Adaptive variable structure tracking control for constrained robots

The problem of controller design for constrained robots with the consideration of computational efficiency is addressed. An efficient adaptive variable structure control algorithm based on a reduced dynamics formulation, is presented for trajectory tracking of an end-effector on a constrained surface with specified constraint forces. It is shown that the objective can be achieved without exact knowledge of robot dynamics and on-line calculation of nonlinear dynamic functions. The control algorithm is constructed with at most three control parameters to be adjusted adaptively, and that number is determined independently of the number of degrees of freedom of the robotic manipulators. A numerical example is presented to illustrate the developed method     

Adaptive model architecture and extended Kalman-Bucy filters

In radar systems, extended Kalman-Bucy filters (EKBFs) are used to estimate state vectors of objects in track. Filter models accounting for fundamental aerodynamic forces on reentry vehicles are well known. A general model structure accommodating the dynamics of reentry vehicles in both exoatmospheric and endoatmospheric flight is presented. The associated EKBFs for these various models are described and the resulting associated parameter estimation and identification problems are discussed. The effects of position, velocity, drag, and aerodynamic lift are described within a nested set of EKBF models     

Evaluating a multiple-hypothesis multitarget tracking algorithm

The performance evaluation of multiple-hypothesis, multitarget tracking algorithm is presented. We are primarily interested in target-detection/track-initiation capabilities as measures of performance. Through Monte Carlo simulations, a multiple-hypothesis tracking algorithm was evaluated in terms of 1) probability of establishing a track from target returns and 2) false track density. A radar was chosen as the sensor, and a general multiple-hypothesis, multitarget tracking algorithm was used in the Monte Carlo simulations. The simulation results predict the probability of establishing a track from returns of a target as well as the false track density per scan volume per unit time. The effects of the target radar cross section and the radar power, measured through the mean signal-to-noise ratio (SNR) were studied, as were the effects of detection threshold and track quality threshold. Computational requirements were also investigated     

A recursive multiple model approach to noise identification

Correct knowledge of noise statistics is essential for an estimator or controller to have reliable performance. In practice, however, the noise statistics are unknown or not known perfectly and thus need to be identified. Previous work on noise identification is limited to stationary noise and noise with slowly varying statistics only. An approach is presented here that is valid for nonstationary noise with rapidly or slowly varying statistics as well as stationary noise. This approach is based on the estimation with multiple hybrid system models. As one of the most cost-effective estimation schemes for hybrid system, the interacting multiple model (IMM) algorithm is used in this approach. The IMM algorithm has two desirable properties: it is recursive and has fixed computational requirements per cycle. The proposed approach is evaluated via a number of representative examples by both Monte Carlo simulations and a nonsimulation technique of performance prediction developed by the authors recently. The application of the proposed approach to failure detection is also illustrated     

Synergism of binocular and motion stereo for passive ranging

Range measurements to objects in the world relative to mobile platforms such as ground or air vehicles are critical for visually aided navigation and obstacle detection/avoidance. An approach is presented that consists of a synergistic combination of two types of passive ranging method: binocular stereo and motion stereo. We show a new way to model the errors in binocular and motion stereo in conjunction with an inertial navigation system (INS) and derive the appropriate Kalman filter to refine the estimates from these two stereo ranging techniques. We present results using laboratory images that show that refined estimates can be optimally combined to give range values which are more accurate than any one of the individual estimates from binocular and motion stereo. By incorporating a blending filter, the approach has the potential of providing accurate, dense range measurements for all the pixels in the field of view (FOV)     

Data fusion of decentralized local tracker outputs

An approach for fusing offboard track-level data at a central fusion node is presented. The case where the offboard tracker continues to update its local track estimate with measurement and system dynamics models that are not necessarily linear is considered. An algorithm is developed to perform this fusion at a central node without having access to the offboard measurements, their noise statistics, or the location of the local estimator. The algorithm is based on an extension of results that were originally established for linear offboard trackers. A second goal of this work is to develop an inequality constraint for selecting the proper sampling interval for the incoming state estimates to the fusion node. This interval is selected to allow use of conventional Kalman filter algorithms at the fusion node without suffering error performance degradation due to processing a correlated sequence of track state estimates     

A new method for finding electromagnetic emitter location

The position of a source of radiation is often obtained from bearing data, taken over an interval of time, and combining it with navigation data. A new method using total least squares (TLS) has been suggested for the accurate estimation of an emitter location when bearing observation errors are random. Further, an iterative two-stage approach involving TLS and Kalman filtering is developed for accurate estimation of the emitter location when bearing observation errors are an algebraic sum of random and systematic errors. The elegance and efficacy of the proposed methods are illustrated through digital computer simulated examples     

Analytical solution for continuous-time Kalman tracking filterswith colored measurement noise in frequency domain

New expressions are given for analytical solutions to the steady-state Kalman gains of the two-state exponentially correlated velocity (ECV) and the three-state exponentially correlated acceleration (ECA) tracking filters with position measurements by using spectral factorization method. The measurement colored noise model is characterized by a correlation time 1/λ. The vehicle oscillations such as wind-induced-bending is also considered in the modeling of the system which leads to the most generalized state transition matrix     

Comment on “Inversion of all principal submatrices of amatrix”

The paper by Koc and Chen (1994) includes new formulas for computational complexity, but the method itself is already known and has dubious numerical stability. A poorly conditioned example is shown