66 research outputs found
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
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