is not necessary.

Finally, the author applied the proposed IFLCD in the attitude stabilization simulation of SSICE. The working environment of SSICE was set at the Sun-Earth L4 point, in which the solar radiation power was 1370W/m2. The stabilization results revealed the feasibility and effectiveness of IFLCD in attitude stabilization under both invariable and time-varying scenarios.

Besides, the comparisons between IFLCD and existed researches revealed that IFLCD possessed a much higher control accuracy with a bit more time consumption, which was still satisfied under time-varying situation. In conclusion, the author offered an intelligent algorithm without any mathematical model, which was especially suitable for unmanned, complicated, and time-varying systems, and also overcame the difficulties in FLC design. Particularly, IFLCD can be used for other spacecraft control process, which can increase the reliability of the unmanned space missions.

Research Report:Intelligent Fuzzy Control in Stabilizing Solar Sail with Individually Controllable Elements

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RIT receives NASA funding to develop new diffractive solar sail concepts
Rochester NY (SPX) Jul 08, 2022
NASA announced new funding for a project led by Rochester Institute of Technology alumni, faculty, and students that could power spacecraft to orbit the sun's poles for the first time. The NASA Innovative Advanced Concepts (NIAC) program will provide Phase III funding to the Diffractive Solar Sailing project led by Amber Dubill '20 (mechanical engineering), '20 MS (mechanical engineering) of the Johns Hopkins University Applied Physics Laboratory. The feasibility of the concept was previously stud ... read more