Vehicle modeling, simulation development, and preliminary control law synthesis
GN&C Design and Analysis Branch, NASA Johnson Space Center
1 January – 31 May 2006
Total award $20,840
The next generation of vehicles that will take humans to the moon or Mars must be much more reliable and safer than both the manned (Space Shuttle Orbiter) and unmanned (e.g, Cassini, Mars Rover) vehicles that are currently being used. Fault tolerant control systems that autonomously adapt and safely and predictably recover from various equipment and system failures will be absolutely necessary. Because the science missions will be more demanding, and the planetary operating environments more extreme and largely unknown in composition and terrain, this newer generation of vehicles will need advanced control systems capable of handling large environmental uncertainties. For example, a vehicle that must land on Mars needs a control system that can cope with uncertainties in atmospheric parameters, such as density and pressure. Additionally, the Mars terrain is composed of different types of soil and rocks which will make landing very difficult. Several hazard avoidance systems are being researched now, and it is very important to have a control system that can be integrated with such algorithms so that it can adapt its parameters to maintain the system stable at all times. One of the techniques currently used to design controllers for nonlinear time-varying systems, such as the one for a Mars Lander is traditional gain-scheduling. This method requires extensive modeling, design, and analysis since the designer picks a finite number of points and designs a different control law for each of these operating conditions. An example of this is the flight control system of the Space Shuttle Orbiter. During the vehicle’s reentry phase, the control system dictates whether to use reaction control system (RCS) jets or aerodynamic control surfaces to generate the necessary torque to follow the given trajectory. When the vehicle is flying at high altitudes, the atmosphere is very thin and the aerodynamic surfaces are not effective; when the vehicle is lower in the atmosphere, the aerodynamic surfaces are very effective and there is no need to consume more fuel by firing the RCS jets. However, this approach could not be used in a Mars Lander entry vehicle because it requires very accurate atmospheric models and vehicle models.
The broad objective of this research is to conduct the theory-computation-experiment cycle for a Mars Lander adaptive control system to support the design of advanced missions and systems for the human exploration of space. Specifically, during the theory and algorithm development stage of this research, we will investigate ways to apply intelligent control techniques such as neural networks and reinforcement learning to adaptive control systems. This will enable the handling of time-varying parameters and environmental disturbances, while also being applicable to the control of nonlinear systems. It is important to validate and test out theory using both numerical simulation and hardware. Work performed during Summer 2006 as part of a Summer Graduate Internship at NASA Johnson Space Center will use the planetary landers simulation and hardware demonstrator systems to test out the advanced adaptive controllers.
Specific tasks and research objectives:
- Develop Linear and Nonlinear Vehicle Models
- Develop Matlab/Simulink Simulation
- Define and Characterize Atmospheric Uncertainties
- Synthesize Baseline Adaptive Controller
- Documentation of Results
Working with me on this program is Graduate Research Assistant:
- Carolina Restrepo