Mapping Soft Computing Techniques with Operations Requirements

Abstract

Space operation tasks can be significantly improved and optimised if they are provided with valuable supporting tools such as diagnostic systems, expert sys tems, smart monitoring and alarming tools or data-driven forecasting systems. This work describes three projects where soft computing techniques such as genetic algorithms, fuzzy logic and artificial neural networks have been used to provide the desired features. The obtained results are very promising and therefore the use of soft computing techniques is highly recommended for future projects. INTRODUCTION Some space operations tasks require advanced features that are difficult to provide using traditional methodologies. These advance methodologies include among others: • Find feasible solutions of a highly constrained problem. Example: unload bias manoeuvre in three-wheels stabilized spacecraft with many attitude changes. It is difficult to find a solution that does not violate the saturation, stiction, stability or feasibility constraints. • Optimisation techniques can help to find a good solution in fixed time. Example: optimise the fuel spent in the unload bias manoeuvre taking into account the next orbit plans and constraints before the next orbit starts. • Monitoring: a method that combines several telemetry parameters and provides gradual alarms is very convenient as decision support tool. Example: monitor the spacecraft’s gyroscope fault detection; once a problem is detected a gradual alarm is fired. The engineers have time to react. • Diagnosis: the diagnosis goes beyond the monitoring. It consists of knowing the cause of an unexpected behaviour. Example: explain why an alarm has been triggered. • Data-Driven Modelling: it only takes into account the real behaviour of the spacecraft in space. By this way assumptions that are usually done when building physical models are avoided. The resulting model can be used to apply “what-if” analysis. Example: build a virtual thermal sensor to recover a failed one using only spacecraft data. SOFT COMPUTING TECHNIQUES Soft computing techniques encompass: • Genetic Algorithms: robust search algorithms that do not require knowledge of the objective function to be optimised and can search through large spaces quickly. They were derived from processes of molecular biology and the evolution of life. Their operators – crossover, mutation, and reproduction – match the synonymous biological processes. Instead of DNA, they usually process strings of symbols of finite length; these symbols encode the parameters to be optimised. • Fuzzy Logic: it provides an approach to approximate reasoning in which the rules of inference are approximate rather than exact. Fuzzy logic is useful in manipulating information that is incomplete, imprecise, or unreliable. Also called fuzzy set theory, fuzzy logic extends the simple Boolean operators, can express implication, and is used extensively in Artificial Intelligence (AI) programs. • Artificial Neural Networks: are parallel computation models made of adaptative processing units called neurons. These networks are parallel implementations of non-linear systems. Their major characteristic is the ability to learn by examples. This ability is very useful when we do not know how a complex system works but we have some data (examples) of its behaviour. Another interesting characteristic is its intrinsic parallelism that allows obtaining solutions very fast when implemented in hardware.