Vahram Stepanyan

Information consensus in distributed multiple vehicle coordinated control

Cooperation in multiple vehicle teams requires information to be shared between team members. If shared information is not synchronized across the team, then cooperation is adversely affected. This paper considers the problem of information consensus in multiple agent teams. We define notions of asymptotic consensus and show that a team of agents can be asymptotically brought into consensus if and only if the associated communication topology admits a spanning tree. A linear consensus strategy is proposed and demonstrated via several simulation examples.

Robust Adaptive Observer Design for Uncertain Systems With Bounded Disturbances

This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters and non-vanishing disturbances. Using the universal approximation property of radial basis function neural networks and the adaptive bounding technique the developed observer achieves asymptotic convergence of state estimation error to zero, while ensuring boundedness of parameter errors. A comparative simulation study is presented by the end.

Visual Tracking of a Maneuvering Target

This paper presents an acceleration-command generation and its implementation for an aerial vehicle in the problem of visual target tracking, when the target is free to make any maneuver with arbitrary but otherwise bounded acceleration. The acceleration command is generated by an adaptive disturbance rejection control algorithm that uses only visual information about the target, obtained by a monocular camera mounted on the aerial vehicle. This information consists of the maximal image size in pixels and the pixel coordinates of the image centroid in the image plane. The acceleration command is translated into forward velocity and attitude angle commands, which are afterward used to obtain actual control surface deflections for the aerial vehicle. Simulations of a full nonlinear model verify the theoretical statements.

Aerial Refueling Autopilot Design Methodology: Application to F-16 Aircraft Model

The problem of autonomous aerial refueling autopilot design is considered using techniques from differential games and adaptive control. A closed-loop linear reference model is built using a differential game approach. This way derived optimal control is augmented with an adaptive signal for compensation of vortex induced uncertainties.

Stability and Performance Metrics for Adaptive Flight Control

This paper addresses the problem of verifying adaptive control techniques for enabling safe flight in the presence of adverse conditions. Since the adaptive systems are non-linear by design, the existing control verification metrics are not applicable to adaptive controllers. Moreover, these systems are in general highly uncertain. Hence, the systems characteristics cannot be evaluated by relying on the available dynamical models. This necessitates the development of control verification metrics based on the systems input-output information. For this point of view, a set of metrics is introduced that compares the uncertain aircrafts input-output behavior under the action of an adaptive controller to that of a closed-loop linear reference model to be followed by the aircraft. This reference model is constructed for each specific maneuver using the exact aerodynamic and mass properties of the aircraft to meet the stability and performance requirements commonly accepted in flight control. The proposed metrics are unified in the sense that they are model independent and not restricted to any specific adaptive control methods. As an example, we present simulation results for a wing damaged generic transport aircraft with several existing adaptive controllers.

Adaptive Disturbance Rejection Controller for Visual Tracking of a Maneuvering Target

This paper presents an adaptive disturbance rejection control architecture for a flying vehicle to track a maneuvering target using a monocular camera as a visual sensor. Viewing the target’s velocity as a time-varying disturbance, the change in magnitude of which has a bounded integral, a guidance law is derived that guarantees asymptotic tracking of the target in the presence of measurement noise and an intelligent excitation signal in the reference input. Implementation of the guidance law is done via conventional adaptive block backstepping. Simulations illustrate the benefits of the method.

Robust Adaptive Observer Design for Uncertain Systems with Bounded Disturbances

This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters and non-vanishing disturbances. Using the universal approximation property of radial basis function neural networks and the adaptive bounding technique the developed observer achieves asymptotic convergence of state estimation error to zero, while ensuring boundedness of parameter errors. A comparative simulation study is presented by the end.

Adaptive Control with Reference Model Modification

This paper presents a modification of the conventional model reference adaptive control (MRAC) architecture in order to improve transient performance of the input and output signals of uncertain systems. A simple modification of the reference model is proposed by feeding back the tracking error signal. It is shown that the proposed approach guarantees tracking of the given reference command and the reference control signal (one that would be designed if the system were known) not only asymptotically but also in transient. Moreover, it prevents generation of high frequency oscillations, which are unavoidable in conventional MRAC systems for large adaptation rates. The provided design guideline makes it possible to track a reference commands of any magnitude from any initial position without re-tuning. The benefits of the method are demonstrated with a simulation example

Estimating Loss-of-Control: a Data-Based Predictive Control Approach

Loss-of-control is a major contributor to aircraft fatalities. Recent work has been done to develop quantitative criteria for determining loss-of-control from accident time history data. This work proposes an approach to estimating boundaries on control actions to provide information to pilots and/or control systems to assist in avoiding loss-of-control scenarios. Data-based predictive control theory is used to develop an algorithm that finds the minimum control input that will result in the aircraft exceeding a safe operating envelope at various minimum time estimates. The calculated minimum control inputs become a boundary of a set of safe control inputs. With this information, a pilot could change flying strategy or an autonomous system could schedule controller gains to prevent the vehicle from exceeding the envelope.

Adaptive Vortex Seeking Formation Flight Neurocontrol

This paper focuses on adaptive output-tracking flight control design problem with on-line extremum seeking command generation. The problem is motivated by the need to design an autopilot for autonomous close-coupled formation flight of two aircraft. Control design task is associated with the trailing aircraft only. Flying in formation, the trailing aircraft must constantly seek an optimal relative to the leader position that minimizes aerodynamic drag force induced by the wing tip vortices of the lead aircraft. The trailing aircraft dynamics are represented by two interconnected uncertain subsystems: 1) longitudinal (relative position, true airspeed) dynamics and 2) lateral- directional (lateral position, bank angle, roll rate) dynamics. The two subsystems approximate the trailing aircraft motion in a closed-coupled formation with the leader. While the control command for the first subsystem is predetermined (desired longitudinal separation), the command for the second subsystem (desired lateral separation) is computed on-line such that the influence of the second subsystem on the first one is minimized. Using feedforward neural networks, direct adaptive model reference control, and on-line extremum seeking command generation, the proposed formation flight autopilot provides the trailing aircraft with bounded output tracking and minimizes effects of vortex uncertainty on the aircraft aerodynamic drag force. Boundedness of the tracking error signals in the closed-loop system is shown using Lyapunov direct method.