Dean W. Sparks

Survey of Experiments and Experimental Facilities for Control of Flexible Structures

This paper presents a survey of ground experiments primarily conducted in the United States and U.S. facilities dedicated to the study of active control of flexible structures. The facilities are briefly described in terms of capability, configuration, size, and instruments. Topics on the experiments include vibration suppression, slewing control, and system identification. The experiments are listed in tables containing the experiments name, the responsible organization, a brief description of the test article configuration, and the actuator/sensor devices used in the experiment. Selected experiments will be further discussed to help illustrate the control problems. Some of the test facilities dedicated to ground testing of large space structures are discussed in more detail, to give the reader a better appreciation of ground-testin g work. Several research issues are mentioned, including real-time computer systems, test article suspension, and new actuator/sensor technology development.

Application of piezoelectric devices to vibration suppression

Embedded piezoelectric devices may be ideally suited for vibration control of space structures, which lack an inertial ground. When subjected to an input voltage, an embedded piezoelectric actuator changes its dimensions, which in turn generates a pair of forces exerted on adjacent structural members. From the direct piezoelectric effect, an embedded piezoelectric transducer generates an electric charge proportional to the structural dynamic response. In this paper, the implementation, testing, and modeling of an active truss structure with piezoelectric sensors and actuators are described. Linear quadratic Gaussian, second-order, and direct-rate feedback control schemes are designed to suppress the vibrations of the active structure. Simulation and test results are presented. It is shown that special model reduction considerations are required to achieve good correlation between test and analysis. Nomenclature The typical symbology for piezoelectric material properties are used in this paper. Except where noted, the piezoelectric variables are with respect to the standard piezoelectric material 1-2-3 Cartesian coordinate frame. The single, or first of the double, subscript denotes the direction of the applied/sensed electrical field. The second subscript represents the direction of the stress/strain in the piezoelectric material. The subscript r represents the radial direction, as measured in a cylindrical coordinate frame. A = cross-sectional area Ac - controller state matrix As - surface area B, BI = input matrix, /th input matrix Bc = controller input matrix

Design of neural networks for fast convergence and accuracy: dynamics and control

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Design of Neural Networks for Fast Convergence and Accuracy

A novel procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed to provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component spacecraft design changes and measures of its performance. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The design algorithm attempts to avoid the local minima phenomenon that hampers the traditional network training. A numerical example is performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Neural networks for rapid design and analysis

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

On the isolation of science payloads from spacecraft vibrations

The remote sensing of the Earths features from space requires precision pointing of scientific instruments. To this end, the NASA Langley Research Center has been involved in developing numerous controlled structures technologies. This paper describes one of the more promising technologies for minimizing pointing jitter, namely, payload isolation. The application of passive and active payload mounts for attenuation of pointing jitter of the EOS AM-1 spacecraft is discussed. In addition, analysis and ground tests to validate the performance of isolation mounts using a scaled dynamics model of the EOS AM-1 spacecraft are presented.

Application of piezoelectric devices to vibration suppression - From modeling and controller designs to implementation

Embedded piezoelectric devices may be ideally suited for vibration control of space structures, which lack an inertial ground. When subjected to an input voltage, an embedded piezoelectric actuator changes its dimensions, which in turn generates a pair of forces exerted on adjacent structural members. From the direct piezoelectric effect, an embedded piezoelectric transducer generates an electric charge proportional to the structural dynamic response. In this paper, the implementation, testing and modeling of an active truss structure consisting of piezoelectric sensors and actuators are described. Linear quadratic Gaussian (LQG), second-order, and direct rate feedback control schemes are designed to suppress the vibrations of the active structure. Simulation and test results are presented. It is shown that special model reduction considerations are required to achieve good correlation between test and analysis.

Optimal aircraft control upset recovery with and without component failures

Recovery of safe flight for a particular aircraft, in a specific initial adverse condition is examined, with and without a specific control failure. It is shown that under optimal circumstances, the adverse vehicle state could be corrected with little altitude loss. In addition, a control failure which proved fatal in practice was corrected, assuming independently acting spoilers. Generalizing these results to a comprehensive set of upset scenari will be time consuming, but could offer valuable heuristic piloting insights.

Fault Accommodation in Control of Flexible Systems

New synthesis techniques for the design of fault accommodating controllers for e exible systems are developed. Three robust control design strategies, static dissipative, dynamic dissipative, and π-synthesis, are used in the approach. The approach provides techniques for designing controllers that maximize, in some sense, the tolerance of the closed-loop system against faults in actuators and sensors, while guaranteeing performance robustness at a specie ed performance level, measured in terms of the proximity of the closed-loop poles to the imaginary axis (the degree of stability ). For dissipative control designs, nonlinear programming is employed to synthesize the controllers, whereas in π-synthesis, the traditional D‐K iteration is used. To demonstrate the feasibility of the proposed techniques, they are applied to the control design of a structural model of a e exible laboratory test structure.