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.

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.

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.

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