A Statistical Methodology of Designing Controllers for Minimum Sensitivity of Parameter Variations

Tsai, Kenneth C.; Auslander, David M.

doi:10.1115/1.3152662

Cited by 8 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Probabilistic synthesis of control systems is a natural adjunct to probabilistic analysis; the random or randomized search is a dual to Monte Carlo evaluation. Building on [51], random-search methods of finding control system gains are explored in [6,60,55]. There are similarities to directed searches that minimize multi-objective cost functions [48], to parameter-space methods [50,1], and to fine-tuning of control gains by search [3].…”

Section: Stochastic Robustness Design(srd)mentioning

confidence: 99%

Probabilistic Control of Nonlinear Uncertain Systems

Wang

Stengel

Probabilistic and Randomized Methods for Design Under Uncertainty

View full text Add to dashboard Cite

Robust controllers for nonlinear systems with uncertain parameters can be reliably designed using probabilistic methods. In this chapter, a design approach based on the combination of stochastic robustness and dynamic inversion is presented for general systems that have a feedback-linearizable nominal system. The efficacy of this control approach is illustrated through the design of flight control systems for a hypersonic aircraft and a highly nonlinear, complex aircraft model. The proposed stochastic robust nonlinear control explores the direct design of nonlinear flight control logic; therefore the final design accounts for all significant nonlinearities in the aircraft's high-fidelity simulation model. Monte Carlo simulation is used to estimate the likelihood of closed-loop system instability and violation of performance requirements subject to variations of the probabilistic system parameters. The stochastic robustness cost function is defined in terms of the probabilities that design criteria will not be satisfied. We use randomized algorithms, in particular genetic algorithms, to search the design parameters of the parameterized controller with feedback linearization structure. The design approach is an extension of earlier methods for probabilistic robust control of linear systems. Prior results are reviewed, and the nonlinear approach is presented.

show abstract

Section: Stochastic Robustness Design(srd)mentioning

confidence: 99%

Probabilistic Control of Nonlinear Uncertain Systems

Wang

Stengel

Probabilistic and Randomized Methods for Design Under Uncertainty

View full text Add to dashboard Cite

show abstract

Model parameter estimation and analysis: Understanding parametric structure

Watanabe

Auslander

et al. 1994

Ann Biomed Eng

View full text Add to dashboard Cite

We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.

show abstract

Parameter uncertainty and interaction in complex environmental models

Spear¹,

Grieb²,

Shang³

1994

Water Resources Research

146

View full text Add to dashboard Cite

Recently developed models for the estimation of risks arising from the release of toxic chemicals from hazardous waste sites are inherently complex both structurally and parametrically. To better understand the impact of uncertainty and interaction in the high‐dimensional parameter spaces of these models, the set of procedures termed regional sensitivity analysis has been extended and applied to the groundwater pathway of the MMSOILS model. The extension consists of a tree‐structured density estimation technique which allows the characterization of complex interaction in that portion of the parameter space which gives rise to successful simulation. Results show that the parameter space can be partitioned into small, densely populated regions and relatively large, sparsely populated regions. From the high‐density regions one can identify the important or controlling parameters as well as the interaction between parameters in different local areas of the space. This new tool can provide guidance in the analysis and interpretation of site‐specific application of these complex models.

show abstract

A Statistical Methodology of Designing Controllers for Minimum Sensitivity of Parameter Variations

Cited by 8 publications

References 0 publications

Probabilistic Control of Nonlinear Uncertain Systems

Probabilistic Control of Nonlinear Uncertain Systems

Model parameter estimation and analysis: Understanding parametric structure

Parameter uncertainty and interaction in complex environmental models

Contact Info

Product

Resources

About