Multi-Objective Robust H-infinity Control of Spacecraft Rendezvous

Based on the relative motion dynamic model illustrated by C-W equations, the problem of robust Hinfin control for a class of spacecraft rendezvous systems is investigated, which contains parametric uncertainties, external disturbances and input constraints. An Hinfin state-feedback controller is designed via a Lyapunov approach, which guarantees the closed-loop system to meet the multi-objective design requirements. The existence conditions for admissible controllers are formulated in the form of linear matrix inequalities (LMIs), and the controller design is cast into a convex optimization problem subject to LMI constraints. An illustrative example is provided to show the effectiveness of the proposed control design method

NASA Automated Rendezvous and Capture Review. Executive summary

In support of the Cargo Transfer Vehicle (CTV) Definition Studies in FY-92, the Advanced Program Development division of the Office of Space Flight at NASA Headquarters conducted an evaluation and review of the United States capabilities and state-of-the-art in Automated Rendezvous and Capture (AR&C). This review was held in Williamsburg, Virginia on 19-21 Nov. 1991 and included over 120 attendees from U.S. government organizations, industries, and universities. One hundred abstracts were submitted to the organizing committee for consideration. Forty-two were selected for presentation. The review was structured to include five technical sessions. Forty-two papers addressed topics in the five categories below: (1) hardware systems and components; (2) software systems; (3) integrated systems; (4) operations; and (5) supporting infrastructure

Robust Model Predictive Control Based on MRAS for Satellite Attitude Control System

In this paper, an improved robust model predictive controller (RMPC) is proposed based on model reference adaptive system (MRAS). In this algorithm, using the MRAS a combinational RMPC controller for three degree freedom satellite is designed such that the effect of moment of inertia uncertainty and external disturbance is compensated on the stability and performance of closed loop system. Control law is a state feedback which its gain is obtained by solving a convex optimization problem subject to several linear matrix inequalities (LMIs). To avoid the actuators saturation an input constraint is incorporated as LMI in the mentioned optimization problem. In addition to, using the MRAS system the effect of input disturbance is rejected on the system.The advantages of this algorithm are needless to exact information from system’s model, robustness against model uncertainties and external disturbance. Results from the simulation of the system with the proposed algorithm are presented and compared to generalized incremental model predictive control (GIPC). The results show that the suggestive controller is more robust than the GIPC method.DOI:http://dx.doi.org/10.11591/ijece.v4i1.496

Nonlinear Robust Neural Control with Applications to Aerospace Vehicles

Nonlinear control has become increasingly more used over the last few decades, mainly due to the research and development of better analysis tools, that can simulate real-world problems, which are almost always, nonlinear. Nonlinear controllers have the advantage of being more accurate and efficient when dealing with complex scenarios, such as orbit control, satellite rendezvous, or attitude control, compared to linear ones. However, common nonlinear control techniques require having a high-fidelity model, which is often not the case, thereby limiting their use. Additionally, rapid advancements in the field of machine learning have raised the possibility of using tools like neural networks to learn the dynamics of nonlinear systems in an effort to compute control inputs without the need to solve the highly complex mathematical equations that some nonlinear controllers require to solve, in real-time, therefore bypassing the need of higher computational power, which can reduce costs and weight, in space missions. This dissertation will focus on the development of a neural controller based on H8 pseudolinear control, to be applied to the satellite attitude control problem, as well as the satellite orbit control problem. The resulting controller is proven to be robust when dealing with important disturbances that are relevant in space missions, due to being trained using H8 controller data. Moreover, since the original controller is pseudolinear, the neural controller can capture the nonlinearities that exist in the equations of motion as well as in the attitude dynamics equations.Nas últimas décadas, o controlo não-linear tem sido cada vez mais utilizado, maioritariamente devido ao desenvolvimento de melhores ferramentas de análise, utilizadas para a simulação problemas reais, que tendem a ser não-lineares. Os controladores não-lineares têm a vantagem de serem mais precisos e eficientes quando utilizados em situações complexas, como controlo orbital, rendezvous de satélites, e controlo de atitude, comparados com controladores lineares. No entanto, as técnicas comuns de controlo não-linear requerem o uso de modelos com alto grau de fidelidade, o que muitas vezes não é o caso, limitando assim a sua utilização. Além disso, os rápidos avanços no campo de machine learning levantaram a possibilidade de utilizar ferramentas como redes neuronais para aprender a dinâmica de sistemas não lineares, numa tentativa de poder computar as entradas de controlo sem a necessidade de resolver as equações matemáticas altamente complexas que alguns controladores não lineares necessitam que sejam resolvidas, em tempo real, contornando assim a necessidade de maior potência computacional, que pode reduzir custos e peso, em missões espaciais. Esta dissertação focar-se-á no desenvolvimento de um controlador neuronal, baseado em controlo pseudolinear por H8, com o intuito de ser aplicado no problema de controlo orbital, bem como no problema de controlo de atitude. O controlador resultante provou ser robusto ao lidar com perturbações importantes, relevantes em missões espaciais, devido ao facto de ter sido treinado usando dados do controlador H8. Além disso, como o controlador original é pseudolinear, o controlador neuronal pode captar as dinâmicas não lineares que existem nas equações de movimento, bem como nas equações da dinâmica de atitude

Integrated Optimal and Robust Control of Spacecraft in Proximity Operations

With the rapid growth of space activities and advancement of aerospace science and technology, many autonomous space missions have been proliferating in recent decades. Control of spacecraft in proximity operations is of great importance to accomplish these missions. The research in this dissertation aims to provide a precise, efficient, optimal, and robust controller to ensure successful spacecraft proximity operations. This is a challenging control task since the problem involves highly nonlinear dynamics including translational motion, rotational motion, and flexible structure deformation and vibration. In addition, uncertainties in the system modeling parameters and disturbances make the precise control more difficult. Four control design approaches are integrated to solve this challenging problem. The first approach is to consider the spacecraft rigid body translational and rotational dynamics together with the flexible motion in one unified optimal control framework so that the overall system performance and constraints can be addressed in one optimization process. The second approach is to formulate the robust control objectives into the optimal control cost function and prove the equivalency between the robust stabilization problem and the transformed optimal control problem. The third approach is to employ the è-D technique, a novel optimal control method that is based on a perturbation solution to the Hamilton-Jacobi-Bellman equation, to solve the nonlinear optimal control problem obtained from the indirect robust control formulation. The resultant optimal control law can be obtained in closedorm, and thus facilitates the onboard implementation. The integration of these three approaches is called the integrated indirect robust control scheme. The fourth approach is to use the inverse optimal adaptive control method combined with the indirect robust control scheme to alleviate the conservativeness of the indirect robust control scheme by using online parameter estimation such that adaptive, robust, and optimal properties can all be achieved. To show the effectiveness of the proposed control approaches, six degree-offreedom spacecraft proximity operation simulation is conducted and demonstrates satisfying performance under various uncertainties and disturbances

Reference governors: Theoretical Extensions and Practical Applications.

As systems become downsized and operate at the limits of performance, control systems must be designed to ensure that system state and control constraints are satisfied; however, conventional control schemes are often designed without taking constraints into account. Reference governors and the related, more flexible, extended command governors are add-on, constraint enforcement schemes that modify reference signals to conventionally designed, closed-loop systems for the purpose of enforcing output constraints. The focus of this dissertation is on theoretical and methodological extensions of reference and extended command governors, and on their practical applications. Various theoretical results are presented. The first is the development of reduced-order reference and extended command governors, which enables constraint enforcement schemes using simplified models. The second, related development is that of reference governors for decentralized systems that may or may not communicate over a network. The third considers command governors with penalty functions that are used to enforce prioritized sets of constraints, as well as reference governors that are applied to a sequence of prioritized references. The fourth considers the often overlooked case of applying reference governors to linear systems subject to nonlinear constraints; various formulations of constraints are considered, including quadratic constraints and mixed logical-dynamic constraints. The final theoretical development considers using contractive sets to design reference governors for systems with time-varying reference inputs or subject to time-dependent constraints. Numerical simulations are used throughout to illustrate the theoretical advances. The design of reference governor schemes for three systems arising in practical applications is also presented. The first scheme enforces compressor surge constraints for turbocharged gasoline engines, ensuring that the compressor does not surge. The second scheme is designed for an airborne wind energy system that is subject to various flight constraints including constraints on altitude and angle of attack. The third and final scheme is designed for the constrained control of spacecraft attitude, whose discrete-time dynamics evolve on the configuration space SO(3). In the case of the first application, experimental vehicle results are reported that show successful avoidance of surge. For the other two applications, nonlinear model simulation results are reported that show enforcement of system constraints.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113518/1/kalabic_1.pd