Multi-Variable Adaptive Control Method for Turbofan Engine With Dynamic and Input Uncertainties

Author(s):  
Jiashuai Liu ◽  
Xi Wang ◽  
Meiyin Zhu ◽  
Keqiang Miao

Abstract The dynamic characteristics of the turbofan engine vary greatly in the full flight envelope, which makes the problem of dynamic uncertainty and input uncertainty very prominent. This brings different degrees of performance impact to the engine control system and even makes it lose stability. This paper proposes an adaptive variable parameter control method for dealing with multivariable dynamic uncertainty and input uncertainty. In this paper, the dynamic uncertainty and input uncertainty are mathematically converted into standard matched uncertainty, which can be handled more conveniently. Firstly, in the state space model, for the case where the number of state variables is less than or equal to the number of input variables and the input matrix satisfies the full-rank condition of the row, the existence of the right pseudo-inverse matrix of the input matrix can be guaranteed. So the dynamic uncertainty can be separated from the system matrix, and the input uncertainty can be separated from the input matrix. Thus these uncertainties are equivalently transformed into parametric matched uncertainty. Then the matched uncertainty model with two vectors of bounded basis functions is established. Secondly, the Lyapunov quadratic function is constructed by the closed-loop tracking error vector and the adaptively adjustable control parameter estimation errors, and the Lyapunov stability constraint is considered. Then, under the premise of considering the dynamic characteristics of the actuator, an adaptive control algorithm for multivariable matched uncertainty model of turbofan engine is derived. Finally, ground and high altitude simulations are carried out on the dual-loop control system based on the nonlinear dynamic model of the turbofan engine. The results show that the control system has robust stability and anti-interference performance for dynamic uncertainty and input uncertainty of turbofan engine in the full flight envelope. The fan speed control loop basically achieves no static error tracking. The dynamic error of the core speed control loop is less than 0.6% and the steady state error is less than 0.05%. By introducing stronger parameter change rate information to the controller, its performance can be further improved, and the transient state control is more stable.

Author(s):  
Jiashuai Liu ◽  
Xi Wang ◽  
Meiyin Zhu ◽  
Keqiang Miao

Abstract The dynamic characteristics of the turbofan engine vary greatly in the full flight envelope, which makes the problem of dynamic uncertainty and input uncertainty very prominent. This brings different degrees of performance impact to the engine control system and even makes it lose stability. This paper proposes an adaptive variable parameter control method for dealing with multivariable dynamic uncertainty and input uncertainty. Firstly, the dynamic uncertainty and input uncertainty are mathematically converted into standard matched uncertainty. Then the matched uncertainty model with two vectors of bounded basis functions is established. Secondly, this paper establishes the Lyapunov function and considers the stability constraints. Then, under the premise of considering the dynamic characteristics of the actuator, an adaptive control algorithm for multivariable matched uncertainty model of turbofan engine is derived. Finally, ground and high altitude simulations are carried out on the dual-loop control system based on the nonlinear dynamic model of the turbofan engine. The results show that the control system has robust stability and anti-interference performance for dynamic uncertainty and input uncertainty of turbofan engine in the full flight envelope. The fan speed control loop basically achieves no static error tracking. The dynamic error of the core speed control loop is less than 0.6% and the steady state error is less than 0.05%. By introducing stronger parameter change rate information to the controller, its performance can be further improved, and the transient state control is more stable.


2011 ◽  
Vol 383-390 ◽  
pp. 79-85
Author(s):  
Dong Yuan ◽  
Xiao Jun Ma ◽  
Wei Wei

Aiming at the problems such as switch impulsion, insurmountability for influence caused by nonlinearity in one tank gun control system which adopts double PID controller to realize the multimode switch control between high speed and low speed movement, the system math model is built up; And then, Model Reference Adaptive Control (MRAC) method based on nonroutine reference model is brought in and the adaptive gun controller is designed. Consequently, the compensation of nonlinearity and multimode control are implemented. Furthermore, the Tracking Differentiator (TD) is affiliated to the front of controller in order to restrain the impulsion caused by mode switch. Finally, the validity of control method in this paper is verified by simulation.


Author(s):  
Mahmood Lahroodi ◽  
A. A. Mozafari

Neural networks have been applied very successfully in the identification and control of dynamic systems. When designing a control system to ensure the safe and automatic operation of the gas turbine combustor, it is necessary to be able to predict temperature and pressure levels and outlet flow rate throughout the gas turbine combustor to use them for selection of control parameters. This paper describes a nonlinear SVFAC controller scheme for gas turbine combustor. In order to achieve the satisfied control performance, we have to consider the affection of nonlinear factors contained in controller. The neural network controller learns to produce the input selected by the optimization process. The controller is adaptively trained to force the plant output to track a reference output. Proposed Adaptive control system configuration uses two neural networks: a controller network and a model network. The model network is used to predict the effect of controller changes on plant output, which allows the updating of controller parameters. This paper presents the new adaptive SFVC controller using neural networks with compensation for nonlinear plants. The control performance of designed controller is compared with inverse control method and results have shown that the proposed method has good performance for nonlinear plants such as gas turbine combustor.


2012 ◽  
Vol 220-223 ◽  
pp. 851-854
Author(s):  
Yan Diao ◽  
Hong Ping Jia ◽  
Tian Jun Geng

The brushless DC motor control system often adopts the classic PID control, the advantages of which are as follows: simple to control, easy to adjust the parameter and a certain degree of control precision. But it relies on accurate mathematical model. The permanent magnet brushless DC motor control system is a multi-variable and nonlinear. As to the deficiencies of the classic PID control method, this thesis proposes a method called artificial neural network PID adaptive control method, which is based on algebraic algorithm and overcomes the shortcomings such as the slow convergence of BP algorithm, easy to trap in local minimum, and etc.


2012 ◽  
Vol 488-489 ◽  
pp. 1298-1301
Author(s):  
Xiong Xin Hu

In this paper, the running stability, the resonant frequency seeking ability of the novel HFFT (High frequency fatigue tester) control system are introduced. Firstly the HFFT control system model is developed. Secondly the HFFT control system dynamic characteristics is simulated and tested, which includes the dynamic load control loop and the frequency locked loop. Finally a experimental result is shown. Compare to the conventional PID control system, the novel tester’s dynamic characteristics are more preferable.


2013 ◽  
Vol 655-657 ◽  
pp. 1460-1464
Author(s):  
Xian Bin Lu ◽  
Jian Hua Wei ◽  
Rui Lin Feng ◽  
Qiang Zhang

Thrust system is the key system of casing rotator. A thrust hydraulic system with flow adaptive control is designed, operating principle and control method are elaborated as well. In order to investigate the dynamic characteristics of the thrust hydraulic system, simulation analyses with the combined platform of AMESim and MATLAB/Simulink are carried out. The simulation results show that the designed thrust hydraulic system can meet the driving velocity requirement and reduce the overflow loss.


2020 ◽  
Vol 92 (10) ◽  
pp. 1475-1481
Author(s):  
Haiyan Qiao ◽  
Hao Meng ◽  
Wei Ke ◽  
Quanxi Gao ◽  
Shaobo Wang

Purpose To improve the robustness of missile control system and reduce the error, a missile attitude adaptive control method based on active disturbance rejection control technology (ADRC) and BP neural network is innovatively proposed. Design/methodology/approach ADRC improves the performance of the missile control system by estimating and eliminating the total disturbance of the system. BP neural network adjusts the parameters of ADRC controller according to the state of the system to realize adaptive control. Based on the control system and missile dynamics model, the convergence analysis of the extended state observer and the stability analysis of the closed-loop system after embedding BP neural network are given. Findings The simulation results show that the adaptive control method can adjust the coefficient of error feedback rate according to the system input, output and error change rate, which accelerates the response speed of missile attitude angle and reduces the attitude angle error. Practical implications BP–ADRC further improves the robustness and environmental adaptability of the missile control system. The BP–ADRC control method proposed in this paper is proved feasible. Originality/value Different from the traditional ADRC, the BP–ADRC feedback signal proposed in this paper uses the output signal and its rate of the closed-loop system instead of the system state quantity estimated by extended state observer (ESO). This innovative method combined with BP neural network can make the system output meet the requirements when ESO has errors in the estimation of missile dynamics model.


2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yizhe Wang ◽  
Xiaoguang Yang ◽  
Hailun Liang ◽  
Yangdong Liu

The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles. Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects.


2021 ◽  
Vol 2113 (1) ◽  
pp. 012018
Author(s):  
Enfan Lin ◽  
Jiangning Xu ◽  
Miao Wu ◽  
Hongyang He

Abstract Aiming at the problems of strong non-linearity of gravimeter stabilisation platform system, poor robustness of linear PID control algorithm and non-adaptive control system. This paper designs a LADRC-based gravimetric stabilisation platform control system design and method based on the research of PID controller and ADRC control method, and gives the anti-saturation and anti-noise design applicable to it, and the simulation experiment shows that the method is feasible.


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