On-line, auto-tuning regulation of electronic expansion valve for evaporator control

Author(s):  
Alessandro Beghi ◽  
Luca Cecchinato ◽  
Mirco Rampazzo
Keyword(s):  
On Line ◽  
1985 ◽  
Vol 107 (4) ◽  
pp. 235-240 ◽  
Author(s):  
W.-D. Gruhle ◽  
R. Isermann

Based on the balance equations for enthalpy, mass, and momentum a theoretical model of a refrigerant evaporator has been developed. The distributed parameter process is approximated by several lumped parameter models. The model is completed by equations for the expansion valve, the compressor and the superheater. Various effects, e.g., the random fluctuations of the liquid-dry-out-point can be explained by the model. The dynamic behavior of the evaporator is investigated as a function of the manipulating signal UEV (position of the expansion valve) and various disturbances (air temperature ϑA, condenser pressure pCd and compressor rotation speed nc), considering the superheating temperature ϑs as control variable and the evaporator performance Q˙E, which has to be optimized. Two controllers are considered. First, the control behavior with a conventional thermostatic expansion valve is shown, which often operates unstable. The control performance can be considerably improved by a controller whose structure and parameters are better adapted to the evaporation process. For the experiments a process computer is connected on-line to the process. It will be demonstrated that the performance of the evaporator and therefore its efficiency can be increased by at least 5 percent.


2006 ◽  
Vol 505-507 ◽  
pp. 529-534 ◽  
Author(s):  
Chin Sheng Chen ◽  
Yu Reng Lee

This paper presented a digital servo driver that realizes an auto-tuning feedback and feedforward controller design using on-line parameters identification. Firstly, the variant inertia constant, damping constant and the disturbed load torque of the servo motor are estimated by the recursive least square (RLS) estimator, which is composed of an RLS estimator and a disturbance torque compensator. Furthermore, the auto-tuning algorithm of feedback and feedforward controller is realized according to the estimated parameters to match the tracking specification. The proposed auto-tuning digital servo controllers are evaluated and compared experimentally with a traditional controller on a microcomputer-controlled servo motor positioning system. The experimental results show that this auto-tuning digital servo system remarkably reduces the tracking error.


This paper proposes a predictive nonlinear PID neural voltage-tracking controller design for Proton Exchange Membrane Fuel Cell (PEMFC) Model with an on-line auto-tuning intelligent algorithm. The purpose of the proposed robust feedback nonlinear PID neural predictive voltage controller is to find the optimal value of the hydrogen partial pressure action in order to control the stack terminal voltage of the (PEMFC) model for one-step-ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) is utilized as a stable and intelligent robust on-line auto-tuning algorithm to obtain the near-optimal weights for the proposed controller so as to improve the performance index of the system as well as to minimize the energy consumption. The Simulation results demonstrated the effectiveness of the proposed controller compared with the linear PID neural controller.


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