19,208 research outputs found
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
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Learning-based control algorithms require data collection with abundant
supervision for training. Safe exploration algorithms ensure the safety of this
data collection process even when only partial knowledge is available. We
present a new approach for optimal motion planning with safe exploration that
integrates chance-constrained stochastic optimal control with dynamics learning
and feedback control. We derive an iterative convex optimization algorithm that
solves an \underline{Info}rmation-cost \underline{S}tochastic
\underline{N}onlinear \underline{O}ptimal \underline{C}ontrol problem
(Info-SNOC). The optimization objective encodes both optimal performance and
exploration for learning, and the safety is incorporated as distributionally
robust chance constraints. The dynamics are predicted from a robust regression
model that is learned from data. The Info-SNOC algorithm is used to compute a
sub-optimal pool of safe motion plans that aid in exploration for learning
unknown residual dynamics under safety constraints. A stable feedback
controller is used to execute the motion plan and collect data for model
learning. We prove the safety of rollout from our exploration method and
reduction in uncertainty over epochs, thereby guaranteeing the consistency of
our learning method. We validate the effectiveness of Info-SNOC by designing
and implementing a pool of safe trajectories for a planar robot. We demonstrate
that our approach has higher success rate in ensuring safety when compared to a
deterministic trajectory optimization approach.Comment: Submitted to RA-L 2020, review-
Chance-Constrained AC Optimal Power Flow Integrating HVDC Lines and Controllability
The integration of large-scale renewable generation has major implications on
the operation of power systems, two of which we address in this work. First,
system operators have to deal with higher degrees of uncertainty due to
forecast errors and variability in renewable energy production. Second, with
abundant potential of renewable generation in remote locations, there is an
increasing interest in the use of High Voltage Direct Current lines (HVDC) to
increase transmission capacity. These HVDC transmission lines and the
flexibility and controllability they offer must be incorporated effectively and
safely into the system. In this work, we introduce an optimization tool that
addresses both challenges by incorporating the full AC power flow equations,
chance constraints to address the uncertainty of renewable infeed, modelling of
point-to-point HVDC lines, and optimized corrective control policies to model
the generator and HVDC response to uncertainty. The main contributions are
twofold. First, we introduce a HVDC line model and the corresponding HVDC
participation factors in a chance-constrained AC-OPF framework. Second, we
modify an existing algorithm for solving the chance-constrained AC-OPF to allow
for optimization of the generation and HVDC participation factors. Using
realistic wind forecast data, for 10 and IEEE 39 bus systems with HVDC lines
and wind farms, we show that our proposed OPF formulation achieves good in- and
out-of-sample performance whereas not considering uncertainty leads to high
constraint violation probabilities. In addition, we find that optimizing the
participation factors reduces the cost of uncertainty significantly
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