Adaptive Control for a Class of Nonlinear, Time Varying Rotational Systems

Aerospace systems often exhibit nonlinear, time varying dynamics. Expansive mission profiles, fuel burn or transfer and payload deployments or slung loads can supply additional complexity that can excite the plant dynamics and result in undesirable performance. Because of these often unknown dynamical effects in aerospace systems, an emphasis is placed on controller robustness as significant safety risks are present. Due to the difficulty of predicting uncertainties, robust control can be achieved in two distinct ways. The first approach is to design a control law to be tolerant to a large amount of uncertainty, and the second is to design a control law that can adapt to changing system parameters. It is well known that adaptive controllers can respond to external disturbances and uncertain or nonlinear dynamics. This dissertation investigates the development of adaptive control laws to stabilize and control a class of nonlinear, time varying systems. A direct model reference adaptive control architecture, which includes actuator hedging in the reference model to address actuator bandwidth limitations and uncertainty, is designed and implemented to compensate for dynamical effects that could, for example, be caused by a slung load suspended from a quadrotor. A variety of systems that belong to the same class are presented including a rotating tank system with fluid slosh, and quadrotors with slung loads or actuator failures. A direct model reference adaptive controller complete with a PID or LQR controlled reference model is implemented in each case to enable the system to track attitude trajectories generated by a reference model. Modifications to a baseline direct model reference adaptive controller are applied to attenuate the effects of measurement noise and actuator dynamics. The stability of this control law is investigated via Lyapunov analysis. Simulation results are provided showcasing overall controller performance of attitude control in the presence of both internal and external disturbances, measurement noise, actuator dynamics and actuator failure

An Efficient Resilient MPC Scheme via Constraint Tightening against Cyberattacks: Application to Vehicle Cruise Control

We propose a novel framework for designing a resilient Model Predictive Control (MPC) targeting uncertain linear systems under cyber attack. Assuming a periodic attack scenario, we model the system under Denial of Service (DoS) attack, also with measurement noise, as an uncertain linear system with parametric and additive uncertainty. To detect anomalies, we employ a Kalman filter-based approach. Then, through our observations of the intensity of the launched attack, we determine a range of possible values for the system matrices, as well as establish bounds of the additive uncertainty for the equivalent uncertain system. Leveraging a recent constraint tightening robust MPC method, we present an optimization-based resilient algorithm. Accordingly, we compute the uncertainty bounds and corresponding constraints offline for various attack magnitudes. Then, this data can be used efficiently in the MPC computations online. We demonstrate the effectiveness of the developed framework on the Adaptive Cruise Control (ACC) problem.Comment: To Appear in ICINCO 202

Robust Estimation of Optical Phase Varying as a Continuous Resonant Process

It is well-known that adaptive homodyne estimation of continuously varying optical phase provides superior accuracy in the phase estimate as compared to adaptive or non-adaptive static estimation. However, most phase estimation schemes rely on precise knowledge of the underlying parameters of the system under measurement, and performance deteriorates significantly with changes in these parameters; hence it is desired to develop robust estimation techniques immune to such uncertainties. In related works, we have already shown how adaptive homodyne estimation can be made robust to uncertainty in an underlying parameter of the phase varying as a simplistic Ornstein-Uhlenbeck stochastic noise process. Here, we demonstrate robust phase estimation for a more complicated resonant noise process using a guaranteed cost robust filter.Comment: 5 pages, 10 figures, Proceedings of the 2013 Multi-Conference on Systems and Contro

Adaptive sliding mode observers in uncertain chaotic cryptosystems with a relaxed matching condition

We study the performance of adaptive sliding mode observers in chaotic synchronization and communication in the presence of uncertainties. The proposed robust adaptive observer-based synchronization is used for cryptography based on chaotic masking modulation (CM). Uncertainties are intentionally injected into the chaotic dynamical system to achieve higher security and we use robust sliding mode observer design methods for the uncertain nonlinear dynamics. In addition, a relaxed matching condition is introduced to realize the robust observer design. Finally, a Lorenz system is employed as an illustrative example to demonstrate the effectiveness and feasibility of the proposed cryptosyste

Improving Transient Performance of Adaptive Control Architectures using Frequency-Limited System Error Dynamics

We develop an adaptive control architecture to achieve stabilization and command following of uncertain dynamical systems with improved transient performance. Our framework consists of a new reference system and an adaptive controller. The proposed reference system captures a desired closed-loop dynamical system behavior modified by a mismatch term representing the high-frequency content between the uncertain dynamical system and this reference system, i.e., the system error. In particular, this mismatch term allows to limit the frequency content of the system error dynamics, which is used to drive the adaptive controller. It is shown that this key feature of our framework yields fast adaptation with- out incurring high-frequency oscillations in the transient performance. We further show the effects of design parameters on the system performance, analyze closeness of the uncertain dynamical system to the unmodified (ideal) reference system, discuss robustness of the proposed approach with respect to time-varying uncertainties and disturbances, and make connections to gradient minimization and classical control theory.Comment: 27 pages, 7 figure

A Stability Analysis for the Acceleration-based Robust Position Control of Robot Manipulators via Disturbance Observer

This paper proposes a new nonlinear stability analysis for the acceleration-based robust position control of robot manipulators by using Disturbance Observer (DOb). It is shown that if the nominal inertia matrix is properly tuned in the design of DOb, then the position error asymptotically goes to zero in regulation control and is uniformly ultimately bounded in trajectory tracking control. As the bandwidth of DOb and the nominal inertia matrix are increased, the bound of error shrinks, i.e., the robust stability and performance of the position control system are improved. However, neither the bandwidth of DOb nor the nominal inertia matrix can be freely increased due to practical design constraints, e.g., the robust position controller becomes more noise sensitive when they are increased. The proposed stability analysis provides insights regarding the dynamic behavior of DOb-based robust motion control systems. It is theoretically and experimentally proved that non-diagonal elements of the nominal inertia matrix are useful to improve the stability and adjust the trade-off between the robustness and noise sensitivity. The validity of the proposal is verified by simulation and experimental results.Comment: 9 pages, 9 figures, Journa