Design and implementation of robust decentralized control laws for the ACES structure at Marshall Space Flight Center

Many large space system concepts will require active vibration control to satisfy critical performance requirements such as line-of-sight accuracy. In order for these concepts to become operational it is imperative that the benefits of active vibration control be practically demonstrated in ground based experiments. The results of the experiment successfully demonstrate active vibration control for a flexible structure. The testbed is the Active Control Technique Evaluation for Spacecraft (ACES) structure at NASA Marshall Space Flight Center. The ACES structure is dynamically traceable to future space systems and especially allows the study of line-of-sight control issues

High performance, accelerometer-based control of the Mini-MAST structure at Langley Research Center

Many large space system concepts will require active vibration control to satisfy critical performance requirements such as line of sight pointing accuracy and constraints on rms surface roughness. In order for these concepts to become operational, it is imperative that the benefits of active vibration control be shown to be practical in ground based experiments. The results of an experiment shows the successful application of the Maximum Entropy/Optimal Projection control design methodology to active vibration control for a flexible structure. The testbed is the Mini-Mast structure at NASA-Langley and has features dynamically traceable to future space systems. To maximize traceability to real flight systems, the controllers were designed and implemented using sensors (four accelerometers and one rate gyro) that are actually mounted to the structure. Ground mounted displacement sensors that could greatly ease the control design task were available but were used only for performance evaluation. The use of the accelerometers increased the potential of destabilizing the system due to spillover effects and motivated the use of precompensation strategy to achieve sufficient compensator roll-off

Aplicación de Muestreo basado en Modelos de Control Predictivo a un Vehículo Autónomo Subacuático

Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the vehicle can feasibly traverse. As a result, Sampling-Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling-based motion planning with model predictive control (MPC) while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization (e.g., A*) in place of standard numerical optimization. This formulation of MPC readily applies to nonlinear systems and avoids the local minima which can cause a vehicle to become immobilized behind obstacles. The SBMPC algorithm is applied to an AUV in a 2D cluttered environment and an AUV in a common local minima problem. The algorithm is then used on a full kinematic model to demonstrate the benefits.Los UUVs pueden ser utilizados para realizar tareas difíciles en ambientes atiborrados de reflexiones de onda tales como muelles y puertos. Sin embargo, dado que los UUVs tienen dinámicas áltamente acopladas y no lineales, la programación de movimiento y el control pueden ser complicados cuando son realizadas tareas complejas. Introducir modelos en el proceso de programación del movimiento puede producir patrones que el vehículo puede cruzar de manera viable. Como resultado, el modelo de control predictivo basado en muestreo (SBMPC, por sus siglas en inglés) es propuesto para generar simultáneamente entradas de control y trayectorias de sistema para un vehículo autónomo sumergible. El algoritmo combina los beneficios de la planeación de movimiento con el control predictivo de modelo (MPC), mientras que evita algunos de los mayores obstáculos que enfrentan tanto los algoritmos basados en muestreo como el tradicional MPC. El método está basado en el muestreo (es decir, discretización) del espacio de entrada en cada período de muestreo e implementación de una optimización dirigida a objetivos (por ejemplo, A*) en lugar de la optimización numérica estándar. Esta formulación del MPC aplica fácilmente a los sistemas no lineales y evita el mínimo local, el cual puede ocasionar que un vehículo quede inmóvil detrás de los obstáculos. El algoritmo SBMPC se aplica a un UAV en un ambiente cargado de reflexiones de onda y a un UAV en un problema de mínimo común local. El algoritmo es luego usado en un modelo cinemático completo para demostrar los beneficios de aplicar restricciones y un modelo en programación de movimiento

Robust Stability Analysis Using the Small Gain, Circle, Positivity, and Popov Theorems: A Comparative Study

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57852/1/SmallGainTCST1993.pd

An Input Normal Form Homotopy for the L2 Optimal Model Order Reduction Problem

In control system analysis and design, finding a reduced order model, optimal in the L-squared sense, to a given system model is a fundamental problem. The problem is very difficult without the global convergence of homotopy methods, and a homotopy based approach has been proposed. The issues are the number of degrees of freedom, the well posedness of the finite dimensional optimization problem, and the numerical robustness of the resulting homotopy algorithm. A homotopy algorithm based on the input normal form characterization of the reduced order model is developed here and is compared with the homotopy algorithms based on Hyland and Bernstein's optimal projection equations. The main conclusions are that the input normal form algorithm can be very efficient, but can also be very ill conditioned or even fail

A Homotopy Algorithm for the Combined H-squared/H-to Infinity Model Reduction Problem

The problem of finding a reduced order model, optimal in the H-squared sense, to a given system model is a fundamental one in control system analysis and design. The addition of a H-to infinity constraint to the H-squared optimal model reduction problem results in a more practical yet computationally more difficult problem. Without the global convergence of probability-one homotopy methods the combined H-squared/H-to infinity model reduction problem is difficult to solve. Several approaches based on homotoppy methods have been proposed. The issues are the number of degrees of freedom, the well posedness of the finite dimensional optimization problem, and the numerical robustness of the resulting homotopy algorithm. Homotopy algorithms based on two formulations - input normal form; Ly, Bryson, and Cannon's 2 x 2 block parametrization - are developed and compared here

A homotopy algorithm for digital optimal projection control GASD-HADOC

The linear-quadratic-gaussian (LQG) compensator was developed to facilitate the design of control laws for multi-input, multi-output (MIMO) systems. The compensator is computed by solving two algebraic equations for which standard closed-loop solutions exist. Unfortunately, the minimal dimension of an LQG compensator is almost always equal to the dimension of the plant and can thus often violate practical implementation constraints on controller order. This deficiency is especially highlighted when considering control-design for high-order systems such as flexible space structures. This deficiency motivated the development of techniques that enable the design of optimal controllers whose dimension is less than that of the design plant. A homotopy approach based on the optimal projection equations that characterize the necessary conditions for optimal reduced-order control. Homotopy algorithms have global convergence properties and hence do not require that the initializing reduced-order controller be close to the optimal reduced-order controller to guarantee convergence. However, the homotopy algorithm previously developed for solving the optimal projection equations has sublinear convergence properties and the convergence slows at higher authority levels and may fail. A new homotopy algorithm for synthesizing optimal reduced-order controllers for discrete-time systems is described. Unlike the previous homotopy approach, the new algorithm is a gradient-based, parameter optimization formulation and was implemented in MATLAB. The results reported may offer the foundation for a reliable approach to optimal, reduced-order controller design

Regulation of the SCOLE configuration

Studies were performed to determine location for proof mass actuators, if a significant reduction in the number of sensors would work, and to design a control law to meet requirements for line of sight error and actuators. Conclusions are drawn and briefly discussed

Application of maximum entropy optimal projection design synthesis to a benchmark problem

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76995/1/AIAA-20955-739.pd