Energy Efficiency and Tracking Performance Evaluation for Dual-Mode Model Predictive Control of HVAC Systems

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
Zicheng Cai ◽  
Asad A. Ul Haq ◽  
Michael E. Cholette ◽  
Dragan Djurdjanovic
Keyword(s):  
Energy Consumption ◽  
Predictive Control ◽  
Pid Control ◽  
Control Structure ◽  
Tracking Performance ◽  
System Control ◽  
Hvac System ◽  
Dual Mode ◽  
Different Types ◽  
Predictive Controller

This paper presents evaluation of the energy consumption and tracking performance associated with the use of a recently introduced dual-mode model predictive controller (DMMPC) for control of a heating, ventilation, and air conditioning (HVAC) system. The study was conducted using detailed simulations of an HVAC system, which included a multizone air loop, a water loop, and a chiller. Energy consumption and tracking performance are computed from the simulations and evaluated in the presence of different types and magnitudes of noise and disturbances. Performance of the DMMPC is compared with a baseline proportional-integral-derivative (PID) control structure commonly used for HVAC system control, and this comparison showed clear and consistent superiority of the DMMPC.

scholarly journals Model Predictive Control with Adaptive Building Model for Heating Using the Hybrid Air-Conditioning System in a Railway Station

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1996
Author(s):  
Ruixin Lv ◽  
Zhongyuan Yuan ◽  
Bo Lei ◽  
Jiacheng Zheng ◽  
Xiujing Luo
Keyword(s):  
Energy Consumption ◽  
Model Predictive Control ◽  
Thermal Comfort ◽  
Predictive Control ◽  
Pid Control ◽  
Air Conditioning ◽  
Adaptive Model ◽  
Air Conditioning System ◽  
Building Model ◽  
Indoor Thermal Comfort

A model predictive control (MPC) system with an adaptive building model based on thermal-electrical analogy for the hybrid air conditioning system using the radiant floor and all-air system for heating is proposed in this paper to solve the heating supply control difficulties of the railway station on Tibetan Plateau. The MPC controller applies an off-line method of updating the building model to improve the accuracy of predicting indoor conditions. The control performance of the adaptive MPC is compared with the proportional-integral-derivative (PID) control, as well as an MPC without adaptive model through simulation constructed based on a TRNSYS-MATLAB co-simulation testbed. The results show that the implementation of the adaptive MPC can improve indoor thermal comfort and reduce 22.2% energy consumption compared to the PID control. Compared to the MPC without adaptive model, the adaptive MPC achieves fewer violations of constraints and reduces energy consumption by 11.5% through periodic model updating. This study focuses on the design of a control system to maintain indoor thermal comfort and improve system efficiency. The proposed method could also be applied in other public buildings.

Multiple Models Predictive Control of Once-Through Steam Generator

2008 ◽  
Author(s):  
Yun Tai ◽  
Su-Xia Hou ◽  
Fu-Yu Zhao
Keyword(s):  
Steam Generator ◽  
Predictive Control ◽  
Good Control ◽  
Multiple Models ◽  
Mathematics Model ◽  
System Control ◽  
Multiple Model ◽  
Nonlinear System Control ◽  
Predictive Controller ◽  
Output System

Because of multiphase flow during the heat transfer, OTSG (Once-Through Steam Generator) is a complex nonlinear MIMO (Multiple Input and Multiple Output) system. This article sets the mathematics model of OTSG with internal screw and double tubes, uses the FOROTSG program to simulate OTSG, and identifies the system models of different power levels. Based on this, Multiple Model Predictive Control (MMPC) strategy is proposed, which designs the Model Predictive Controller of each model identified, and then integrates the multiple models by Membership Function Law, to achieve smoothly switch of the multiple models at last. The simulating result indicates the MMPC has the good control effects, and it is an available strategy to solve the problems of the nonlinear system control.

scholarly journals Predictive Control of aWastewater Treatment Process

2007 ◽  
Vol 2 (2) ◽  
pp. 132 ◽  
Author(s):  
Sergiu Caraman ◽  
Mihaela Sbarciog ◽  
Marian Barbu
Keyword(s):  
Predictive Control ◽  
Internal Model ◽  
Current Practice ◽  
Treatment Process ◽  
Control Structure ◽  
Control Law ◽  
Wastewater Treatment Process ◽  
Prediction Horizon ◽  
Control Objective ◽  
Predictive Controller

The paper deals with the design of a predictive controller for a wastewater treatment process. In the considered process, the wastewater is treated in order to obtain an effluent having the substrate concentration within the standard limits established by law (below 20 mg/l). This goal is achieved by controlling the concentration of dissolved oxygen to a certain value. The predictive controller uses a neural network as internal model of the process and alters the dilution rate in order to fulfill the control objective. This control strategy offers various possibilities for the control law adjustment by means of the following parameters: the prediction horizon, the control horizon, the weights of the error and the command. The predictive control structure has been tested in three functioning regimes, considered essential due to the frequency of their occurrence in current practice.

Predictive Control of Harmonic Drive in Automotive Application

2007 ◽  
Vol 11 (9) ◽  
pp. 1165-1172 ◽  
Author(s):  
Ádam Varga ◽  
◽  
Béla Lantos
Keyword(s):  
Real Time ◽  
Predictive Control ◽  
Controller Design ◽  
Rapid Prototype ◽  
Steering System ◽  
Tracking Performance ◽  
Automotive Application ◽  
Harmonic Drive ◽  
Steering Feel ◽  
Predictive Controller

This paper addresses the predictive control of the harmonic drive in an automotive application. The goal of the control was to provide good steering feel for the driver and satisfactory tracking performance in a steering system. The paper presents the dynamic model of the harmonic drive, a design framework and a two step algorithm for predictive controller design. The elaborated model predictive controller is similar to a cascade type controller with constraints in the performance function to ensure closed loop stability and a useful compromise between torque tracking and position tracking. The controller was developed and implemented in a real-time environment for rapid prototype design using Matlab, Simulink, Real-Time Workshop and dSPACE AutoBox hardware, then it was experimentally tuned for best steering feel and good tracking performance.

A New Tracking System Overall Structure Designed Based on Laser Scanning

2013 ◽  
Vol 401-403 ◽  
pp. 964-967
Author(s):  
Qiao Zhang ◽  
Guo Hua Cao
Keyword(s):  
Predictive Control ◽  
Pid Control ◽  
Laser Scanning ◽  
Tracking System ◽  
Control Unit ◽  
System Control ◽  
Central Control ◽  
Two Dimensional ◽  
Central Control Unit ◽  
And Control

Design the overall framework of a tracking system based on laser scanning . The system uses a two-dimensional scanning galvanometer agencies to complete scanning, uses the computer as the central control unit to process scanning the returned signal and control the whole system. Control system uses hierarchical ideology, with predictive control and PID control combination.

Energy and Robustness Evaluation for Dual-Mode Model Predictive Control of HVAC Systems

2017 ◽  
Author(s):  
Zicheng Cai ◽  
Asad Ul Haq ◽  
Dragan Djurdjanovic
Keyword(s):  
Hvac System ◽  
Dual Mode ◽  
Process Noise ◽  
Model Predictive Controller ◽  
System A ◽  
Air Handling Unit ◽  
Guaranteed Stability ◽  
Modeling Uncertainties ◽  
Predictive Controller ◽  
Simulation And Evaluation

This paper presents an evaluation of energy efficiency and robustness to disturbances for a discrete-time dual-mode model predictive controller (DMMPC) of a heating, ventilating, and air conditioning (HVAC) system. The recently introduced controller only requires finite number of iterations for the underlying model predictive controller optimization to achieve guaranteed stability under the assumption of an error free controllable system. A nonlinear model for the air handling unit of a generic multi-zone HVAC system is used for simulation and evaluation of control performance. Energy consumption is computed from simulation results, while the robustness conclusions are drawn from analyses of the results in the presence of process noise and modeling uncertainties. The energy and robustness performance of the dual-mode controller is compared with a traditional PID control structure, showing the superiority of the newly proposed controller.

Application of model predictive control for the optimization of thermo-hygrometric comfort and energy consumption of buildings

2018 ◽  
Vol 18 (3) ◽  
pp. 375-391
Author(s):  
Ludovico DANZA ◽  
Lorenzo BELUSSI ◽  
Fabio FLOREANI ◽  
Italo MERONI
Keyword(s):  
Energy Consumption ◽  
Model Predictive Control ◽  
Predictive Control

Design a Robust RST Controller for Stabilization of a Tri-Copter UAV

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Zain Anwar Ali ◽  
Dao Bo Wang ◽  
Muhammad Aamir
Keyword(s):  
Control Algorithm ◽  
Control Structure ◽  
Flight Test ◽  
System Control ◽  
Test Results ◽  
Output Control ◽  
Aerial Robot ◽  
Mimo Model

<span>Research on the tri-rotor aerial robot is due to extra efficiency<span> over other UAV’s regarding stability, power and size<span> requirements. We require a controller to achieve 6-Degree<span> Of Freedom (DOF), for such purpose, we propose the RST<span> controller to operate our tri-copter model. A MIMO model<span> of a tri-copter aerial robot is challenged in the area of control<span> engineering. Ninestates of output control dynamics are treated<span> individually. We designed dynamic controllers to stabilize the<span> parameters of an UAV. The resulting system control algorithm<span> is capable of stabilizing our UAV to perform numerous<span> operations autonomously. The estimation and simulation<span> implemented inMATLAB, Simulink to verify the results. All<span> real flight test results are presented to prove the success of<span> the planned control structure.<br /><br class="Apple-interchange-newline" /></span></span></span></span></span></span></span></span></span></span></span></span></span></span>

Enlarging the region of stability in robust model predictive controller based on dual-mode control

2021 ◽  
pp. 014233122110123
Author(s):  
Fatemeh Khani ◽  
Mohammad Haeri
Keyword(s):  
Stability Region ◽  
Linear Matrix ◽  
Input State ◽  
Dual Mode ◽  
Model Predictive Controller ◽  
Mode Control ◽  
Robust Model ◽  
Output Constraints ◽  
Predictive Controller ◽  
The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

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