Gordon Lee

Frequency Domain State-Space System Identification

This paper presents an algorithm for identifying state-space models of linear systems from frequency response data. A matrix-fraction description of the transfer function is employed to curve-fit the frequency response data, using the least-squares method. The parameters of the matrix-fraction representation are then used to construct the Markov parameters of the system. Finally, state-space models are obtained through the Eigensystem Realization Algorithm using the Markov parameters. The main advantage of this approach is that the curve-fitting and the Markov-parameter-construction are linear problems which avoid the difficulties of non-linear optimization of other approaches. Another advantage is that it avoids windowing distortions associated with other frequency domain methods.

System requirements for planetary rovers

Mission designs to Mars as well as return missions to the Moon usually include initial unmanned exploration to obtain geological and environmental data necessary for future manned programs. An important component of these unmanned exploration missions is the autonomous or remotely operated planetary rover -- a wheeled and/or legged vehicle containing several data collection and processing units. Further manned missions will also include such vehicles to assist humans with exploration and in supporting life science functions. The planetary rover must satisfy several mission requirements including surveying the terrain, preparing the landing sites, loading and unloading components for base operations, and aiding in recovery of in-situ materials. As such, the design of such rovers require the synergy of several vehicle functions which must operate in a coordinated fashion. This paper presents some of the design issues and vehicle requirements, particularly those requiring sensor information, that need be addressed for this important planetary surface system. Further based on some of the designs presently being investigated, the issues of system sensor fusion and standardization of interfaces are discussed.

Stable State-Space System Identification from Frequency Domain Data

Due to possible distortion contained in frequency-domain data, system identification methods based on data-matching alone do not guarantee stable models. This paper presents a method of identifying a stable state-space model of a linear system from its frequency response data. It is an improvement and extension of previous work. The method first identifies a matrix-fraction description of the transfer function matrix of the system from data. Then, through forming a multi-variable observable or controllable canonical form, the method separates and replaces the unstable sub-system by a stable sub-system having approximately the same frequency response. Finally, it calculates the Markov parameters of the resulting model and obtains a state-space model through the Eigensystem Realization Algorithm.

Several recursive techniques for observer/Kalman filter system identification from data

This paper derives algorithms for identifying autoregressive models, with external input, of multi-input multi-output systems from data using a fast transversal filter or a least-squares lattice filter. The autoregressive models including external inputs are used to identify state-space models and the corresponding observer/Kalman filter gains of the system. The derivation is an extension of scalar autoregressive model approaches, modified to cope with multivariables, external inputs and an extra direct-influence term. Comparisons between the fast transversal filter, the least-squares lattice filter and the classical least-squares method are made in terms of complexity, computational cost and practical applications issues. A numerical example is included to illustrate the approach.

A discrete-time virtual passive control algorithm for large space systems

The virtual passive control approach has recently been shown to be an effective technique for robust compensation of linear as well as nonlinear systems, when the system is open-loop stable and behaves as a block second-order plant. Mechanical systems as well as current space structure designs possess this property whereby inertia, damping and stiffness parameters characterize the dynamics. Implementation of such controllers using digital systems may lead to instability, however, unless discretization issues are addressed. This paper presents a discrete-time representation of the Virtual Passive Approach and develops the robust controller structure for linear systems. The algorithm is then applied to a model of an actual experimental testbed; the test article is a ten-bay truss structure located at NASA Langleys Spacecraft Dynamics Branch. Results illustrate the robustness properties of the algorithm and expose issues which need to be addressed in future research activities.

ANALYSIS AND CONTROL OF FLEXIBLE SPACE STRUCTURES EMPLOYING DISTRIBUTED ACTUATORS AND SENSORS

A theory is developed that incorporates the piezoelectric effect into slewing flexible composite materials using classical laminate theory. Using the piezoelectric material as a modal sensors allows for placement of all of the poles of the system without the need for a state observer design. Pole placement is used on a numeric example involving a graphite epoxy beam and a DC motor. Critical damping of both the motor and beam are achieved.

A Fuzzy Controller for Space Manipulator Systems

In this paper, a robust robotic controller algorithm is developed using a fuzzy logic structure. A general rule base is formulated using quantization tables in conjunction with traditional PID control. This hybrid fuzzy controller has an advantage in that a broader range of uncertainties, including unknown initial conditions, may still be addressed and stabilized using this structure. An off-line tuning approach has been investigated using phase portraits to gain further insight into the algorithm. The approach has been tested in simulation on a threedegree of freedom revolute manipulator model as well as other system dynamics.

A Single-Axis Testbed for Slewing Control Experiments

In this paper, a simple single-axis testbed is described and initial experimental results are presented to illustrate collocated and non-collocated control for this structure. The testbed is made up of a pair of single-axis flexible beams attached to a dc servo motor. An optical encoder and strain gauges provide hub and beam position information, respectively. The system is driven by an IBM PC system; with a Cyber Research Inc. motor controller, a programmable digital filter processes position error information through user-selected gains and pole-zero configurations. A 25kHz data acquisition system provides the necessary interface between processor and motor. The control approaches currently being investigated include collocated PD control and non-collocated phase compensation.