Model predictive control for energy-efficient optimization of radiant ceiling cooling systems

Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control

In order to reduce voltage error and stator current distortion caused by dead time in the model predictive control (MPC). Based on the permanent magnet synchronous motor driver and model predictive current control (MPCC), an novel control algorithm is proposed to correct the negative influence caused by dead time to improve its steady-state performance of model predictive control. The correctness of the proposed algorithm is verified by simulation.

Model predictive control (MPC) is an advanced control that has found widespread use in industries, particularly in process industries like oil refining and petrochemicals. Although the basic premise behind MPC is easy to comprehend, its inner workings are still generally viewed or regarded as too advanced for actual plant operator understanding. This lack of understanding is exposed when MPC performance deteriorates sometime after commissioning, as is often the case in some commercially operated process plants. Currently operators might have difficulty over reasoning about MPC performance degradation and formulating steps to investigate the cause. A tool is described that aims to make MPC more transparent to the operators. Commonly reported causes of MPC performance degradation are discussed and ways in which the operator can recognise them when they occur are outlined. Issues that are addressed include: making the set of controlled variables to be used for a given set of manipulated variables simpler and clearer; ways to recognise when a MPC controller is performing poorly and to identify the source of performance deterioration. An aim is to determine under what instances the operator can return the MPC performance to previous levels or request for specialist support or simply switch the MPC off. A goal is to avoid the kind of often reported situation where the operator gets worried that the controller is deteriorating and ends up taking knee jerk actions that cause further problems in MPC. At the top of the maintenance tool hierarchy is the trends comparison group, where MPC reference graphical performance trends are compared with actual graphical performance trends counterpart. If any abnormality is observed in these trends, the operator is encouraged to choose an option from a list of preliminary diagnostic questions contained in a group below trends comparison group, which best describes the observed abnormality. Each abnormality is associated with a list of suspected causes. When a suspected cause is chosen from the associated list, the operator is led to the symptoms investigation window, which contains scripts detailing steps for systematic examination of each symptom, with a view to either rejecting or confirming the suspicion. Assisted in the investigation are four background information windows: the virtual plant without MPC window, the virtual plant with MPC window, the transfer function matrix window and steady state gain, relative gain array (RGA) and relative weight array (RWA) window. The windows contain information and guidance that the operator might refer to from time to time during symptom investigation. Development of the maintenance tool is still at the design stage. The key components described have been research implementing MPC on three nonlinear process models, a continuous stirred tank reactor (CSTR), an evaporator process and a fluid catalytic cracking unit (FCCU). Case studies representing different MPC degradation scenarios are simulated, followed by a systematic procedure for diagnosing, isolating and recovering from such degradation, based on assumed operator’s perspective and expert’s technical reasoning. The knowledge gained from the case studies is used to develop an outline of a vision for a data-driven model predictive maintenance tool to help the operator make sensible judgements about performance degradation, the form and direction of diagnosis and fault isolation, and possibly, the recovery procedure.

Occupant feedback based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation

An advanced control of hybrid cooling technology for telecommunication base stations

A robust dichotomy solution-based model predictive control (DS-MPC) with a voltage observer for a three-phase grid-connected inverter (GCI) is proposed in this paper. The optimal voltage vector can be dynamically selected in the whole space vector circle plane in a very short time due to the voltage vector dichotomy solution algorithm. Therefore, the proposed DS-MPC control has not only fast dynamic performance but also good steady state performance. Meanwhile, the performance of model predictive control (MPC) usually depends on the accuracy of plant model. In weak grid case, the variation of equivalent grid impedance could impact on the performance. To improve the current control robustness, DS-MPC is combined with a grid voltage disturbance observer using the extended Kalman filter (EKF) so that the disturbance of weak grid voltages is effectively suppressed. Moreover, the grid currents are well regulated even with greatly distorted and unbalanced grid voltages. The test results verify the correctness and effectiveness of the proposed DS-MPC with EKF observer under the weak grid.

Algorithms for improved fixed-time performance of Lyapunov-based economic model predictive control of nonlinear systems

Discussion on: “Improved MPC Design based on Saturating Control Laws”

Data-driven model predictive control (MPC) has gained popularity as a proactive control strategy due to the development of the Internet of Things (IoT) and data storage technologies. To apply the data-driven MPC to residential buildings, it is necessary to consider what data to measure, how to control a target system, and which prediction models are inserted. In this paper, we investigate the effect of different datasets due to sensor and controller set selection on data-driven model and data-driven MPC performance. We confirmed that the control variable according to the controller set affects MPC performance considerably.

<div class="section abstract"><div class="htmlview paragraph">Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust, which allows fuel economy (FE) improvements in hybrid electric vehicles (HEVs) through optimal energy management strategies (EMS). Velocity predictions generated between 5 and 30 s predictions could be implemented using model predictive control (MPC), but the performance of MPC must be well understood. Also, the vulnerability of predictive optimal EMS to velocity prediction accuracy should be addressed. Before an optimal EMS can be implemented, its overall performance must be evaluated and benchmarked against relevant velocity prediction metrics. A real-world highway drive cycle (DC) in the high-fidelity, controls-oriented 2017 Toyota Prius Prime model operating in charge-sustaining mode was utilized to observe FE realization. We propose three important metrics for comparison to no velocity prediction control: (1) perfect full DC prediction using dynamic programming (DP), (2) 10 s prediction horizon MPC, (3) 10 s constant velocity prediction. The very first optimal EMS requires the whole DC prediction in advance, whereas the rest of the two strategies only require limited horizon velocity prediction. These different velocity predictions are input into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. Our results show that the constant velocity prediction algorithm outperformed the baseline control strategy, but underperformed the MPC strategy. We also show that using a 10 s prediction window, MPC strategy provided FE improvement results very close to the full DC prediction case. So with the advancement in perception systems, MPC could be implementable in real vehicles. Also, constant velocity prediction results are good enough that optimization should be implemented in vehicle controllers with MPC type of framework. In future work, we seek to combine these models with velocity prediction models to demonstrate FE improvements on a physical vehicle.</div></div>

The documented study in this paper is the comparison between Model Predictive Control (MPC) and fine-tuned conventional proportional Integral (PI) controllers. The algorithms used for fine tuning of PI controllers are Particle Swarm Optimization, Genetic Algorithm, Z-N (Ziegler Nichols), and Grey Wolf Optimization. The MATLAB/Simulink environment is used for comparison of results. The MPC based steam condenser presented excellent and superior dynamic performance in response to different step changes. Also, the detailed tabular comparison of various control metrics showed the better performance of MPC. The overall simulation results demonstrated that MPC can be an efficient alternative to PI controller for the steam condenser.

Model predictive control has been recognized as one of the essential solutions to achieve considerable energy savings in buildings. However, its performance on a building zone level can be inferior to a well-tuned conventional controller, especially in situations with constant energy prices and conservative comfort constraints. Optimization problem in the background has to be chosen to guarantee recursive feasibility and considerable energy savings without compromising the users comfort at the same time. This paper gives a novel formulation of the model predictive temperature control problem in buildings and its fair comparison with conventional controllers with the same level of flexibility allowed in zone temperature control. All controllers are tested for a system with seasonal heating and cooling, which is the most common case in real applications. It is shown that the introduced formulation leads to the model predictive controller that significantly outperforms conventional controllers both in energy consumption and users comfort.

The ability to meet a controlled time of arrival during a continuous descent operation will enable environmentally friendly and fuel-efficient descent operations while simultaneously maintaining airport throughput. However, if the wind forecast used to compute the initial trajectory plan is not accurate enough, the guidance system will need to correct time deviations from the plan during the execution of the descent. Previous work proposed an onboard guidance strategy based on model predictive control, which repeatedly updates the trajectory plan in real-time from the current aircraft state and for the remainder of the descent. However, the wind conditions downstream, at altitudes not explored yet, were difficult to predict due to the lack of data. This paper shows the potential benefits of using wind observations, broadcast by nearby aircraft, to reconstruct the wind profile downstream. The wind profile in the trajectory optimization problem is modeled as a spline, control points of which are updated to fit the observations before replanning the trajectory. Results from simulations using realistic wind data show that the performance of model predictive control significantly improves when including up-to-date wind observations, in terms of time and energy errors at the metering fix and fuel consumption.

This note proposes a model predictive control scheme for tracking a random reference and analyses its performance. In such situations it is usual to assume that the reference eventually converges to a constant in which case convergence to zero of the tracking error can be established. In this note we remove this simplifying assumption and characterise the set to which the tracking error converges and the associated region of convergence.

The design and tracking performance of model predictive controllers for complex, control oriented, physics based helicopter models is investigated. A nonlinear model of the helicopter, which also includes blade flexibility, is first linearized around specific flight conditions like level banked turns and helical turns. Model predictive controllers are then designed using the linearized helicopter models and their ability to track discontinuous maneuvering trajectories is evaluated. The performance metrics used herein are tracking quality, satisfaction of heterogeneous constraints, and computation time.