Adaptive Augmentation of Non-Minimum Phase Flexible Aerospace Systems

This work demonstrates the efficacy of direct adaptive augmentation on a robotic flexible system as an analogue of a large flexible aerospace structure such as a launch vehicle or aircraft. To that end, a robot was constructed as a control system testbed. This robot, named “Penny,” contains the command and data acquisition capabilities necessary to influence and record system state data, including the flex states of its flexible structures. This robot was tested in two configurations, one with a vertically cantilevered flexible beam, and one with a flexible inverted pendulum (a flexible cart-pole system). The physical system was then characterized so that linear analysis and control design could be performed. These characterizations resulted in linear and nonlinear models developed for each testing configuration. The linear models were used to design linear controllers to regulate the nominal plant’s dynamical states. These controllers were then augmented with direct adaptive output regulation and disturbance accommodation. To accomplish this, sensor blending was used to shape the output such that the nonminimum phase open loop plant appears to be minimum phase to the controller. It was subsequently shown that augmenting linear controllers with direct adaptive output regulation and disturbance accommodation was effective in enhancing system performance and mitigating oscillation in the flexible structures through the system’s own actuation effort

Progressive learning and its application to robotic assembly

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1995.Includes bibliographical references (leaves 89-93).by Boo-Ho Yang.Ph.D

Robust Sampled-Data Adaptive Control of the Rohrs Counterexamples

Abstract-We revisit the Rohrs counterexamples within the context of sampled-data adaptive control. In particular, retrospective cost adaptive control (RCAC) is applied to the sampled continuous-time plant with unmodeled high-frequency dynamics, which involves nonminimum-phase (NMP) sampling zeros. It is shown that, without knowledge of these NMP zeros, RCAC stabilizes the uncertain plant and asymptotically follows the sinusoidal command

Robust, Practical Adaptive Control for Launch Vehicles

A modern mechanization of a classical adaptive control concept is presented with an application to launch vehicle attitude control systems. Due to a rigorous flight certification environment, many adaptive control concepts are infeasible when applied to high-risk aerospace systems; methods of stability analysis are either intractable for high complexity models or cannot be reconciled in light of classical requirements. Furthermore, many adaptive techniques appearing in the literature are not suitable for application to conditionally stable systems with complex flexible-body dynamics, as is often the case with launch vehicles. The present technique is a multiplicative forward loop gain adaptive law similar to that used for the NASA X-15 flight research vehicle. In digital implementation with several novel features, it is well-suited to application on aerodynamically unstable launch vehicles with thrust vector control via augmentation of the baseline attitude/attitude-rate feedback control scheme. The approach is compatible with standard design features of autopilots for launch vehicles, including phase stabilization of lateral bending and slosh via linear filters. In addition, the method of assessing flight control stability via classical gain and phase margins is not affected under reasonable assumptions. The algorithm s ability to recover from certain unstable operating regimes can in fact be understood in terms of frequency-domain criteria. Finally, simulation results are presented that confirm the ability of the algorithm to improve performance and robustness in realistic failure scenarios

Direct Adaptive Control for Stability and Command Augmentation System of an Air-Breathing Hypersonic Vehicle

In this paper we explore a Direct Adaptive Control scheme for stabilizing a non-linear, physics based model of the longitudinal dynamics for an air breathing hypersonic vehicle. The model, derived from first principles, captures the complex interactions between the propulsion system, aerodynamics, and structural dynamics. The linearized aircraft dynamics show unstable and non-minimum phase behavior. It also shows a strong short period coupling with the fuselage-bending mode. The value added by direct adaptive control and the theoretical requirements for stable convergent operation is displayed. One of the main benefits of the Directive Adaptive Control is that it can be implemented knowing very little detail about the plant. The implementation uses only measured output feedback to accomplish the adaptation. A stability analysis is conducted on the linearized plant to understand the complex aero-propulsion and structural interactions. The multivariable system possesses certain characteristics beneficial to the adaptive control scheme; we discuss these advantages and ideas for future work

Adaptive control of plants with input saturation: an approach for performance improvement

In this work, a new method for adaptive control of plants with input saturation is presented. The new anti-windup scheme can be shown to result in bounded closed-loop states under certain conditions on the plant and the initial closed-loop states. As an improvement in comparison to existing methods in adaptive control, a new degree of freedom is introduced in the control scheme. It allows to improve the closed-loop response when actually encountering input saturation without changing the closed-loop performance for unconstrained inputs.Diese Arbeit präsentiert eine neue Methode für die adaptive Regelung von Strecken mit Stellgrößenbegrenzung. Für das neue anti-windup Verfahren wird gezeigt, dass die Zustände des Regelkreises begrenzt bleiben, wenn dessen initiale Werte und die Regelstrecke bestimmte Bedingungen erfüllen. Eine Verbesserung im Vergleich zu existierenden Methoden wird durch die Einführung eines zusätzlichen Freiheitsgrades erzielt. Dieser erlaubt die Verbesserung der Regelgüte des geschlossenen Regelkreises, wenn das Eingangssignal sich in der Limitierung befindet, ohne diese sonst zu verändern

Design of stable adaptive fuzzy control.

by John Tak Kuen Koo.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 217-[220]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- "Robust, Adaptive and Fuzzy Control" --- p.2Chapter 1.3 --- Adaptive Fuzzy Control --- p.4Chapter 1.4 --- Object of Study --- p.10Chapter 1.5 --- Scope of the Thesis --- p.13Chapter 2 --- Background on Adaptive Control and Fuzzy Logic Control --- p.17Chapter 2.1 --- Adaptive control --- p.17Chapter 2.1.1 --- Model reference adaptive systems --- p.20Chapter 2.1.2 --- MIT Rule --- p.23Chapter 2.1.3 --- Model Reference Adaptive Control (MRAC) --- p.24Chapter 2.2 --- Fuzzy Logic Control --- p.33Chapter 2.2.1 --- Fuzzy sets and logic --- p.33Chapter 2.2.2 --- Fuzzy Relation --- p.40Chapter 2.2.3 --- Inference Mechanisms --- p.43Chapter 2.2.4 --- Defuzzification --- p.49Chapter 3 --- Explicit Form of a Class of Fuzzy Logic Controllers --- p.51Chapter 3.1 --- Introduction --- p.51Chapter 3.2 --- Construction of a class of fuzzy controller --- p.53Chapter 3.3 --- Explicit form of the fuzzy controller --- p.57Chapter 3.4 --- Design criteria on the fuzzy controller --- p.65Chapter 3.5 --- B-Spline fuzzy controller --- p.68Chapter 4 --- Model Reference Adaptive Fuzzy Control (MRAFC) --- p.73Chapter 4.1 --- Introduction --- p.73Chapter 4.2 --- "Fuzzy Controller, Plant and Reference Model" --- p.75Chapter 4.3 --- Derivation of the MRAFC adaptive laws --- p.79Chapter 4.4 --- "Extension to the Multi-Input, Multi-Output Case" --- p.84Chapter 4.5 --- Simulation --- p.90Chapter 5 --- MRAFC on a Class of Nonlinear Systems: Type I --- p.97Chapter 5.1 --- Introduction --- p.98Chapter 5.2 --- Choice of Controller --- p.99Chapter 5.3 --- Derivation of the MRAFC adaptive laws --- p.102Chapter 5.4 --- Example: Stabilization of a pendulum --- p.109Chapter 6 --- MRAFC on a Class of Nonlinear Systems: Type II --- p.112Chapter 6.1 --- Introduction --- p.113Chapter 6.2 --- Fuzzy System as Function Approximator --- p.114Chapter 6.3 --- Construction of MRAFC for the nonlinear systems --- p.118Chapter 6.4 --- Input-Output Linearization --- p.130Chapter 6.5 --- MRAFC with Input-Output Linearization --- p.132Chapter 6.6 --- Example --- p.136Chapter 7 --- Analysis of MRAFC System --- p.140Chapter 7.1 --- Averaging technique --- p.140Chapter 7.2 --- Parameter convergence --- p.143Chapter 7.3 --- Robustness --- p.152Chapter 7.4 --- Simulation --- p.157Chapter 8 --- Application of MRAFC scheme on Manipulator Control --- p.166Chapter 8.1 --- Introduction --- p.166Chapter 8.2 --- Robot Manipulator Control --- p.170Chapter 8.3 --- MRAFC on Robot Manipulator Control --- p.173Chapter 8.3.1 --- Part A: Nonlinear-function feedback fuzzy controller --- p.174Chapter 8.3.2 --- Part B: State-feedback fuzzy controller --- p.182Chapter 8.4 --- Simulation --- p.186Chapter 9 --- Conclusion --- p.199Chapter A --- Implementation of MRAFC Scheme with Practical Issues --- p.203Chapter A.1 --- Rule Generation by MRAFC scheme --- p.203Chapter A.2 --- Implementation Considerations --- p.211Chapter A.3 --- MRAFC System Design Procedure --- p.215Bibliography --- p.21

Progressive learning of endpoint feedback systems with model uncertainty and sensor noise

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.Includes bibliographical references (leaves 141-146).by Shih-Hung Li.Ph.D