High-Performance Aerial Platforms

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.

A high-order tuner for accelerated performance and learning: 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 project pertains to higher-order learning concepts that attempts to improve performance in the presence of time-varying regressors. Elements of convex optimization in the presence of constraints, high-order parameter learning, exponential convergence, and trade-off between performance and learning are being explored.

Adaptive Control and Intersections with Reinforcement Learning: Adaptive control and reinforcement learning are two different methods that are both commonly employed for the control of uncertain systems. Historically, adaptive control has excelled at real-time control of systems with specific model structures through adaptive rules that learn the underlying parameters while providing strict guarantees on stability, asymptotic performance, and learning. Reinforcement learning methods are applicable to a broad class of systems and are able to produce near-optimal policies for highly complex control tasks. This is often enabled by significant offline training via simulation or the collection of large input-state datasets. This article attempts to compare adaptive control and reinforcement learning using a common framework. The problem statement in each field and highlights of their results are outlined. Two specific examples of dynamic systems are used to illustrate the details of the two methods, their advantages, and their deficiencies. The need for real-time control methods that leverage tools from both approaches is motivated through the lens of this common framework.

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). Current work is focused on how adaptation can occur in the presence of rate limits for general flight platform models. A filter placed in the control path accommodates the presence of rate limits, but introduces other challenges in analytical tractability. These challenges are overcome using an output feedback based adaptive controller. Analytical guarantees of bounded solutions and satisfactory tracking as well as numerical validations using a high-fidelity model have been provided.

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.

Adaptation and Optimization: There is a concerted effort ongoing in understanding the fundamental relationships between adaptation, learning, and optimization. While adaptation is necessarily a concept that is based on the past and the present, optimization is focused on the future; learning is a link between these two foundational concepts. Several directions are being pursued to gain this understanding.