Analysis of Multimodal Systems

Bruckner, J. M. H.; Scott, Ronald W.; Rea, F. G.

doi:10.1109/taes.1973.309663

Cited by 11 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, all possible sequences over a sliding window are considered, and the preceding sequences are merged by using one Gaussian distribution just as above. Two special cases of this algorithm are given in Bruckner et al (1973) and Chang and Athans (1978), where the window size is one. The difference is just the model complexity.…”

Section: Algorithm 703 Gpbmentioning

confidence: 99%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

602

699

View full text Add to dashboard Cite

Section: Algorithm 703 Gpbmentioning

confidence: 99%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

602

699

View full text Add to dashboard Cite

“…Bruckner, Scott, and Rea [20] extended the adaptive algorithm of [17] to the switching of plant parameters in an air traffic control system.…”

Section: Adaptive Kalman Filteringmentioning

confidence: 99%

Active Control of Generalized Complex Modal Structures in a Stochastic Environment

Baumann¹,

Moose²,

VanLandigham³

et al. 1992

View full text Add to dashboard Cite

IOMS Aov ed0Pubic reporting burden for this colilection of information is estimated to Average I mour oer resporse. nciuaing the time for reviewing instructions. Searching existing data sourCe , gathering and maintaining the data needed, and completing and reviewing the collectiOn of incrinatiOn Send comments regarding this burden estimate or any other aspect of thys collection of information. including suggestion% for reducing tis Ourden to Washington Healdouarters Services. See Attachment ABSTRACT (Maximum 200 words)This report deals with the active control of heavily damped vibratory mechanical systems in a stochastic environment. The systems of interest are multiple input, multiple output (MIMO)structures that are modeled by generalized complex modes. The control objective is to suppress the effects of impulsive distur bances within a short period of time as well as to provide specified reduction of vibration due to ever-present Gaussian and nonGaussian stochastic disturbances.The team effortdescribed separatelyin this report can be viewed as three interactive parts:one group investigating the robust control issues; one, the nongaussian estimation problem and a third group working with a mechanical plate experiment, generating data and identification algorithms.The experimental model was constructed in the M. E. Department under DARPA funding. The robust control investigators used modern analysis and synthesis tools to incorporate the required performance directly into the design procedure. The approach to handle the nongaussian state estimation problem was in the use of an adaptive Gaussian sum state estimator. SUBJECT TERMS IWorking with realistic modal descriptions of vibratory mechanical systems, the technical objective was to design active damping controllers. The systems of interest are multiple input, multiple output (MIMO), heavily damped structures with complex modes. The control objective is to suppress the effects of impulsive disturbances within a short period of time as well as to E provide a specified reduction of vibration due to ever-present stochastic disturbances.Systems with complex modes cannot be decoupled exactly. One issue that was resolved was the impracticality of using a controller for an approximately decoupled system. Simulations showed that the desired amount of damping could not be achieved with this controller due to stability problems caused by the approximations. 3The technical issue regarding state estimation was to develop an optimal estimator for the system driven by non-gaussian stochastic signals. I (2) TECHNICAL APPROACHES: IThe team effort can be viewed as three interactive parts:one group investigating the robust control issues; one, the non-gaussian estimation problem and a third group working with a mechanical plate experiment, generating data and identification algorithms. The experimental model was constructed in the M.E. Department under DARPA funding. Our group interacted well with the M.E. group, sharing ideas and methods where relevant. IThe robust control ...

show abstract

“…For several decades, filtering algorithms with multiple models have been popular in the field of the estimation and identification [1–7]. Multiple model filtering algorithms become explicit and feasible by using the homogeneous Markov chain on the transition between models.…”

Section: Introductionmentioning

confidence: 99%

Re‐entry vehicle tracking with a new multiple model estimation applicable to highly non‐linear dynamics

Shin

2015

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Analysis of Multimodal Systems

Cited by 11 publications

References 4 publications

Adaptive Filtering and Change Detection

Adaptive Filtering and Change Detection

Active Control of Generalized Complex Modal Structures in a Stochastic Environment

Re‐entry vehicle tracking with a new multiple model estimation applicable to highly non‐linear dynamics

Contact Info

Product

Resources

About