A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints

Accurate estimation of parameters is paramount in developing high-fidelity models for complex dynamical systems. Model-based optimal experiment design (OED) approaches enable systematic design of dynamic experiments to generate input-output data sets with high information content for parameter estimation. Standard OED approaches however face two challenges: (i) experiment design under incomplete system information due to unknown true parameters, which usually requires many iterations of OED; (ii) incapability of systematically accounting for the inherent uncertainties of complex systems, which can lead to diminished effectiveness of the designed optimal excitation signal as well as violation of system constraints. This paper presents a robust OED approach for nonlinear systems with arbitrarily-shaped time-invariant probabilistic uncertainties. Polynomial chaos is used for efficient uncertainty propagation. The distinct feature of the robust OED approach is the inclusion of chance constraints to ensure constraint satisfaction in a stochastic setting. The presented approach is demonstrated by optimal experimental design for the JAK-STAT5 signaling pathway that regulates various cellular processes in a biological cell.Comment: Submitted to ADCHEM 201

Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints

This paper presents a stochastic model predictive control approach for nonlinear systems subject to time-invariant probabilistic uncertainties in model parameters and initial conditions. The stochastic optimal control problem entails a cost function in terms of expected values and higher moments of the states, and chance constraints that ensure probabilistic constraint satisfaction. The generalized polynomial chaos framework is used to propagate the time-invariant stochastic uncertainties through the nonlinear system dynamics, and to efficiently sample from the probability densities of the states to approximate the satisfaction probability of the chance constraints. To increase computational efficiency by avoiding excessive sampling, a statistical analysis is proposed to systematically determine a-priori the least conservative constraint tightening required at a given sample size to guarantee a desired feasibility probability of the sample-approximated chance constraint optimization problem. In addition, a method is presented for sample-based approximation of the analytic gradients of the chance constraints, which increases the optimization efficiency significantly. The proposed stochastic nonlinear model predictive control approach is applicable to a broad class of nonlinear systems with the sufficient condition that each term is analytic with respect to the states, and separable with respect to the inputs, states and parameters. The closed-loop performance of the proposed approach is evaluated using the Williams-Otto reactor with seven states, and ten uncertain parameters and initial conditions. The results demonstrate the efficiency of the approach for real-time stochastic model predictive control and its capability to systematically account for probabilistic uncertainties in contrast to a nonlinear model predictive control approaches.Comment: Submitted to Journal of Process Contro

Stability for Receding-horizon Stochastic Model Predictive Control

A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems with arbitrary time-invariant probabilistic uncertainties and additive Gaussian process noise. Closed-loop stability of the SMPC approach is established by appropriate selection of the cost function. Polynomial chaos is used for uncertainty propagation through system dynamics. The performance of the SMPC approach is demonstrated using the Van de Vusse reactions.Comment: American Control Conference (ACC) 201

An Architectural Style for Ajax

A new breed of web application, dubbed AJAX, is emerging in response to a limited degree of interactivity in large-grain stateless Web interactions. At the heart of this new approach lies a single page interaction model that facilitates rich interactivity. We have studied and experimented with several AJAX frameworks trying to understand their architectural properties. In this paper, we summarize three of these frameworks and examine their properties and introduce the SPIAR architectural style. We describe the guiding software engineering principles and the constraints chosen to induce the desired properties. The style emphasizes user interface component development, and intermediary delta-communication between client/server components, to improve user interactivity and ease of development. In addition, we use the concepts and principles to discuss various open issues in AJAX frameworks and application development.Comment: 2nd revision: references ordered, images resized, typo

Hybrid DOM-Sensitive Change Impact Analysis for JavaScript

JavaScript has grown to be among the most popular programming languages. However, performing change impact analysis on JavaScript applications is challenging due to features such as the seamless interplay with the DOM, event-driven and dynamic function calls, and asynchronous client/server communication. We first perform an empirical study of change propagation, the results of which show that the DOM-related and dynamic features of JavaScript need to be taken into consideration in the analysis since they affect change impact propagation. We propose a DOM-sensitive hybrid change impact analysis technique for Javascript through a combination of static and dynamic analysis. The proposed approach incorporates a novel ranking algorithm for indicating the importance of each entity in the impact set. Our approach is implemented in a tool called Tochal. The results of our evaluation reveal that Tochal provides a more complete analysis compared to static or dynamic methods. Moreover, through an industrial controlled experiment, we find that Tochal helps developers by improving their task completion duration by 78% and accuracy by 223%