Comparative Study of THD in Multilevel Converter Using Model Predictive Controller

2021 ◽  
Author(s):  
S. Rajasekaran ◽  
CH. Mahendar
Keyword(s):  
Comparative Study ◽  
Multilevel Converter ◽  
Model Predictive Controller ◽  
Predictive Controller

A Comparative Study of the Energy-Saving Controllers for Automotive Air-Conditioning/Refrigeration Systems

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Yanjun Huang ◽  
Amir Khajepour ◽  
Milad Khazraee ◽  
Majid Bahrami
Keyword(s):  
Sliding Mode Control ◽  
Comparative Study ◽  
Energy Saving ◽  
Air Conditioning ◽  
Sliding Mode ◽  
Model Predictive Controller ◽  
Set Point ◽  
Proportional Integral ◽  
Automotive Air Conditioning ◽  
Predictive Controller

With the extensive application of air-conditioning/refrigeration (A/C-R) systems in homes, industry, and vehicles, many efforts have been put toward the controller development for A/C-R systems. Therefore, this paper proposes an energy-saving model predictive controller (MPC) via a comparative study of several control approaches that could be applied in automotive A/C-R systems. The on/off controller is first presented and used as a basis to compare with others. The conventional proportional-integral (PI) as well as a set-point controller follows. In the set-point controller, the sliding mode control (SMC) strategies are also employed. Then, the MPC is elaborated upon. Finally, the simulation and experimental results under the same scenario are compared to demonstrate how the advanced MPC can bring more benefits in terms of performance and energy saving (10%) over the conventional controllers.

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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