Research on Power Regulation Schedule Control System for Turboprop Engine

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Tianqian Xia ◽  
Xianghua Huang
Keyword(s):  
Control System ◽  
Steady State ◽  
Fuel Consumption ◽  
Reference Model ◽  
Sliding Mode ◽  
Speed Control ◽  
Variable Speed ◽  
Turboprop Engine ◽  
Speed Control System ◽  
Variable Speed Control

Abstract A method of variable speed control system for turboprop engine is presented in this paper. Firstly, the steady operation state of turboprop engine is analyzed, and the operating line is figured out in the steady state characteristic diagram, which is the design basis of Engine Thrust Management System (ETMS). Secondly, the reference model sliding mode multivariable control is used to design the control law to follow the speed instructions given by ETMS. Finally, the optimization of the minimum fuel consumption operating curve is realized, and the control system designed is applied to a numerical model of a turboprop engine. The simulation results show that compared with the constant speed control system, the variable speed control system can reduce the specific fuel consumption by 2.37 % on average and 3.1 % in steady state conditions. Furthermore, the method can enable the pilot to manipulate the turboprop aircraft by using only one throttle lever, which can greatly reduce the pilot operation burden.

scholarly journals Engine Speed Control System for Improving the Fuel Efficiency of Agricultural Tractors for Plowing Operations

2019 ◽  
Vol 9 (18) ◽  
pp. 3898 ◽  
Author(s):  
Jin Woong Lee ◽  
Su Chul Kim ◽  
Jooseon Oh ◽  
Woo-Jin Chung ◽  
Hyun-Woo Han ◽  
...  
Keyword(s):  
Control System ◽  
Fuel Consumption ◽  
Engine Speed ◽  
Field Experiments ◽  
Fuel Efficiency ◽  
Speed Control ◽  
Average Error ◽  
Speed Controller ◽  
Actual Load ◽  
Speed Control System

This study was conducted to develop a load-sensitive engine speed control system to maximize the fuel efficiency of an agricultural tractor. The engine speed controller was developed through a model-based design approach using a tractor simulation model. The simulated engine speed and torque values were measured with an average error range of 1.4–4.9% compared to results obtained from field experiments. Using the tractor model, the gain parameters of the proportional–integral (PI) controller were optimized under the step, ramp, and actual load conditions. The simulation results using the actual load showed that the engine speed could be adjusted to within 2–3% of the desired value using the proposed engine speed controller. The throttle control system was constructed using four parts of a tractor engine, a microprocessor with an engine speed control algorithm, a throttle actuator, and a data acquisition system. Using the developed system, the operating engine speed values showed an average 1.17 % error compared to the desired engine speed. Three fuel efficiency parameters were used for evaluating the fuel-saving performance of the control system: specific volumetric fuel consumption (SVFC), fuel consumption per tilled area (FCA), and fuel consumption per work hour (FC). The values for SVFC, FCA, and FC obtained from the engine speed control system during plowing operations were 23.03–57.87%, 4.11–42.06%, and −7.24–38.48%, respectively, showing an improvement over the same operations without the control system.

scholarly journals Measurement, Modeling, and Optimization Speed Control of BLDC Motor Using Fuzzy-PSO Based Algorithm

2021 ◽  
Vol 5 (1) ◽  
pp. 17-25
Author(s):  
Izza Anshory ◽  
Dwi Hadidjaja ◽  
Indah Sulistiyowati
Keyword(s):  
Control System ◽  
Steady State ◽  
Rise Time ◽  
Speed Control ◽  
Physical Parameters ◽  
Control Methods ◽  
Bldc Motor ◽  
Motor Speed ◽  
Modeling And Optimization ◽  
Speed Control System

Measurement, modeling, and optimization are three important components that must be done to get a better system on the BLDC motor speed control system. The problem that arises in the BLDC motor speed control system is the instability indicated by a high overshoot value, a slow rise time value, and a high error steady-state. The purpose of this study is to increase the stability indicator by eliminating the high value of overshoot and error steady-state and increasing the value of the rise time. The method used in this research is to measure the input and output physical parameters, to model the BLDC motor plant mathematically and the last is to perform optimization using several control methods such as Proportional Integral Derivative (PID) control, fuzzy logic intelligent control, and Particle Swarm Optimization algorithm. (PSO). Experimental and simulation results show that the PSO algorithm has a better value in increasing stability indicators when compared to the other two control methods with a rise time of 0.00121 seconds, settling time of 0.00241 seconds, and overshoot of 0%.

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