Inlet unstart on hypersonic aircraft causes a rapid and dangerous loss of thrust. Fortunately, proper control design can help prevent inlet unstart. This paper demonstrates how [K]control of Adaptive Multiple timescale Systems (KAMS) can effectively address this challenging problem. First, a multiple-timescale model of a hypersonic aircraft is developed to facilitate the control law design. Then, a KAMS controller is designed using Adaptive Nonlinear Dynamic Inversion to stabilize the reduced subsystems and Sequential Control is used to fuse the control signals for reduced subsystems. The closed-loop system is proven to be stable despite weak non-minimum phase effects. KAMS provides stability guarantees that are more rigorous than prior work and also provides insights into the system's underlying physics. Numerical results presented in the paper show that KAMS can effectively prevent inlet unstart and mitigate uncertainty using angle-of-attack regulation.

Singularly perturbed systems are a class of mathematical systems that are not well approximated by their limits and can be used to model plants with multiple fast and slow states. Multiple-timescale systems are very common in engineering applications, but adaptive control can be sensitive to timescale effects. Recently a method called [K]control of Adaptive Multiple-timescale Systems (KAMS) has shown improved performance and increased robustness for singularly perturbed systems, but it has only been studied on systems using adaptive control for the slow states. This article extends KAMS to the general case when adaptive control is used to stabilize both the slow and fast states simultaneously. This causes complex interactions between the fast state reference model and the manifold to which the fast states converge. It is proven that under certain conditions the system still converges to the reference model despite these complex interactions. This method is demonstrated on a nonlinear, nonstandard, numerical example.

Tracking the motion of an evasive, or even hostile, maneuvering ground target based on no percepts other than aerial images is a challenge that is relevant to military and commercial applications. This paper investigates the problem of tracking such a target using a multirotor unmanned air system with a non-gimballed optical sensor. The fixed sensor makes the problem challenging because the vehicle must use its dynamics and therefore attitude to change the sensor's field of view. A Soft Actor-Critic reinforcement learning controller is developed to address the maneuvering ground target tracking problem for various target movement types including a sinusoidal motion, random motion, and evasive motion. The addition of ground occlusions which block the agent's view of the target is also investigated. This is challenging because the reinforcement learning agent does not have information about the location or the existence of occlusions, but must still generate actions which track the target. Results in a simulated environment demonstrate the effectiveness of the learned policy. Consistent indefinite tracking is achieved with the mean distance of the target from the center of the image varying from 23 to 27 pixels for all motion types tested. Selection of the command rate, the rate at which the agent can generate actions, is also investigated. Faster command rates from the learned policy are shown to be less effective for tracking a maneuvering ground target since the agent needs to perform a greater number of correct sequential actions. Finally, the agent is able to successfully track a randomly moving target with the presence of occlusions.

Multiple-timescale systems are a noteworthy class of dynamic systems that can be modeled with singularly perturbed differential equations. Adaptive control has not been studied in the context of singularly perturbed plants. This paper introduces and evaluates three methods of adaptive control for multiple-timescale systems. Each method is a framework that is valid for a wide class of adaptive control methods. Full-order adaptive control (FOAC) applies adaptive control to the system as a whole. It is straightforward but can be sensitive to timescale effects. Reduced-order adaptive control (ROAC) applies adaptive control to either the fast or slow modes only. This simplifies synthesis but can also constrain the range of valid timescale separation. [K]Control of Adaptive Multiple-Timescale Systems (KAMS) fuses two adaptive control signals using multiple-timescale techniques. KAMS takes advantage of model reduction unlike FOAC, and allows for unstable fast dynamics unlike ROAC. Generalized formal definitions, stability criteria, and examples are developed and presented for each method. Results presented in the paper for the control of a Boeing 747-100/200 on approach show that KAMS has a desirable blend of performance and robustness because each reduced-order model is stabilized separately.

Adaptive control for non-minimum phase systems is a challenging problem. This paper proposes a method of adaptive control for systems that may be both nonlinear and non-minimum phase. This is accomplished by exploiting time scale separation between the internal and external dynamics. The original non-minimum phase control problem is reduced to two minimum phase control problems through a time scale analysis. The resulting adaptive control signals are fused via multiple time scale control techniques. Singular perturbation theory is used to prove the stability and convergence of the full-order system as an extension of the stability and convergence of the two reduced-order systems. The effectiveness of this method is validated on a nonlinear example system.

Multiple time scale systems are a noteworthy class of dynamical systems. Adaptive control has not been studied in the context of multiple time scale systems because it is not robust to time scale separation. This paper introduces and evaluates three methods of adaptive control for multiple time scale systems. Each method is a framework that is valid for a wide class of adaptive control methods. Full-Order Adaptive Control (FOAC) applies adaptive control to the system as a whole. It is straightforward but sensitive to time scale uncertainty. Reduced-Order Adaptive Control (ROAC) applies adaptive control to either the fast or slow modes only. This simplifies synthesis but can also constrain the range of valid time scale separation. [K]Control of Adaptive Multiple Time Scale Systems (KAMS) fuses two adaptive control signals using multiple time scale techniques. Generalized formal definitions, stability criteria, and examples are developed and presented for each method. Results show that [K]Control of Adaptive Multiple Time Scale Systems has the best performance because each reduced-order model is stabilized separately and because the fast dynamics converge to the manifold more quickly than the other methods. It also takes advantage of model reduction and relaxes the stability requirements of the other methods.

Certain dynamic modes of asymmetric quadrotor configurations are difficult to accurately model analytically. This paper synthesizes an analytical nonlinear parametric state-space model of an asymmetric quadrotor, and verifies it using a non-parametric model calculated from experimentally measured inputs and outputs of the actual vehicle. The offline system identification process produces a discrete-time Linear Time Invariant state-space model using the Observer Kalman Identification algorithm. This model is converted to a continuous time model for comparison to the linearized analytical model. Eigenvlaues, modes, and mode metrics are used to compare the parametric and non-parametric linear models. Results presented in the paper demonstrate that the identified linear model compares well to the linearized analytical model and validates the approach.