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.

Accelerated Learning in the Presence of Time Varying Features with Applications to Machine Learning and Adaptive Control

Error_MRAC Features in machine learning problems are often time varying and may be related to outputs in an algebraic or dynamical manner. The dynamic nature of these machine learning problems renders current accelerated gradient descent methods unstable or weakens their convergence guarantees. This work proposes algorithms for the case when time varying features are present, and demonstrates provable performance guarantees. We develop a variational perspective within a continuous time algorithm. This variational perspective includes, among other things, higher-order learning concepts and normalization, both of which stem from adaptive control, and allows stability to be established for dynamical machine learning problems. These higher-order algorithms are also examined for achieving accelerated learning in adaptive control.


Connections Between Adaptive Control and Optimization in Machine Learning

Error_Models This work demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning. Starting from common output error formulations, similarities in update law modifications are examined. Concepts in stability, performance, and learning, common to both fields are then discussed. Building on the similarities in update laws and common concepts, new intersections and opportunities for improved algorithm analysis are provided.


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 and Cyber-Physical & Human Systems

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. An on-going project in our lab is the development of a shared control architecture that includes the actions of both a human pilot and an autopilot to ensure resilient tracking performance in the presence of anomalies. Autonomous model-based controllers, including model reference adaptive control, rely on model-structures, specified performance goals, and assumptions on structured uncertainties. 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. The goal is to therefore examine shared control architectures where the pilot is tasked with higher-level decision making tasks such as anomaly detection, estimation and command regulation and the autopilot is assigned lower-level tasks such as command following. A general goal here is to understand how such cyber-physical & human systems can be designed for safe and efficient performance.

Shared Decision-Making in the presence of varying relative degree

A shared architecture that has been recently developed is a combination of human pilot based decision making and an adaptive control based autopilot design. We propose the use of human pilot based on concepts of Capacity for Maneuver (CfM) and Graceful Command Degradation (GCD), both of which originate in Cognitive Sciences. Together, they provide guidelines for a system to be resilient, which corresponds to the system’s readiness to respond to unforeseen events. The proposed design incorporates elements from adaptive control theory under the control of human pilot. The shared control architecture is shown to be capable of achieving maximum CfM while allowing minimal GCD, as well as satisfactory command following post-anomaly, resulting in resilient flight capabilities. The proposed controller is analyzed in a simulation study of a nonlinear F-16 aircraft under actuator anomalies. It is shown through numerical studies that under suitable inputs from the pilot, the shared controller is able to deliver a resilient flight.

Shared Decision-Making in the presence of varying relative degree

VFA_Rendering Shared_Control_Response

The shared control framework is applied to the anomaly response problem for a very flexible aircraft (VFA) model when the net order of the system changes due to to an anomaly. 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 adaptive controller, allows for the recovery of the nominal command tracking performance in the presence of anomalous and uncertain actuator dynamics.


Cyber Attack Mitigation

The goal of this work is to develop a defense methodology for a cyber-physical system by which an attempted stealthy cyber-attack is detected in near real time. This work considers the effects of a type of stealthy attack on a class of cyber-physical systems that can be modeled as linear time-invariant systems. The effects of this attack are studied from both the perspective of the attacker as well as the defender. A previously developed method for conducting stealthy attacks is introduced and analyzed. Successful implementation of this attack is shown to require the attacker to attain perfect model knowledge in order for the attack to be stealthy. A method is then proposed in which the defender attempts to feed the attacker a slightly falsified model, baiting the attacker with data that will make an attack detectable. It is then shown that the defender can not only detect this faulty attack, but use observations of the detection signal to regain more accurate state estimates, mitigating the effect of the attack.