6 results
Now showing1 - 6 of 6
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Approximate explicit model predictive controller using Gaussian processesModel predictive control is a successful method of regulating the operation of constrained dynamical systems. However, its applicability is limited by the necessity of solving in real-time an optimization problem. Explicit model predictive control techniques aim to precompute the optimal control law off-line for all feasible points in the state space of the system. However, constructing the explicit control law off-line and using it to compute the control inputs on-line can be computationally demanding for medium to large scale systems. Hence, several approaches have been suggested to approximate the explicit control law. This paper proposes the use of Gaussian processes for this purpose. Gaussian processes allow one to define a systematic way of selecting these training data which minimize the uncertainty of the approximation. Unlike other approaches in the literature, domain specific knowledge is exploited here to simplify the training effort, while probabilistic guarantees are provided for the proximity of the derived approximation to the explicit control law. We illustrate, in a number of benchmark systems, the efficacy of the proposed approach which leads to closed-loop operation similar to that of the exact explicit control law, only at a fraction of the computation effort.Publicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Distributed model predictive control for linear systems with adaptive terminal setsWe propose a distributed model predictive control scheme for linear time-invariant constrained systems that admit a separable structure. To exploit the merits of distributed computation algorithms, the terminal cost and invariant terminal set of the optimal control problem need to respect the coupling structure of the system. Existing methods to address this issue typically separate the synthesis of terminal controllers and costs from the one of terminal sets, and do not explicitly consider the effect of the current and predicted system states on this synthesis process. These limitations can adversely affect performance due to small or even empty terminal sets. Here, we present a unified framework to encapsulate the synthesis of both the stabilizing terminal controller and invariant terminal set into the same optimization problem. Conditions for Lyapunov stability and invariance are imposed in the synthesis problem in a way that allows the terminal cost and invariant terminal set to admit the desired distributed structure. We illustrate the effectiveness of the proposed method on several numerical examples.Publicationtype: Journal Article20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Humans-in-the-loop: a game-theoretic perspective on adaptive building energy systemsEfficient building energy management has attracted a great interest in diverse research areas due to significant potential energy savings. A remaining challenge is how to combine the efforts of engineers on improving the energy management system with approaches developed by social scientists to integrate the occupants actively in the energy management system. This paper proposes the formulation of a game between the building energy management system and occupants to agree on room temperature comfort bounds. Under mild assumptions on the cost functions of the occupants, we show that a generalized Nash equilibrium exists and it can be shown to equal the social optimum. The alternating direction method of multipliers is used to solve the resulting consensus optimization problem in a distributed way, with the building management system as coordinator. An advantage of the proposed method is that the building energy management system does not need to rely on an explicit model of the occupant behavior but due to the game theoretic approach, indirectly receives an adapted model at each iteration. An extensive numerical study demonstrates the efficacy of the proposed approach.Publicationtype: Conference Paper15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Scalability through decentralization: a robust control approach for the energy management of a building community(Elsevier, 2017-07); ; ; ; Recent studies in the literature have shown that cooperative energy management of an aggregation of buildings may lead to substantial energy savings. These approaches typically assume the existence of a central operator that is capable of formulating and solving, within a reasonable amount of time, a centralized optimization problem. However, this requirement may be unrealizable in cases of large scale districts, and it also fails to address privacy concerns of the building occupants. In this paper, we deal with these issues by proposing a decentralized control scheme which only requires the individual buildings to communicate bounds on their energy demands. The proposed method partly alleviates concerns on privacy since this limited communication scheme does not reveal the exact characteristics of the energy usage within each building. In addition, it enables a distributed computation of the solution, making our method highly scalable. We demonstrate through a numerical study the efficacy of the proposed approach, which leads to solutions that closely approximate those obtained by the centralized formulation only at a fraction of the computational effort.Publicationtype: Journal Article14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Humans in the loop: a stochastic predictive approach to building energy management in the presence of unpredictable usersEfficient building energy management has attracted a great deal of academic interest with significant potential energy savings to be envisaged. Social scientists strive to achieve these savings by employing behavior-based approaches, while engineers investigate control strategies for the efficient operation of the building devices. This work can be seen as a first step towards bridging these two approaches by proposing a control scheme that encapsulates building occupant behavior into the energy management system. In particular, the occupants willingness to tolerate comfort bound violations is modeled as a random measurable uncertainty and incorporated into the building energy management system through disturbance feedback control policies. The respective optimal control problem is formulated as a mixed-integer stochastic optimization problem, and a computationally tractable approximation of it is derived by restricting the disturbance feedback control policies to admit an affine structure. An extensive numerical study verifies that the proposed approach can significantly reduce the energy consumption of the buildings.Publicationtype: Journal Article10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A data-driven stochastic optimization approach to the seasonal storage energy managementSeveral studies in the literature have shown the potential energy savings emerging from the cooperative management of the aggregated building energy demands. Sophisticated predictive control schemes have recently been developed that achieve these gains by exploiting the energy generation, conversion, and storage equipment shared by the building community. A common difficulty with all these methods is integrating knowledge about the long term evolution of the disturbances affecting the system dynamics (e.g., ambient temperature and solar radiation). In this context, the seasonal storage capabilities of the system are difficult to be optimally managed. This letter addresses this issue by exploiting available historical data to: (i) construct bounds that confine with high probability the optimal charging trajectory of the seasonal storage and (ii) generate a piece-wise affine approximation of the value function of the energy stored in the seasonal storage at each time step. Using these bounds and value functions, we formulate a multistage stochastic optimization problem to minimize the total energy consumption of the system. In a numerical study based on a realistic system configuration, the proposed method is shown to operate the system close to global optimality.Publicationtype: Journal Article15