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

L1 adaptive control for nonlinear and non-square multivariable systems

This research presents development of L1 adaptive output-feedback control theory for a class of uncertain, nonlinear, and non-square multivariable systems. The objective is to extend the L1 adaptive control framework to cover a wide class of underactuated systems with uniform performance and robustness guarantees. This dissertation starts by investigating some structural properties of multivariable systems that are used in the development of L1 adaptive output feedback controllers. In particular, a state-decomposition is introduced for adaptive laws that only depends on the output signals. The existence of the decomposition is ensured by defining a virtual system for underactuated plants. Based on the mathematical findings, we propose a set of output feedback solutions for uncertain underactuated systems. In adaptive control applications, a baseline control augmentation is often preferred, where the baseline controller defines the nominal system response. Adaptive controllers are incorporated into the control loop to improve the system response by recovering the nominal performance in the presence of uncertainties. This thesis provides a solution for L1 output feedback control augmentation. Stability and transient performance bounds are proven using Lyapunov analysis. To demonstrate the benefits of the L1 adaptive controllers we consider a missile system and an inverted pendulum, which are both underactuated systems. Finally, we propose a filter design framework in the frequency domain. A new sufficient condition is presented to ensure stability of the closed loop and the reference systems, which is subsequently used in the optimal filter design. Existing H-infinity optimization techniques are leveraged to address the performance and robustness trade-off issues

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Aeronautical Engineering: A continuing bibliography with indexes (supplement 207)

This bibliography lists 484 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1986

The estimation and compensation of processes with time delays

The estimation and compensation of processes with time delays have been of interest to academics and practitioners for several decades. A full review of the literature for both model parameter and time delay estimation is presented. Gradient methods of parameter estimation, in open loop, in the time and frequency domains are subsequently considered in detail. Firstly, an algorithm is developed, using an appropriate gradient algorithm, for the estimation of all the parameters of an appropriate process model with time delay, in open loop, in the time domain. The convergence of the model parameters to the process parameters is considered analytically and in simulation. The estimation of the process parameters in the frequency domain is also addressed, with analytical procedures being defined to provide initial estimates of the model parameters, and a gradient algorithm being used to refine these estimates to attain the global minimum of the cost function that is optimised. The focus of the thesis is subsequently broadened with the consideration of compensation methods for processes with time delays. These methods are reviewed in a comprehensive manner, and the design of a modified Smith predictor, which facilitates a better regulator response than does the Smith predictor, is considered in detail. Gradient algorithms are subsequently developed for the estimation of process parameters (including time delay) in closed loop, in the Smith predictor and modified Smith predictor structures, in the time domain; the convergence of the model parameters to the process parameters is considered analytically and in simulation. The thesis concludes with an overview of the methods developed, and projections regarding future developments in the topics under consideration

Techniques for studying vocal learning in bottlenose dolphins, Tursiops truncatus

Thesis (Ph. D.)--Joint Program in Biological Oceanography (Massachusetts Institute of Technology, Dept. of Biology; and the Woods Hole Oceanographic Institution), 1999.Vita.Includes bibliographical references.by Deborah Redish Fripp.Ph.D