We present a new optimal control approach to robust control of robot manipulators in the framework of Lin (1997). Due to the unknown load placed on a manipulator and the other uncertainties in the manipulator dynamics, it is important to design a robust control law that will guarantee the performance of the manipulator under these uncertainties. To solve this robust control problem, we first translate the robust control problem into an optimal control problem, where the uncertainties are reflected in the performance index. We then use the optimal control approach to solve the robust control problem. We show that the solution to the optimal control problem is indeed a solution to the robust control problem. We illustrate this approach using a two-joint SCARA type robot, whose robust control is obtained by solving an algebraic Riccati equation.

We propose a model for the interframe correspondences existing between pixels of an image sequence. These correspondences form the elements of a field called the motion field. In our model, spatial neighborhoods of motion elements are related based on a generalization of autoregressive (AR) modeling of the time-series. We also propose a joint spatio-temporal model by including spatial neighborhoods of pixel intensities in the motion model. A fundamental difference of our approach with most previous approaches to modeling motion is in basing our model on concepts from statistical signal processing. The developments in this paper give rise to the promise of extending well-understood tools of signal processing (e.g., filtering) to the analysis and processing of motion fields. Simulation results presented show the performance of our models in interframe prediction; specifically, on average the motion model performs 29% better in terms of the mean squared error energy over a commonly used pel-recursive approach. The spatio-temporal model improves the prediction efficiencies by 8% over the motion model. Our model can also be used to obtain estimates of the optical flow field as the simulations demonstrate.

The authors consider a prototype-based binary image classifier that makes comparisons based on blurred representations of the images. The blurring induces a metric on the space of all images that varies continuously under continuous deformation of the image plane. This blurred representation is suitable for direct implementation of a nearest-neighbor classifier. However, it is still desirable to have a representation which is invariant under certain spatial deformations, such as rotation, translation, and scaling of the image plane. A representation which is invariant under these transformation is produced by transforming an input to a local minimum of its distance from each prototype simultaneously. These minima are found by performing a gradient descent on an appropriate error surface over the transformation parameters. The error functional is the L/sub 2/-norm of the difference between the blurred prototype and the blurred input. The resulting classifier makes more efficient use of prototypes than does the nearest-neighbor classifier.>