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Sensitivity analysis of data-driven building energy demand forecasts
(2019)Journal of Physics: Conference SeriesData-driven models of buildings could potentially reduce implementation barriers for demand forecasting and predictive control in the built environment. However, such models appear to be sensitive to the quality of the available input data. Here, we investigate the influence of sampling time, noise level and amount of available measurement data as well as the quality of the weather forecast on a heating demand forecast with online corrected ...Conference Paper -
The Two Tank Experiment: A Benchmark Control Problem
(1988)1988 American Control ConferenceThis paper describes an experimental process control system under development at Caltech. It is intended to be a source of benchmark control and identification problems. A first principles theoretical model is developed and compared to preliminary experimental data.Conference Paper -
Active exploration in adaptive model predictive control
(2020)2020 59th IEEE Conference on Decision and Control (CDC)A dual adaptive model predictive control (MPC) algorithm is presented for linear, time-invariant systems subject to bounded disturbances and parametric uncertainty in the state-space matrices. Online set-membership identification is performed to reduce the uncertainty and thus control affects both the informativity of identification and the system’s performance. The main contribution of the paper is to include this dual effect in the MPC ...Conference Paper -
Kernel-based identification of positive systems
(2020)2019 IEEE 58th Conference on Decision and Control (CDC)In this paper, we introduce a novel method for identification of internally positive systems. In this regard, we consider a kernel-based regularization framework. For the existence of a positive realization of a given transfer function, necessary and sufficient conditions are introduced in the realization theory of the positive systems. Utilizing these conditions, we formulate a convex optimization problem by which we can derive a positive ...Conference Paper -
Regularized System Identification: A Hierarchical Bayesian Approach
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsIn this paper, the hierarchical Bayesian method for regularized system identification is introduced. To this end, a hyperprior distribution is considered for the regularization matrix and then, the impulse response and the regularization matrix are jointly estimated based on a maximum a posteriori (MAP) approach. Toward introducing a suitable hyperprior, we decompose the regularization matrix using Cholesky decomposition and reduce the ...Conference Paper -
Low-Complexity Identification by Sparse Hyperparameter Estimation
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsThis paper presents a novel kernel-based system identification method, which promotes low complexity of the model in terms of the McMillan degree of the system. The regularization matrix is characterized as a linear combination of pre-selected rank-one matrices with unknown hyperparameter coefficients, and the hyperparameters are derived using a maximum a posteriori estimation approach. Each basis matrix is the optimal regularization ...Conference Paper -
Frequency regulation with heat pumps using robust MPC with affine policies
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsThe increase in the renewable energy sources connected to the electricity grid has resulted in an increased need for frequency regulation. On the demand side, frequency regulation services can be provided by electrified heating/cooling systems exploiting the energy stored in thermal mass of buildings. To provide such services a first principles model of the building is needed, which is often difficult to obtain in practice. This issue can ...Conference Paper -
Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsTensioned cable nets can be used as supporting structures for the efficient construction of lightweight building elements, such as thin concrete shell structures. To guarantee important mechanical properties of the latter, the tolerances on deviations of the tensioned cable net geometry from the desired target form are very tight. Therefore, the form needs to be readjusted on the construction site. In order to employ model-based optimization ...Conference Paper -
Robust Adaptive Model Predictive Control with Worst-Case Cost
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsA robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The system is characterized by an impulse response model, which is assumed to lie within a bounded set called the feasible system set. Online set-membership identi cation is used to reduce uncertainty in the impulse response. In the MPC scheme, robust constraints are ...Conference Paper -
Structured exploration in the finite horizon linear quadratic dual control problem
(2020)IFAC-PapersOnLine ~ 21th IFAC World Congress. ProceedingsThis paper presents a novel approach to synthesize dual controllers for unknown linear time-invariant systems with the tasks of optimizing a quadratic cost while reducing the uncertainty. To this end, a synthesis problem is defined where the feedback law has to simultaneously gain knowledge of the system and robustly optimize the cost. By framing the problem in a finite horizon setting, the trade-offs arising when the tasks include both ...Conference Paper