Verifiable Adaptive Control with Analytical Stability Margins by Optimal Control Modification

This paper presents a verifiable model-reference adaptive control method based on an optimal control formulation for linear uncertain systems. A predictor model is formulated to enable a parameter estimation of the system parametric uncertainty. The adaptation is based on both the tracking error and predictor error. Using a singular perturbation argument, it can be shown that the closed-loop system tends to a linear time invariant model asymptotically under an assumption of fast adaptation. A stability margin analysis is given to estimate a lower bound of the time delay margin using a matrix measure method. Using this analytical method, the free design parameter n of the optimal control modification adaptive law can be determined to meet a specification of stability margin for verification purposes

Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges

In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed

Indirect Model Reference Adaptive Control with Online Parameter Estimation

Over the years, parameter estimation has focused on approaches in both the time and frequency domains. The parameter estimation process is particularly important for aerospace vehicles that have considerable uncertainty in the model parameters, as might be the case with unmanned aerial vehicles (UAVs). This thesis investigates the use of an Indirect Model Reference Adaptive Controller (MRAC) to provide online, adaptive estimates of uncertain aerodynamic coefficients, which are in turn used in the MRAC to enable an aircraft to track reference trajectories. The performance of the adaptive parameter estimator is compared to that of the Extended Kalman Filter (EKF), a classical time-domain approach. The algorithms will be implemented on simulation models of a general aviation aircraft, which would be representative of the dynamics of a medium-scale fixed-wing UAV. The relative performance of the parameter estimation algorithms within an adaptive control framework is assessed in terms of parameter estimation error and tracking error under various conditions. It was found that limitations exist with the adaptive update laws in terms of number of parameters estimated within the Indirect MRAC system. The Indirect MRAC-EKF was determined to be a viable option to estimate multiple parameters simultaneously

Research on optimal control, stabilization and computational algorithms for aerospace applications

The research carried out in the areas of optimal control and estimation theory and its applications under this grant is reviewed. A listing of the 257 publications that document the research results is presented

Adaptive Control of a Generic Hypersonic Vehicle

This paper presents an adaptive augmented, gain-scheduled baseline LQR-PI controller applied to the Road Runner six-degree-of-freedom generic hypersonic vehicle model. Uncertainty in control effectiveness, longitudinal center of gravity location, and aerodynamic coefficients are introduced in the model, as well as sensor bias and noise, and input time delays. The performance of the baseline controller is compared to the same design augmented with one of two different model-reference adaptive controllers: a classical open- loop reference model design, and modified closed-loop reference model design. Both adaptive controllers show improved command tracking and stability over the baseline controller when subject to these uncertainties. The closed-loop reference model controller offers the best performance, tolerating a reduced control effectiveness of 50%, rearward center of gravity shift of up to -1.6 feet (11% of vehicle length), aerodynamic coefficient uncertainty scaled 4× the nominal value, and sensor bias of up to +3.2 degrees on sideslip angle measurement. The closed-loop reference model adaptive controller maintains at least 70% of the delay margin provided by the robust baseline design when subject to varying levels of uncertainty, tolerating input time delays of between 15-41 ms during 3 degree angle of attack doublet, and 80 degree roll step commands.Approved for Public Release; Distribution Unlimited. Case Number 88ABW-2013-3392

Adaptive Control Based on Retrospective Cost Optimization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83558/1/AIAA-46741-507.pd

Robust adaptive flight control systems in the presence of time delay

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 161-165).Adaptive control technology is a promising candidate to deliver high performance in aircraft systems in the presence of uncertainties. Currently, there is a lack of robustness guarantees against time delay with the difficulty arising from the fact that the underlying problem is nonlinear and time varying. Existing results for this problem have been quite limited, with most results either being local or at best, semi-global. In this thesis, robust adaptive control for a class of plants with global boundedness in the presence of time-delay is established. This class of plants pertains to linear systems whose states are accessible. The global boundedness is accomplished using a standard adaptive control law with a projection algorithm for a range of non-zero delays. The upper bound of such delays, i.e. the delay margin, is explicitly computed. The results of this thesis provide a highly desirable fundamental property of adaptive control, robustness to time-delays, a necessary step towards developing theoretically verifiable flight control systems.by Megumi Matsutani.Ph.D

Cooperative control of multi-uavs under communication constraints.

This research aims to develop an analysis and control methodology for the multiple un-manned aerial vehicles (UAVs), connected over a communication network. The wireless communication network between the UAVs is vulnerable to errors and time delays, which may lead to performance degradation or even instability. Analysis on the effects of the potential communication constraints in the multiple UAV control is a critical issue for successful operation of multiple UAVs. Therefore, this thesis proposes a systematic method by incorporating three steps: proposing the analysis method and metrics considering the wireless communication dynamics, designing the structure of the cooperative controller for UAVs, and applying the analysis method to the proposed control in representative applications. For simplicity and general insights on the effect of communication topology, a net-worked system is first analysed without considering the agent or communication dynamics. The network theory specifies important characteristics such as robustness, effectiveness, and synchronisability with respect to the network topology. This research not only reveals the trade-off relationship among the network properties, but also proposes a multi-objective optimisation (MOO) method to find the optimal network topology considering these trade-offs. Extending the analysis to the networked control system with agent and communication dynamics, the effect of the network topology with respect to system dynamics and time delays should be considered. To this end, the effect of communication dynamics is then analysed in the perspective of robustness and performance of the controller. The key philosophy behind this analysis is to approximate the networked control system as a transfer function, and to apply the concepts such as stability margin and sensitivity function in the control theory. Through the analysis, it is shown that the information sharing between the agents to determine their control input deteriorates the robustness of their stability against system uncertainties. In order to compensate the robustness and cancel out the effect of uncertainties, this thesis also develops two different adaptive control methods. The proposed adaptive control methods in this research aim to cope with unmatched uncertainty and time-varying parameter uncertainty, respectively. The effect of unmatched uncertainty is reduced on the nominal performance of the controller, using the parameter-robust linear quadratic Gaussian method and adaptive term. On the other hand, time-varying parameter uncertainty is estimated without requiring the persistent excitation using concurrent learning with the directional forgetting algorithm. The stability of the tracking and parameter estimation error is proved using Lyapunov analysis. The proposed analysis method and control design are demonstrated in two application examples of a formation control problem without any physical interconnection between the agents, and an interconnected slung-load transportation system. The performance of the proposed controllers and the effect of topology and delay on the system performance are evaluated either analytically or numerically.PhD in Aerospac

Relaxing Fundamental Assumptions in Iterative Learning Control

Iterative learning control (ILC) is perhaps best decribed as an open loop feedforward control technique where the feedforward signal is learned through repetition of a single task. As the name suggests, given a dynamic system operating on a finite time horizon with the same desired trajectory, ILC aims to iteratively construct the inverse image (or its approximation) of the desired trajectory to improve transient tracking. In the literature, ILC is often interpreted as feedback control in the iteration domain due to the fact that learning controllers use information from past trials to drive the tracking error towards zero. However, despite the significant body of literature and powerful features, ILC is yet to reach widespread adoption by the control community, due to several assumptions that restrict its generality when compared to feedback control. In this dissertation, we relax some of these assumptions, mainly the fundamental invariance assumption, and move from the idea of learning through repetition to two dimensional systems, specifically repetitive processes, that appear in the modeling of engineering applications such as additive manufacturing, and sketch out future research directions for increased practicality: We develop an L1 adaptive feedback control based ILC architecture for increased robustness, fast convergence, and high performance under time varying uncertainties and disturbances. Simulation studies of the behavior of this combined L1-ILC scheme under iteration varying uncertainties lead us to the robust stability analysis of iteration varying systems, where we show that these systems are guaranteed to be stable when the ILC update laws are designed to be robust, which can be done using existing methods from the literature. As a next step to the signal space approach adopted in the analysis of iteration varying systems, we shift the focus of our work to repetitive processes, and show that the exponential stability of a nonlinear repetitive system is equivalent to that of its linearization, and consequently uniform stability of the corresponding state space matrix.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133232/1/altin_1.pd