Robustness margins and high performance for an adaptive flight control system with application to hypersonic vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (leaves 59-61).The design tools developed for use with linear controllers such as gain and phase margins do not apply to nonlinear control architectures such as adaptive control. For decades, flight control engineers have used these tools extensively to measure the robustness of their linear control systems and make guarantees on the performance of the closed-loop system in the presence of uncertainties. Stringent demands on performance for safety-critical flight systems, as in the case of hypersonic vehicles, make advanced control methods such as adaptive control increasingly attractive. The major obstacle in the widespread application of adaptive control to such applications is the lack of guarantees on performance and robustness. This thesis presents robustness margins, adaptive control analogs to the linear control notions of gain and phase margins, which can be used to make those guarantees. This paves the way for a systematic Verification and Validation (V&V) approach for adaptive controllers. The operation of an adaptive controller can be broken down into two distinct phases: the adaptation mode, in which the adaptive parameters are varying, and the steady-state mode, in which the adaptive parameters have converged to their steady-state values.(cont.) During the steady-state mode, the nonlinear adaptive controller converges to a linear time-invariant (LTI) system, and many tools exist for the calculation of the requisite margins. However, during the adaptation mode, which is arguably a more crucial mode of operation for the aircraft, no such tools exist. This thesis provides the tools for the numerical calculation of robustness margins during the adaptation mode. Robustness with respect to a range of uncertainties including parametric uncertainties, disturbances, time-delays, unmodeled dynamics, and actuator saturation is derived. The robustness of the adaptive controller is then demonstrated on a fully nonlinear model of a high-performance hypersonic aircraft. The importance of theoretically justified adaptive controllers is illustrated using the historical example of the NASA X-15 research airplane. NASA's three X-15 aircraft together flew nearly 200 flights, acting as test beds for many bleeding-edge technologies, including the nonlinear adaptive controller implemented on the X-15-3. The application of this controller demonstrated the advantages of adaptive control including improved performance and a shorter design cycle.(cont.) However, when the X-15-3 crashed in 1967, one of the severe disadvantages of this early adaptive control was highlighted: the lack of guaranteed stability and performance. Using modern adaptive control theory and the tools developed in this thesis, the control design of the X-15 is revisited and it is demonstrated that had the X-15 controllers been implemented today, all of the 200 flights, without a single exception, would have been performed safely, without incident.by Zachary Thompson Dydek.S.M

Adaptive control of UAS

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from PDF version of thesis.Includes bibliographical references (p. 133-139).Adaptive control is considered to be one of the key enabling technologies for future high-performance, safety-critical systems such as air-breathing hypersonic vehicles. Adaptive flight control systems offer improved performance and increased robustness to uncertainties by virtue of their ability to adjust control parameters as a function of online measurements. Extensive research in the field of adaptive control theory has enabled the design, analysis, and synthesis of stable adaptive systems. We are now entering the stage in which adaptive flight control systems have reached the requisite level of maturity for application to hardware flight platforms. Unmanned aerial systems (UAS) provide a unique opportunity for the transition of adaptive controllers from theory to practice. The small, unmanned aerial vehicles (UAVs) examined in this thesis offer a low-cost, low-risk stepping stone between simulation and application to higher-risk systems in which safety is a critical concern. Unmanned aircraft also offer several benefits over their manned counterparts including extreme persistence, maneuverability, lower weight and smaller size. Furthermore, several missions such as surveillance, exploration, search-and-track, and lifting of heavy loads are best accomplished by a UAS consisting of multiple UAVs. This thesis addresses some of the challenges involved with the application of adaptive flight control systems to UAS. Novel adaptive control architectures are developed to overcome performance limitations of UAS, the most significant of which is a large time delay due to communication and limited onboard processing. Analytical tools that allow the calculation of a theoretically justified time delay limit are also developed. These tools in turn lead to an estimate of the time-delay margin of the closed-loop system which is an essential part of the validation and verification methodology for intelligent flight control systems. These approaches are validated numerically using a series of simulation studies. These controllers and analytical methods are then applied to the UAV, demonstrating improved performance and increased robustness to time delays. Also introduced in this thesis is a novel adaptive methodology for coordinated adaptive control of a multi-vehicle UAS. Including two distinct classes of adaptive algorithms at both the local and global levels was found to result, both in simulation and in actual flight 3 tests, in decreased tracking error for individual vehicles, decreased errors in intervehicle distances, and reduced likelihood of collisions with other vehicles or obstacles in the environment.by Zachary Thompson Dydek.Ph.D