Design and Validation of Aerospace Control Systems: an Overview of Methods & Tools

Since their early development in the 1970’s with the introduction of fly-by-wire technology, control systems have considerably evolved. Thanks to powerful on-board computers whose capacities have undergone an exponential growth over the past thirty years, together with the development of enhanced sensors and actuators, the complexity of aerospace control systems is almost no longer bounded today. This is true at least from a technological viewpoint. Control engineers should, however, keep in mind that there are many risks in developing unnecessarily complex systems whose validation will become a real issue. In this world, where technological constraints have been considerably relaxed and where autonomous systems have become a universal Holy Grail, a good balance must be found between the design and validation phases in the development of control systems. Some complexity is inevitable during the design phase to cope with that of the plant itself, as well as with the required level of autonomy. However, complexity must be controlled, so that the validation phase remains as quick and cheap as possible.

In this general context of rapidly growing complexity, the development of efficient control design and analysis tools has become a critical issue. Here again, the exponential growth in computing capacity has played a key role and contributed to a rapid development of many fields in control theory. As a result, if one focuses at least on the linear control framework and its numerous extensions (such as robust control theory, parameter-varying control and adaptive control, to cite a few), a high level of maturity is now reached.

However, the gap between theory and practice remains to be filled. This is the main focus of this thirteenth issue of Aerospace Lab, which is dedicated to the most recent techniques for the design and validation of Aerospace Control Systems, with a particular emphasis on Matlab-oriented tools and toolboxes together with realistic applications. This issue is also strongly connected to the SMAC (Systems Modeling Analysis & Control) toolbox developed by ONERA.