Observer-based event-triggered and set-theoretic neuro-adaptive controls for constrained uncertain systems

In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon L1 libration point. This dynamic system is selected to evaluate the performance of the ETNAC techniques in a setting that is highly nonlinear and chaotic in nature. Moreover, factors like restricted controls, response to uncertainties and jittering makes the controller design even trickier for maintaining a tight formation precision. Lyapunov function-based stability analysis and numerical results are presented. Note that most real-world systems involve constraints due to hardware limitations, disturbances, uncertainties, nonlinearities, and cannot always be efficiently controlled by using linearized models. To address all these issues simultaneously, a barrier Lyapunov function-based control architecture called the segregated prescribed performance guaranteeing neuro-adaptive control is developed and tested for the constrained uncertain nonlinear systems, in the third part. It guarantees strict performance that can be independently prescribed for each individual state and/or error signal of the given system. Furthermore, the proposed technique can identify unknown dynamics/uncertainties online and provides a way to regulate the control input --Abstract, page iv

Theoretical and experimental application of neural networks in spaceflight control systems

“Spaceflight systems can enable advanced mission concepts that can help expand our understanding of the universe. To achieve the objectives of these missions, spaceflight systems typically leverage guidance and control systems to maintain some desired path and/or orientation of their scientific instrumentation. A deep understanding of the natural dynamics of the environment in which these spaceflight systems operate is required to design control systems capable of achieving the desired scientific objectives. However, mitigating strategies are critically important when these dynamics are unknown or poorly understood and/or modelled. This research introduces two neural network methodologies to control the translation and rotation dynamics of spaceflight systems. The first method uses a neural network to perform nonlinear estimation in the control space for both translational and attitude control. The second method uses an observer with a neural network to perform estimation outside the control space, and input-output feedback linearization using the estimated dynamics for both translational and attitude control. The methods are demonstrated for attitude control through simulation and hardware testing on the Wallops Arc-Second Pointer, a high-altitude balloon-borne spaceflight system. Results show that the two new methodologies can provide improved attitude control performance over the heritage control system. The methods are also demonstrated for translational and attitude control of two small spacecraft in a deep space environment, where they provide improved position and attitude control performance as compared to a traditional control method. This work demonstrates, through simulation and hardware testing, that the two neural network methods presented can offer improved translational and attitude control performance of spaceflight systems where the dynamic environment may be unknown or poorly understood and/or modeled”--Abstract, page iv

ETNAC Design Enabling Formation Flight at Liberation Points

This study considers the feasibility of an event-triggered neuro-adaptive controller (ETNAC) providing precision flying control for microsatellites used for deep space missions. For \u27smallsats\u27 factors including limited capabilities of the microsatellite platform, minimal communication, restricted controls and actuation, overly sensitive response to uncertainties, etc. make the controller design challenging. To cope with such challenges, an ETNAC design is proposed in this study. Its performance analysis is given along with its derivation and implementation. ETNAC is based on an observer, known as Modified State Observer (MSO), which is used for online approximation of the uncertainties in the system. The MSO formulation has two tunable gains that allow for fast estimation without inducing high frequency oscillations in the system. At the same time, an event triggering mechanism (ETM) is used in an aperiodic fashion to transmit state information and update the control only when required. In this way, it reduces communication and computational efforts, simplifying onboard implementations. A Lyapunov analysis is used to prove stability. Simulation and performance results show that ETNAC can be an excellent solution for highly nonlinear resource-constrained problems