A genetic search approach to unfalsified PI control design for a weigh belt feeder

2001 ◽  
Vol 15 (5) ◽  
pp. 519-534 ◽  
Author(s):  
Emmanuel G. Collins ◽  
Yanan Zhao ◽  
Richard Millett
Keyword(s):  
Control Design ◽  
Pi Control ◽  
Genetic Search ◽  
Belt Feeder ◽  
Search Approach

Fuzzy pi control design for an industrial weigh belt feeder

2003 ◽  
Vol 11 (3) ◽  
pp. 311-319 ◽  
Author(s):  
Yanan Zhao ◽  
E.G. Collins
Keyword(s):  
Control Design ◽  
Pi Control ◽  
Belt Feeder

Robust Decentralized PI Control Design

2014 ◽  
Vol 47 (3) ◽  
pp. 4699-4703 ◽  
Author(s):  
Petr Husek ◽  
Vladimír Kucera
Keyword(s):  
Control Design ◽  
Pi Control

Uncertainty shift in robust predictive control design for application in CAV air-conditioning systems

2011 ◽  
Vol 32 (4) ◽  
pp. 329-343 ◽  
Author(s):  
Gongsheng Huang ◽  
Tin-Tai Chow
Keyword(s):  
Air Temperature ◽  
Predictive Control ◽  
Air Conditioning ◽  
Prediction Models ◽  
Control Design ◽  
Pi Control ◽  
Control Law ◽  
Original Design ◽  
Input Structure ◽  
Air Conditioning Systems

Constant-air-volume (CAV) air-conditioning systems consist mainly of two local processes: an air-handling process and a room temperature process. A robust model predictive control (RMPC) strategy was developed for CAV air-conditioning systems, which adopted two uncertain first-order plus time-delay models to describe the dynamics of the local processes and used a linear matrix inequality (LMI)-based optimisation technique to optimise the control law. This paper develops a new control design, which reformulates the prediction models by shifting the uncertainties of the first model into the second one, and then uses the reformulated prediction models in the RMPC strategy. This paper will show that compared with the original design, the new control design can enhance the feasibility of the optimisation of control law, reduce the computational burden of the optimisation and also remove the requirement of a sensor for supply air temperature in the original design. Practical applications: The new design method is a further development of a RMPC strategy presented in Xu et al.13 It inherits the benefits of the original control design for practical application, i.e. uncertainties and constraints can be dealt with simultaneously in the design and the robustness of the controlled system can be enhanced. The new design improves the optimisation feasibility, reduces the computational complexity and does not need to measure the supply air temperature. When the new design is adopted to replace the traditional PI control, there is no necessity to change the existent input structure of the PI control. Hence, the new design can be realised in practice easier than the original design.

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