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

Neuroadaptive Model Following Controller Design for a Nonaffine UAV Model

This paper proposes a new model-following adaptive control design technique for nonlinear systems that are nonaffine in control. The adaptive controller uses online neural networks that guarantee tracking in the presence of unmodeled dynamics and/or parameter uncertainties present in the system model through an online control adaptation procedure. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture the unmodeled (neglected) dynamics or model uncertainties due to parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a reference model. This method is tested using a three degree of freedom model of a UAV. Numerical results which demonstrate these features and clearly bring out the potential of the proposed approach are presented in this paper

Observer-based event-triggered and set-theoretic neuro-adaptive controls for constrained uncertain systems

In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon L1 libration point. This dynamic system is selected to evaluate the performance of the ETNAC techniques in a setting that is highly nonlinear and chaotic in nature. Moreover, factors like restricted controls, response to uncertainties and jittering makes the controller design even trickier for maintaining a tight formation precision. Lyapunov function-based stability analysis and numerical results are presented. Note that most real-world systems involve constraints due to hardware limitations, disturbances, uncertainties, nonlinearities, and cannot always be efficiently controlled by using linearized models. To address all these issues simultaneously, a barrier Lyapunov function-based control architecture called the segregated prescribed performance guaranteeing neuro-adaptive control is developed and tested for the constrained uncertain nonlinear systems, in the third part. It guarantees strict performance that can be independently prescribed for each individual state and/or error signal of the given system. Furthermore, the proposed technique can identify unknown dynamics/uncertainties online and provides a way to regulate the control input --Abstract, page iv

Event-triggering architectures for adaptive control of uncertain dynamical systems

In this dissertation, new approaches are presented for the design and implementation of networked adaptive control systems to reduce the wireless network utilization while guaranteeing system stability in the presence of system uncertainties. Specifically, the design and analysis of state feedback adaptive control systems over wireless networks using event-triggering control theory is first presented. The state feedback adaptive control results are then generalized to the output feedback case for dynamical systems with unmeasurable state vectors. This event-triggering approach is then adopted for large-scale uncertain dynamical systems. In particular, decentralized and distributed adaptive control methodologies are proposed with reduced wireless network utilization with stability guarantees. In addition, for systems in the absence of uncertainties, a new observer-free output feedback cooperative control architecture is developed. Specifically, the proposed architecture is predicated on a nonminimal state-space realization that generates an expanded set of states only using the filtered input and filtered output and their derivatives for each vehicle, without the need for designing an observer for each vehicle. Building on the results of this new observer-free output feedback cooperative control architecture, an event-triggering methodology is next proposed for the output feedback cooperative control to schedule the exchanged output measurements information between the agents in order to reduce wireless network utilization. Finally, the output feedback cooperative control architecture is generalized to adaptive control for handling exogenous disturbances in the follower vehicles. For each methodology, the closed-loop system stability properties are rigorously analyzed, the effect of the user-defined event-triggering thresholds and the controller design parameters on the overall system performance are characterized, and Zeno behavior is shown not to occur with the proposed algorithms --Abstract, page iv

Adaptive physical human-robot interaction (PHRI) with a robotic nursing assistant.

Recently, more and more robots are being investigated for future applications in health-care. For instance, in nursing assistance, seamless Human-Robot Interaction (HRI) is very important for sharing workspaces and workloads between medical staff, patients, and robots. In this thesis we introduce a novel robot - the Adaptive Robot Nursing Assistant (ARNA) and its underlying components. ARNA has been designed specifically to assist nurses with day-to-day tasks such as walking patients, pick-and-place item retrieval, and routine patient health monitoring. An adaptive HRI in nursing applications creates a positive user experience, increase nurse productivity and task completion rates, as reported by experimentation with human subjects. ARNA has been designed to include interface devices such as tablets, force sensors, pressure-sensitive robot skins, LIDAR and RGBD camera. These interfaces are combined with adaptive controllers and estimators within a proposed framework that contains multiple innovations. A research study was conducted on methods of deploying an ideal HumanMachine Interface (HMI), in this case a tablet-based interface. Initial study points to the fact that a traded control level of autonomy is ideal for tele-operating ARNA by a patient. The proposed method of using the HMI devices makes the performance of a robot similar for both skilled and un-skilled workers. A neuro-adaptive controller (NAC), which contains several neural-networks to estimate and compensate for system non-linearities, was implemented on the ARNA robot. By linearizing the system, a cross-over usability condition is met through which humans find it more intuitive to learn to use the robot in any location of its workspace, A novel Base-Sensor Assisted Physical Interaction (BAPI) controller is introduced in this thesis, which utilizes a force-torque sensor at the base of the ARNA robot manipulator to detect full body collisions, and make interaction safer. Finally, a human-intent estimator (HIE) is proposed to estimate human intent while the robot and user are physically collaborating during certain tasks such as adaptive walking. A NAC with HIE module was validated on a PR2 robot through user studies. Its implementation on the ARNA robot platform can be easily accomplished as the controller is model-free and can learn robot dynamics online. A new framework, Directive Observer and Lead Assistant (DOLA), is proposed for ARNA which enables the user to interact with the robot in two modes: physically, by direct push-guiding, and remotely, through a tablet interface. In both cases, the human is being “observed” by the robot, then guided and/or advised during interaction. If the user has trouble completing the given tasks, the robot adapts their repertoire to lead users toward completing goals. The proposed framework incorporates interface devices as well as adaptive control systems in order to facilitate a higher performance interaction between the user and the robot than was previously possible. The ARNA robot was deployed and tested in a hospital environment at the School of Nursing of the University of Louisville. The user-experience tests were conducted with the help of healthcare professionals where several metrics including completion time, rate and level of user satisfaction were collected to shed light on the performance of various components of the proposed framework. The results indicate an overall positive response towards the use of such assistive robot in the healthcare environment. The analysis of these gathered data is included in this document. To summarize, this research study makes the following contributions: Conducting user experience studies with the ARNA robot in patient sitter and walker scenarios to evaluate both physical and non-physical human-machine interfaces. Evaluation and Validation of Human Intent Estimator (HIE) and Neuro-Adaptive Controller (NAC). Proposing the novel Base-Sensor Assisted Physical Interaction (BAPI) controller. Building simulation models for packaged tactile sensors and validating the models with experimental data. Description of Directive Observer and Lead Assistance (DOLA) framework for ARNA using adaptive interfaces