Flight Control

Autonomous functioning via real-time monitoring and information management is an attractive ingredient in the design of any complex system. The inevitable presence of uncertainties due to malfunctions, environmental variations, ageing, and modeling errors, requires this management to be adaptive. Several projects are ongoing in the area of flight control and analysis, and are listed below.

Adaptive Control in the Presence of Rate Saturation

Actuator rate saturation nonlinearities are not often explicitly accounted for in the design of flight control systems. Rate saturated actuators pose the risk of failing, rendering a control system unstable and creating pilot induced oscillations (PIO). Two architectures are presented in this area to explicitly counter the effects of rate saturation within an output feedback MIMO adaptive control framework.

Adaptive Control of Hypersonic Vehicles in the Presence of Rate Limits

Block_Diagram2 The rate saturation architecture proposed in this section is formulated as an add-on to an existing sequential loop closure based guidance and control framework. The rate limiter functions by changing the guidance and control commands to push the system away from rate saturation. A buffer region is used to begin the limiting effects before a rate limit is reached. Tracking of the desired guidance command resumes as the aircraft leaves the designed rate saturation buffer region. Augmented output feedback adaptive laws are introduced to account for the effects of hard magnitude saturation additionally. This architecture is used to control a nonlinear hypersonic vehicle model in the presence of rate saturation.

Adaptive Control Theory in the Presence of Hard Limits on Magnitude and Rate with Aerospace Applications

Hard_Limiter_Block_Diagram This section presents an adaptive controller for multiple-input-multiple-output (MIMO) plants with input magnitude and rate saturation in the presence of parametric uncertainty and output feedback. A filter placed in the control path accommodates the presence of rate limits, but introduces challenges in matching conditions and the corresponding control design. These challenges are overcome using an output feedback based adaptive controller to account for the increase in the relative degree. The overall control architecture is based on a linearized model, and includes adaptive laws that are modified to account for the magnitude and rate limits. Analytical guarantees of bounded solutions and satisfactory tracking are provided. The performance of the architecture is validated using a numerical model of an aircraft at a single operating point, in the presence of parametric uncertainties.

Shared Control

As aerial vehicles become more autonomous, and guidance and navigation systems become increasingly network-centric, there is a need to consider a swift response to the growing forms of anomalies that may occur during operation. In this work, we integrate human pilots (either remote or onboard) of aerial vehicles in the detection, diagnosis, and correction of anomalous aircraft dynamical behavior, without a transfer from autonomous to manual control. Autonomous model-based controllers, including model reference adaptive control, rely on accurate modeling of dynamical systems and make assumptions about the relative time constants of unmodeled dynamics. Online changes to these unmodeled dynamics may cause degraded command tracking performance and a loss of stability. Trained human pilots, on the other hand, are able to detect anomalous vehicle behavior which differs from their internal model but are found to have limits when attempting to rapidly learn unfamiliar and anomalous vehicle dynamics. This problem is exacerbated when the human pilot is operating the vehicle from a remote ground station. We introduce a shared control architecture in which a human supervisor (operator) may make suitable changes to the control model of the system following anomalous plant behavior to allow for continued autonomous adaptive control without transferring control responsibilities to the human operator. Use of the trained human operator's perceptive and decision-making capabilities extends the autopilot's ability to handle anomalous vehicle behavior beyond that of a fully autonomous system.

Shared Decision-Making and Adaptive Output-Feedback Control for UAV Anomaly Mitigation

VFA_Rendering Shared_Control_Response

The shared control framework is applied to the anomaly response problem for a high altitude, long endurance (HALE) very flexible aircraft (VFA) model. An autopilot based on model reference adaptive control (MRAC) with output feedback and closed-loop reference models (CRM) compensates for uncertainties in both the vehicle and actuator dynamics online. We consider an anomaly in the longitudinal dynamics of the HALE VFA, in which the actuator dynamics change from first-order to second-order. A passive (fully autonomous) anomaly response results in loss of stability and eventual structural failure of the vehicle. A complete transfer to manual control, on the other hand, may result in loss of control as remote human pilots are unfamiliar with the anomalous dynamics and are not able to sense the anomalous open-loop dynamics of the vehicle through typical vestibular pathways. A shared response, in which the human operator detects and isolates the anomaly, and intervenes to change the MRAC control model, allows for the recovery of the nominal command tracking performance in the presence of anomalous and uncertain actuator dynamics.

Cyber Attack Mitigation

As it becomes more common to see complex dynamic systems that are monitored and controlled over a network, the study of these cyber-physical systems (CPS) has largely concerned their vulnerability to malicious cyber attacks. If a hacker could modify or block access to sensor or actuator data, he or she has the potential to do tremendous physical damage to the system, and in many cases, cause harm to people as well. We are concerned with a class of attacks that are considered undetectable or very difficult to detect, making it difficult to know that damage is being done, let alone respond to the threat. Our goal is to develop strategies combined with estimation techniques that enhance our detection capabilities.