The design objectives for these problems are system reliability and life cycle costs.
CHAPTER 1
Stochastic System Design
For these systems, stochastic design problems are usually related to the expected life cycle cost of a system, or its reliability, defined in terms of "failure". Approaches to transforming the latter problem into one of the first two have been suggested.
Structural Control
Of these different classes, active systems provide the greatest improvement in structural response under dynamic excitations. Thus, the performance of the controlled system is the only metric by which the quality of the control design should be judged.
Overview of the Thesis
Monte Carlo simulation and FORM/SORM, respectively, have been used in these studies for reliability evaluations, but little attention has been paid to the optimization complexity of the associated controller design. An analytical approach, motivated by the simplicity of the models adopted, is discussed for the design problem.
CHAPTER 2
General Problem
Alternatively, a single prediction error can be considered for the performance of the improved system, which combines the errors that may exist for all individual components. Equivalently, h(φ,θ) can be regarded as a utility function (see for example Porter et al. 2004b)) that quantifies the preference for the different outputs of the system (answer z in Figure 2.1).
Reliability-Based Design
An equivalent expression can also be derived for the robust failure probability by explicitly considering the influence of the model prediction error between the responses of the actual system and the assumed model. For example, the definition of the limit state function together with relation (2.10) automatically leads to a specific relation between the assumed model and the actual system responses.
Controlled System Design
Special care is needed so that the former definition leads to a realistic relationship in the latter case, in the context of the specific application and the probabilistic model considered for the prediction error. For this design problem, an analytical approach, motivated by the simplicity of the models, will be discussed that provides useful insight into the characteristics of controller synthesis and allows direct comparison with other controller design methods proposed for such applications.
Probabilistic Seismic Ground Motion Model
In the context of the model description discussed in Section 2.1, the model parameter vector θs=[M, r, Zw] consists of the seismological parameters, M and r, and the white noise sequence Zw. Finally, in the context of the model description discussed in Section 2.1, the vector of model parameters for the near-fault impulse model in (2.15) θs=[M, r, γp, vp, εf, εv] consists of the seismological parameters , M and r and impulse characteristics γp , vp, εf and εv.
CHAPTER 3
Linear Time Invariant System Model
The performance of the controlled system is assessed by the favorableness of the output response quantities ( )z t ∈\nz, which are assumed to be a linear combination of the state and control vectors. Thus, the uncertainty arising from the stochastic input can be easily passed analytically to the system output, in terms of the steady-state performance.
Estimation of Stochastic Integrals and Stochastic Optimization for Simple System Models
This methodology does not require high accuracy in identifying the location of the design points. The efficiency of this approach depends on the accuracy of the asymptotic approximation, especially close to the optimal solution.
Reliability Calculation for Linear Dynamic Systems
Then, vz+ can be expressed as the sum of the exceedance rates corresponding to each failure mode i. This temporal correction factor λzi is an adjustment to take into account the conditioning of the outcrossing rate νz+ on the absence of previous outcrosses, as in (3.15). For the calculation of the confidence integral by stochastic simulation using importance sampling, the accuracy required by Genz's procedure for the estimation of the outcrossing rate νz+(K|θk) at each sample θk is not high.
We are interested in the value of the integral and not in the value of the exit velocity νz+(K|θk), and therefore it is not necessary to calculate the latter with high accuracy.
Nominal Reliability-Based Controller Design
The optimization described by (3.29) is equivalent to the minimization of the stationary crossover rate νz+, and the dependence on the time duration T disappears,. Uniform scaling of these variances, that is, changing γ, changes the sensitivity of the crossover rate to each of them. As the thresholds increase uniformly, there is a reduction in the importance of the less frequent "failure" events, that is, the events that have smaller deviations.
Note that the optimal gain for multi-objective H2 control is independent of the scaling of these thresholds (it is a homogeneous problem).
Robust-Reliability Design with Fixed Time Duration
Unlike the particular parameter case, the choice of the duration, T, influences the design optimization. Duration T enters the problem as a sensitivity parameter that defines the relative importance of the higher order statistics. The magnitude of T for which the higher order moments become significant depends on the value of the outcrossing velocity νz+.
Thus, the controller optimization attempts to improve dynamic performance in areas of the model parameters for which the out-crossing rate is small.
Robust-Reliability Design with Uncertain Time Durations
Furthermore, the variation of T influences the optimal gain and the relative importance of the outcrossing speed statistics under optimal design. Except for short durations, there is some sensitivity of the optimal gain to the value of T. The difference in the magnitude of the out-crossing rate also affects the relative importance of its statistics in the optimization.
The performance improvement in this figure (y-axis) is defined as the ratio of the difference in failure probabilities corresponding to the nominal and robust designs respectively (performance difference), to the performance (failure probability) of the robust design.
Probabilistic Robustness for Minimum Variance Control Design The concept of probabilistically robust controller design is extended in this section to
In a probabilistic setting, the most appropriate quantification for H2 performance is AR, since the H2 metric is related to the average response of the system. This case corresponds to greater variation of the model parameters with respect to their mean value, i.e. to a higher degree of uncertainty. The variation of AR, WC and nominal H2 performance with respect to the feedback gain is shown in Figure 3.10.
This indicates that there is a small sensitivity to the specific probability distributions of the parametric model uncertainty for the mH2 performance for mean robustness quantification.
Concluding Remarks
Also, the optimal gains in this case are close to the nominal optimal gain and the optimal AR gain; this means that there is equivalence of performance quantification between all these design methods. A more thorough analytical approach is warranted, if possible, to enable in-depth theoretical comparisons and to extend the design methodology to complex controller structures. In terms of the proposed stochastic system design approach, the methodologies discussed in this chapter utilize the characteristics of the design problem to simplify the calculation of the performance target and solve the stochastic programming problem.
It was shown in that paper that this approach is sufficient when the focus is only on the structural system response, but cannot be extended to design problems where the reliability of the damper response must also be considered.
CHAPTER 4
Stochastic Subset Optimization
This term is also proportional, ignoring the normalization constants, to the mean value of the objective function in set I. The estimate for H(I) is equal to the ratio of the volume density of samples from π(φ) in sets I and Φ . A more detailed discussion on how the size of the allowed subsets affects SSO efficiency is in the next section.
The relative size of the admissible subsets I define (a) the resolution of φ* and (b) the accuracy information about ˆ ( )H I extracted from the samples of π(φ).
Implementation Issues and Guidelines
Proper selection of the geometric shape and size of the admissible sets is important for the effectiveness of SSO. Another important issue for the efficiency of SSO is the identification of the optimal sets within the class of admissible subsets chosen, that is, optimizing (4.18). The computational cost associated with these methods depends on (a) the number of parameters, nA, needed to characterize the subsets and (b) the description of the allowable values for these parameters.
This efficiency is highly dependent on the quality of the proposed PDFs selected for the generated Markov chains.
Stochastic Subset Optimization Algorithm
Based on these two quantities and the desired quality of identification (see next section), decide whether to (a) stop (or even increase N to obtain better precision of information about H Iˆ ˆ( )k ) or (b) continue with step k+ 1. This ratio expresses the difference in volume density of samples inside and outside the identified set Iˆk. It is also clear that with the development of the identification process in SSO, the reduction in the size of the identified subsets becomes larger and that the value of H Iˆ ˆ( )k is constantly increasing.
These patterns confirm the theoretical discussion presented in Section 4.2.2, assuming that as SSO identification progresses, regions of the design space with lower sensitivity to the objective function are approached.
Convergence Properties of Stochastic Subset Optimization
Starting from each of the original Na samples, [N/Na] samples are generated by the above process. The corresponding analytical characterization for this requirement depends on the shape of the search space. If this distance is greater than 1, the hypersphere in the transformed space is within the intersection of the hyperplanes, which means I⊂X1.
This is satisfied if the maximum distance of the ellipse (C4.1) from the origin is less than 1.
CHAPTER 5
Stochastic Optimization
It is more efficient to aim for a reduction in the relative importance of the estimation error. They are based on comparisons of design choices spread over large parts of the design space. However, they require significant computational efforts if the dimensionality of the design variables is high.
The weighting functions can be chosen according to the location of the interpolation points relative to the point at which the objective function is approximated.
Framework for Stochastic Optimization Using Stochastic Simulation
The efficiency of the stochastic optimization considered in the second stage is affected by (a) the size of the design space Φ defined by its volume VΦ, and, depending on the characteristics of the chosen algorithm, by (b) the initial point φ1 at which the algorithm is initialized, and (c) knowledge of the local behavior of the objective function in Φ. Also, it is found that the sensitivity of the objective function in relation to all components of φ is small. This allows, for example, for efficient normalization of the design space (in choosing step sizes) and choosing interpolating functions (for OAM).
Alternatively, hs(φi,θi) can be used as the loss function in the second stage of optimization.
CHAPTER 6
Optimal Reliability Design of a Base Isolation Protection System
The comparison between the mean value of the objective function in groups Φ and I is also significant. Increasing the number of samples simulated by π(φ) improves, as expected, the quality of the estimation: both the mean Pˆ (F φSSO|Θ) and the c.o.v. A comment about the effectiveness of the base insulation protection system in this example is warranted.
The design configuration corresponding to the minimum of the approach surface (center of the contours) is close to the optimal solution identified earlier.
Optimal Life-Cycle Cost-Based Retrofitting of a Four-Story Structure The retrofitting of a symmetric, four-story, office building with linear viscous dampers is
Next, Figure 6.11 shows the average cost for the sliding-sensitive (Figure 6.11(a)) and acceleration-sensitive (Figure 6.11(b)) components of the structure. Finally, Figure 6.12 plots the expected repair costs for each of the various damaged slip-sensitive assemblies. For the second stage of the optimization framework, the formulation of the objective function in (6.13) is adopted.
There are only minor changes in the distribution of the total costs over the various components.
