Adaptive predictive control method for improving control stability of air-conditioning terminal in public buildings

2021 ◽  
pp. 111261
Author(s):  
Xiuming Li ◽  
Ce Zhang ◽  
Tianyi Zhao ◽  
Zongwei Han
Keyword(s):  
Predictive Control ◽  
Air Conditioning ◽  
Control Method ◽  
Public Buildings ◽  
Control Stability ◽  
Adaptive Predictive Control

scholarly journals Current Prediction-Based Three-Vector Voltage Optimization System for the Induction Motor with Current Static Error Correction

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Li Haixia ◽  
Lin Jican
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Induction Motor ◽  
Predictive Control ◽  
Control Method ◽  
Tracking Error ◽  
Current Control ◽  
Static Error ◽  
Control Stability ◽  
Steady State Performance

In the present study, the current control method of the model predictive control is applied to the field-oriented control induction motor. The augmentation model of the motor is initially established based on the stator current equation, which performs the current predictive control and formulates the new cost function by means of tracking error. Then, the influence of parameter error on the current control stability in the prediction model is analysed, and the current static error is corrected according to the correlation between the input and feedback. Finally, a simple and effective three-vector control strategy is proposed. Moreover, three adjacent basic voltage vectors are utilized, and then six candidate voltage vectors are synthesized in each sector to replace eight basic voltage vectors in the conventional model predictive control (MPC). The obtained results show that synthesized vectors, which have arbitrary amplitude and direction, significantly expand the coverage of the system’s control set, reduce the torque and flux pulsation in the conventional MPC, and improve the steady-state performance of the system. Finally, the dSPACE platform is employed to validate the performed experiment. It is concluded that the proposed method can reduce the torque and flux pulse, perform the induction motor current control, and improve the steady-state performance of the system.

scholarly journals Air conditioning predictive control for rail transit vehicles based on load prediction

2020 ◽  
Vol 165 ◽  
pp. 04071
Author(s):  
Yichen Zhao ◽  
Haiquan Bi ◽  
Honglin Wang
Keyword(s):  
Feedback Control ◽  
Energy Saving ◽  
Predictive Control ◽  
Air Conditioning ◽  
Room Temperature ◽  
Control Method ◽  
Air Cooling ◽  
Load Prediction ◽  
Dynamic Correction ◽  
Air Conditioning Systems

This paper proposes a predictive control method for rail vehicle air-conditioning systems. Due to heat transfer and diffusion, the air-conditioning system is a long-time-delay system. However, most air-conditioning systems use feedback control, which has problems such as long transition time, system shock, and mismatch between air cooling capacity and load, resulting in the waste of energy. Combined with feedforward and feedback control, a predictive control method with dynamic correction is proposed to solve this problem. Based on the load prediction, the real-time indoor temperature feedback link is added to send the cold air into the room in advance, which makes the room temperature stable, and the energy-saving effect significant. In the study, variance analysis of environmental factors is performed to improve the accuracy of the load prediction system, and the mean relative error (MRE) of the prediction reached 0.0112. By comparing the simulation results of predictive control and feedback control, it is proved that the predictive control with correction has a smoother room temperature curve. The energy-saving rate is about 25.2%.

scholarly journals Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaosuo Luo ◽  
Yongduan Song
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Method ◽  
Negative Impact ◽  
Data Driven ◽  
Recursive Identification ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Closed Loop Identification ◽  
Adaptive Predictive Control

This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data. Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.

scholarly journals Multivariable Constrained Adaptive Predictive Control based on Closed-loop Subspace Identification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Method ◽  
Open Loop ◽  
System Model ◽  
Quadratic Program ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Prediction Errors ◽  
Adaptive Predictive Control

In order to deal with nonlinear, time-varying, and multivariable constrained characteristics in closed-loop industrial processes, a multivariable constrained adaptive predictive control (CAPC) method based on closed-loop subspace identification is proposed. The state-space model is obtained through the closed-loop subspace identification algorithm, which is regarded as the system model. The algorithm is implemented online to update the R matrix with a receding window. By comparing the prediction errors before and after updating, it considers whether or not to update the system model. The model is then used to design the model predictive controller, which involves the solution of a quadratic program solving multivariable constraints. This paper presents a comparison between the performance of the proposed control method when applied to a 2-CSTR system, and that of an open-loop subspace CAPC method. The superiority of the proposed method is illustrated by the simulation results.

Sign in / Sign up

Export Citation Format

Share Document