3 research outputs found
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
We present a physics-constrained control-oriented deep learning method for
modeling building thermal dynamics. The proposed method is based on the
systematic encoding of physics-based prior knowledge into a structured
recurrent neural architecture. Specifically, our method incorporates structural
priors from traditional physics-based building modeling into the neural network
thermal dynamics model structure. Further, we leverage penalty methods to
provide inequality constraints, thereby bounding predictions within physically
realistic and safe operating ranges. Observing that stable eigenvalues
accurately characterize the dissipativeness of the system, we additionally use
a constrained matrix parameterization based on the Perron-Frobenius theorem to
bound the dominant eigenvalues of the building thermal model parameter
matrices. We demonstrate the proposed data-driven modeling approach's
effectiveness and physical interpretability on a dataset obtained from a
real-world office building with 20 thermal zones. Using only 10 days'
measurements for training, we demonstrate generalization over 20 consecutive
days, significantly improving the accuracy compared to prior state-of-the-art
results reported in the literature
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems
We present differentiable predictive control (DPC) as a deep learning-based
alternative to the explicit model predictive control (MPC) for unknown
nonlinear systems. In the DPC framework, a neural state-space model is learned
from time-series measurements of the system dynamics. The neural control policy
is then optimized via stochastic gradient descent approach by differentiating
the MPC loss function through the closed-loop system dynamics model. The
proposed DPC method learns model-based control policies with state and input
constraints, while supporting time-varying references and constraints. In
embedded implementation using a Raspberry-Pi platform, we experimentally
demonstrate that it is possible to train constrained control policies purely
based on the measurements of the unknown nonlinear system. We compare the
control performance of the DPC method against explicit MPC and report
efficiency gains in online computational demands, memory requirements, policy
complexity, and construction time. In particular, we show that our method
scales linearly compared to exponential scalability of the explicit MPC solved
via multiparametric programming
Constraint Learning for Control Tasks with Limited Duration Barrier Functions
When deploying autonomous agents in unstructured environments over sustained
periods of time, adaptability and robustness oftentimes outweigh optimality as
a primary consideration. In other words, safety and survivability constraints
play a key role and in this paper, we present a novel, constraint-learning
framework for control tasks built on the idea of constraints-driven control.
However, since control policies that keep a dynamical agent within state
constraints over infinite horizons are not always available, this work instead
considers constraints that can be satisfied over a sufficiently long time
horizon T > 0, which we refer to as limited-duration safety. Consequently,
value function learning can be used as a tool to help us find limited-duration
safe policies. We show that, in some applications, the existence of
limited-duration safe policies is actually sufficient for long-duration
autonomy. This idea is illustrated on a swarm of simulated robots that are
tasked with covering a given area, but that sporadically need to abandon this
task to charge batteries. We show how the battery-charging behavior naturally
emerges as a result of the constraints. Additionally, using a cart-pole
simulation environment, we show how a control policy can be efficiently
transferred from the source task, balancing the pole, to the target task,
moving the cart to one direction without letting the pole fall down