L1 adaptive control flight testing and extension to nonlinear reference systems with unmatched uncertainty

Building upon prior research efforts deploying L1 adaptive control in remotely piloted aerospace applications, this dissertation presents the progression of in-flight evaluation of L1 adaptive control to manned flight testing on Calspan’s variable stability Learjet and to an augmentation of an autonomous trajectory planner on a multirotor aircraft. These efforts ultimately led to the development of a new L1 adaptive controller for a class of control-affine nonlinear reference systems subject to time-varying, state-dependent matched and unmatched uncertainties. The L1 adaptive controller for the Learjet flight tests was designed as stability augmentation system, modifying the pilot's stick-to-surface commands, and was evaluated in a series of flying and handling qualities tests. The results of the Learjet flight tests demonstrated the ability of the L1 adaptive controller to recover desired flying qualities and safe, consistent handling qualities in the presence of off-nominal dynamics, some of which had severe flying qualities deficiencies and aggressive tendencies toward adverse pilot-aircraft interaction, and simulated aircraft failures. A modification of the Learjet control law was implemented, with a nonlinear reference system and estimation of both matched and unmatched uncertainties, for a multirotor aircraft as an augmentation of a geometric trajectory-tracking baseline controller, tracking a reference trajectory generated by a model predictive path integral trajectory planner. Simulation results demonstrated that, with the L1 augmentation, the vehicle was able to navigate a complex environment in the presence of uncertainty and external disturbances. The new L1 adaptive controller provides a theoretical foundation for the L1 augmentation in the multirotor application, and may be applicable to tilt-rotor, tilt-wing, and split-propulsion vertical takeoff and landing aircraft proliferating in the urban air mobility sector. The theory is based on incremental stability for robust trajectory tracking and uses a piecewise-constant adaptive law. It proposes a feedforward compensator (in the form of an embedded linear parameter-varying system), synthesized for the variational dynamics of the system using linear matrix inequality-based robust control methods to minimize the peak-to-peak gain from unmatched uncertainty to the system state. A realization of the feedforward compensator in the ambient space can be directly applied to the nonlinear system. Analysis of the closed-loop system provides an incremental stability guarantee and bounds the transient and steady-state trajectory-tracking error

Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors

Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this letter, we propose L1 -NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline

L1 adaptive control for nonlinear and non-square multivariable systems

This research presents development of L1 adaptive output-feedback control theory for a class of uncertain, nonlinear, and non-square multivariable systems. The objective is to extend the L1 adaptive control framework to cover a wide class of underactuated systems with uniform performance and robustness guarantees. This dissertation starts by investigating some structural properties of multivariable systems that are used in the development of L1 adaptive output feedback controllers. In particular, a state-decomposition is introduced for adaptive laws that only depends on the output signals. The existence of the decomposition is ensured by defining a virtual system for underactuated plants. Based on the mathematical findings, we propose a set of output feedback solutions for uncertain underactuated systems. In adaptive control applications, a baseline control augmentation is often preferred, where the baseline controller defines the nominal system response. Adaptive controllers are incorporated into the control loop to improve the system response by recovering the nominal performance in the presence of uncertainties. This thesis provides a solution for L1 output feedback control augmentation. Stability and transient performance bounds are proven using Lyapunov analysis. To demonstrate the benefits of the L1 adaptive controllers we consider a missile system and an inverted pendulum, which are both underactuated systems. Finally, we propose a filter design framework in the frequency domain. A new sufficient condition is presented to ensure stability of the closed loop and the reference systems, which is subsequently used in the optimal filter design. Existing H-infinity optimization techniques are leveraged to address the performance and robustness trade-off issues