Scott W. Greeley

Robust controller synthesis using the maximum entropy design equations

This note presents an application of the optimality conditions obtained in [1] for dynamic compensation in the presence of state-, control-, and measurement-dependent noise. By solving these equations, which represent a fundamental generalization of standard steady-state LQG theory, a series of increasingly robust control designs is obtained for the example considered in [2].

The optimal projection equations for fixed-order, sampled-data dynamic compensation with computation delay

For an LQG-type sampled-data regulator problem which accounts for computational delay and utilizes an averaging A/D device, the equivalent discrete-time problem is shown to be of increased order due to the inclusion of delayed measurement states. The optimal projection equations for reduced-order, discrete-time compensation are applied to the augmented problem to characterize low-order controllers. The design results are illustrated on a 10th-order flexible beam example.

The optimal projection equations for reduced-order, discrete-time modelling, estimation and control

The optimal projection equations derived previously for reduced-order, continuous-time modelling, estimation and control are developed for the discrete-time case. The design equations are presented in a concise and unified manner to facilitate their accessibility for the development of numerical algorithms for practical applications. As in the continuous-time case, the standard Kalman filter and linear-quadratic-Gaussian results are immediately obtained as special cases of the estimation and control results.

Reduced-order compensation: LQG reduction versus optimal projection using a homotopic continuation method

In previous work, six methods for designing reduced order controllers were compared using an example problem. This paper considers the Optimal Projection method as solved by a recently developed homotopy continuation algorithm. Design results obtained by the different methods for forty-two design cases are compared with respect to closed-loop stability. Only the optimal projection method produced stable designs for all cases.

Active vibration suppression for the mast flight system

Active vibration suppression of a large flexible space structure is addressed. The system (experimental test bed), performance requirements, and system simulations and models are described. The structures is a 60-m truss beam attached to the Shuttle orbiter. A baseline control system is required to provide 5% structural damping for the first ten structural (flexible) modes of the truss beam. The control design approach used to achieve the damping is a decentralized velocity feedback type. Collocated actuator and sensor locations are given, with details of the model for the proof-mass actuating device, the linear DC motor.<<ETX>>

Autonomous system identification and control of MACE II using the Frequency Domain Expert algorithm

The Frequency Domain Expert (FDE) adaptive identification and control design algorithm will be applied to the Middeck Active Control Experiment (MACE) during a re-flight of the experiment. FDE was recently developed by the authors to address the need for an algorithm that requires only local measurement, has predictable convergence properties, and yields performance in terms easily understood by controls system engineers. Ground testing to date has produced FDE controllers that reduce line-of-sight error by more than 22 dB, only a few dB less than the best performance attained via offline, fixed-gain LQG controllers during the original MACE flight.

Active Damping Control Design for the Mast Flight System

Design and development of the Mast Flight System for the COFS (Control of Flexible Structures) program for NASA is currently underway. An active damping controller is required to provide five percent damping for the first ten structural modes of a sixty meter truss beam structure. Two types of controller design methodologies are presented to achieve the required five percent damping. The first is an LQG controller and the second is a positive-real decentralized velocity feedback type, which is the system baseline controller design. The system modelling details are also presented which includes the models for the truss beam and the colocated actuators and sensors.

In flight autonomous system identification and control of MACE II using the frequency domain expert algorithm

The Frequency Domain Expert (FDE) adaptive identification and control design algorithm will be applied to the M iddeck Active Control Experiment (MACE) during a re-flight of the experiment. FDE was recently developed by the authors to address the need for an algorithm that requires only local measurement, has predictable convergence properties, and yields performance in terms easily understood by controls system engineers. Ground testing to date has produced FDE controllers that reduce line-of-sight error by more than 22 dB, only a few dB less than the best performance attained via offline, fixed-gain LQG controllers during the original MACE flight.