Model-Reference Adaptive Control of Distributed Lagrangian Infinite-Dimensional Systems Using Hamiltons Principle

This paper presents a Hamilton's principle for distributed control of infinite-dimensional systems modeled by a distributed form of the Euler-Lagrange method. The distributed systems are governed by a system of linear partial differential equations in space and time. A generalized potential energy expression is developed that can capture most physical systems including those systems that have no spatial distribution. The Hamilton's principle is applied to derive distributed feedback control methods without resorting to the standard weak-form discretization approach to convert an infinite-dimensional systems to a finite-dimensional systems. It can be shown by the principle of least action that the distributed control synthesized by the Hamilton's principle is a minimum-norm control. A model-reference adaptive control framework is developed for distributed Lagrangian systems in the presence of uncertainty. The theory is demonstrated by an application of adaptive flutter suppression control of a flexible aircraft wing

Stability Analysis of a Human-in-the-Loop Telerobotics System with Two Independent Time-Delays

In this paper, stability of a human-in-the-loop telerobotics system with force feedback and communication delays is investigated. A general linear time-invariant time-delayed mathematical model of the human operator is incorporated into the system dynamics based on the interaction of the human operator with the rest of the telerobotic system. The resulting closed loop dynamics contains two independent time-delays mainly due to back and forth communication delay and human reaction time delay. Stability of this dynamics is characterized next on the plane of the two delays by rigorous mathematical investigation using Cluster Treatment of Characteristic Roots (CTCR). An illustrative numerical example is further provided in the results section along with interpretations. © 201

Stability analysis of human–adaptive controller interactions

In this paper, stability of human in the loop model reference adaptive control architectures is analyzed. For a general class of linear human models with time-delay, a fundamental stability limit of these architectures is established, which depends on the parameters of this human model as well as the reference model parameters of the adaptive controller. It is shown that when the given set of human model and reference model parameters satisfy this stability limit, the closed-loop system trajectories are guaranteed to be stable. © 2017, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved

Stability limit of human-in-the-loop model reference adaptive control architectures

Model reference adaptive control (MRAC) offers mathematical and design tools to effectively cope with many challenges of real-world control problems such as exogenous disturbances, system uncertainties and degraded modes of operations. On the other hand, when faced with human-in-the-loop settings, these controllers can lead to unstable system trajectories in certain applications. To establish an understanding of stability limitations of MRAC architectures in the presence of humans, here a mathematical framework is developed whereby an MRAC is designed in conjunction with a class of linear human models including human reaction delays. This framework is then used to reveal, through stability analysis tools, the stability limit of the MRAC–human closed-loop system and the range of model parameters respecting this limit. An illustrative numerical example of an adaptive flight control application with a Neal–Smith pilot model is presented to demonstrate the effectiveness of developed approaches. © 2017 Informa UK Limited, trading as Taylor & Francis Grou

Effects of linear filter on stability and performance of human-in-the-loop model reference adaptive control architectures

Model reference adaptive control (MRAC) can effectively handle various challenges of the real world control problems including exogenous disturbances, system uncertainties, and degraded modes of operations. In human-in-the-loop settings, MRAC may cause unstable system trajectories. Basing on our recent work on the stability of MRAC-human dynamics, here we follow an optimization based computations to design a linear filter and study whether or not this filter inserted between the human model and MRAC could help remove such instabilities, and potentially improve performance. To this end, we present a mathematical approach to study how the error dynamics of MRAC could favorably or detrimentally influence human operator's error dynamics in performing a certain task. An illustrative numerical example concludes the study. Copyright © 2017 ASME

Command Governor-Based Adaptive Control of an Autonomous Helicopter

DOI: http://dx.doi.org/10.2514/6.2012-4830This paper presents an application of a recently developed command governor-based adaptive control framework to a high-fidelity autonomous helicopter model. This framework is based on an adaptive controller, but the proposed command governor adjusts the trajectories of a given command in order to follow an ideal reference system (capturing a desired closed-loop system behavior) both in transient-time and steady-state without resorting to high-gain learning rates in the adaptation (update) law. The high-fidelity autonomous helicopter is a six rigid body degree of freedom model, with additional engine, fuel and rotor dynamics. Non-ideal attributes of physical systems such as model uncertainty, sensor noise, and actuator dynamics are modeled to evaluate the command governor controller in realistic conditions. The proposed command governor adaptive control framework is shown to reduce attitude error with respect to a standard adaptive control scheme during vehicle maneuvers