Eva Zacekova

Control relevant identification and predictive control of a building

Even though a modern control concepts have emerged in numerous parts of the world, a building automation is still a field where the position of the classical control is almost exclusive. The main reason is, that for the synthesis of an advanced controller, a decent model, “model for control”, is needed. Climate changes, diminishing world supplies of the “traditional” fuels, as well as economical aspects are probably the most driving factors of current effort to save the energy. As the buildings account for about 40% of global final energy use, the efficient building climate control can significantly contribute to the saving effort. Predictive building automation can be used to operate buildings in energy and cost effective manner instead of conventional room automation with minimum retrofitting requirements. In this paper a multi-step ahead error minimization approach to a building modeling is presented and influence of the solar radiation on the quality of the constructed model is examined. Moreover, the concept of the predictive control applied to a building is shown.

Economical nonlinear model predictive control for building climate control

In this paper, two alternatives approaches to model predictive control (MPC) are compared and contrasted for the role of zone temperature controller - the commonly used linear formulation (LMPC) and rather unconventional nonlinear formulation (NMPC). The economical focus is reflected by the performance criterion being a combination of the comfort requirements and the monetary cost penalties (price of the consumed hot water and the electricity needed to deliver the water to the building) of the controlled inputs. With this formulation, the optimal controller drives toward minimization of the real price rather than minimization of abstract quantities. It turns out that the NMPC is able to attack the cost minimization directly while retaining a compact optimization formulation as opposed to the suboptimal linear alternative. A considerable part of the superiority of the NMPC can be owed also to the use of a nonlinear model that captures the nonlinear building dynamics much more accurately than the linear models. Thanks to an enhanced search step choice introduced in this paper, the NMPC outperforms both the LMPC and the conventional controller significantly even under severe computational restrictions which demonstrates its strong practical applicability.

On predicted mean vote optimization in building climate control

Low energy buildings have been attracting much attention lately. Most of the research is focused on the building construction or alternative energy sources. Recently, there has been an intense research in the area of Model Predictive Control (MPC) for buildings. The main principle of such a controller is a trade-off between energy savings and user welfare making use of predictions of disturbances acting on the system (ambient temperature, solar radiation, occupancy, etc.). Usually, the thermal comfort is represented by a static range for the operative temperature according to the international standards. By contrast, this paper is devoted to the optimization of the Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly. PMV index, however, is a nonlinear function of various quantities, which makes the problem more difficult to solve. The paper will show the main differences in MPC problem formulation, compare the control performance both to the conventional and predictive control strategies, point out that the proposed optimal control problem formulation shifts the savings potential of classical MPC by additional 11% and finally, the quality of the fulfillment of the thermal comfort will be addressed.

Identification and energy efficient control for a building: Getting inspired by MPC

This paper deals with identification of a building model based on real-life data and subsequent temperature controller design. For the identification, advanced identification technique - namely MPC Relevant Identification method - is used. This approach has the capability of providing models with better prediction performance compared to the commonly used methods. Regarding the controller part, several alternatives are proposed. First, both linear and nonlinear MPC controlling the zone temperature are designed. Although highly attractive due to promising energetic savings and thermal comfort satisfaction, MPCs demand high computational power. To overcome this issue and preserve the attractive properties of the MPC, two MPC-learned feedback controllers are proposed, one learned from LMPC and the other learned from NMPC. While remaining computationally low-cost, they improve the performance of the classical controllers towards the high-performance MPC standards. The results exploiting data from real operation of an office equipped with air handling unit situated in Lakeshore building, Michigan Tech, are presented and discussed.

On dual control for buildings using persistent excitation condition

Buildings belong to one of the biggest consumers of the final energy. The global effort for saving the energy leads to an intensive research aimed at its optimization. Predictive control has become a very popular approach in many industries with building being no exception. The main bottleneck of this method is, however, a need for a good model. When constructing a building model, it is, in many cases, already operating under the feedback control contradicting thus some of the key assumptions (such as e.g. persistence of excitation or input-noise decorrelation) of a vast majority of the identification approaches. This causes problems for both the initial system identification and model adjustments/reidentification. In latter case, a dual control is a possible approach, when the problems of control and system identification are solved simultaneously. This paper presents an approach when a persistent excitation condition in form of maximization of minimal eigenvalue of information matrix is incorporated into a control criterion.

Semi-receding horizon algorithm for “sufficiently exciting” MPC with adaptive search step

In this paper, the task of finding an algorithm providing sufficiently excited data within the MPC framework is tackled. Such algorithm is expected to take action only when the re-identification is needed and it shall be used as the “least costly” closed loop identification experiment for MPC. The already existing approach based on maximization of the smallest eigenvalue of the information matrix increase is revised and an adaptation by introducing a semi-receding horizon principle is performed. Further, the optimization algorithm used for the maximization of the provided information is adapted such that the constraints on the maximal allowed control performance deterioration are handled more carefully and are incorporated directly into the process instead of using them just as a termination condition. The effect of the performed adaptations is inspected using a numerical example. The example shows that the employment of the semi-receding horizon brings major improvement of the identification properties of the obtained data and the proposed adaptive-search step algorithm used for the “informativeness” optimization brings further significant increase of the contained information while the aggravation of the economical and tracking aspects of the control are kept at acceptable level.

Toward a Smart Car: Hybrid Nonlinear Predictive Controller With Adaptive Horizon

This paper focuses on the development of an optimization algorithm for car motion predictive control that addresses both hybrid car dynamics and hybrid minimization criterion. Instead of solving computationally demanding nonlinear mixed-integer programming task or approximating the hybrid dynamics/criterion, the Hamiltonian-switching hybrid nonlinear predictive control algorithm developed in this paper incorporates the information about hybridity directly into the optimization routine. To decrease the time complexity, several adaptive prediction horizon approaches are proposed, and for some of them, it is shown that they preserve maneuverability-related properties of the car. All developed alternatives are verified on an example of a motion control of a racing car and compared with the approximation-based nonlinear predictive control and a commercial product. Moreover, a sensitivity analysis examining robustness of the algorithm is included as well.

MPC for a class of nonlinear systems with guaranteed identifiability

This paper addresses the problem of model predictive control for a class of nonlinear systems which satisfies persistent excitation condition. The conditions under which a nonlinear system description can be handled are specified and two algorithms (one optimizing the first input sample and the other considering optimization of an M-sample subsequence of the input profile) solving the persistent excitation condition within a predictive controller for nonlinear systems are developed, both maximizing the smallest eigenvalue of the information matrix increase. The numerical experiments performed on a test-bed system demonstrate that the algorithms are able to successfully improve identifiability of a nonlinear system description while keeping the original controller performance degradation lower than arbitrarily chosen level.

Quantized nonlinear model predictive control for a building

In this paper, the task of quantized nonlinear predictive control is addressed. In such case, values of some inputs can be from a continuous interval while for the others, it is required that the optimized values belong to a countable set of discrete values. Instead of very straightforward a posteriori quantization, an alternative algorithm is developed incorporating the quantization aspects directly into the optimization routine. The newly proposed quaNPC algorithm is tested on an example of building temperature control. The results for a broad range of number of quantization steps show that (unlike the naive a posteriori quantization) the quaNPC is able to maintain the control performance close to the performance of the original continuous-valued nonlinear predictive controller and at the same time it significantly decreases the undesirable oscillations of the discrete-valued input.

Identification and model selection of building models

Besides retrofitting, modernization and new ways of construction of the buildings, the cheaper and recently a very popular approach how to optimize energy consumption is to employ better control algorithms for the buildings. Predictive control has proven to be a strategy useful in many industries and became a suitable option for the building sector as well. The main bottleneck of this approach is a need for a fine model. There exist a number of building models and identification approaches. This paper provides a brief survey of the building modeling approaches and discusses their properties and applicability for the predictive control. Having a number of potential models at hand, the procedure of the model selection suitable for predictive control is presented. Finally, the performance of the model selection procedure is examined in a two zone building. The results are then presented and the conclusions drown.