Aero-engine direct thrust control with nonlinear model predictive control based on linearized deep neural network predictor

Zheng, Qiangang; Yong, Wang; Sun, Fengyong; Zhang, Haibo

doi:10.1177/0959651819853395

Cited by 11 publications

(7 citation statements)

References 29 publications

(51 reference statements)

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“…In this study, the MPC controller was designed to identify the engine control modes, which was achieved by using Lagrangian multipliers to handle the constraint inequalities and Hildreth's quadratic programming to select the controller weighting values [103]. To improve the engine response performance and reduce the computational complexity, direct thrust control with nonlinear MPC was proposed based on a linearized online sliding-window deep neural network predictor [104]. For application in EAP systems, a coordinated MPC controller has been developed to achieve the setpoint tracking of large transient thrust and electrical loads whilst ensuring system constraints [56,105].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control

2022

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The civil aviation sector plays an increasingly significant role in transportation sustainability in the environmental, economic, and social dimensions. Driven by the concerns of sustainability in the aviation sector, more electrified aircraft propulsion technologies have emerged and form a very promising approach to future sustainable and decarbonized aviation. This review paper aims to provide a comprehensive and broad-scope survey of the recent progress and development trends in sustainable aviation electrification. Firstly, the architectures of electrified aircraft propulsion are presented with a detailed analysis of the benefits, challenges, and studies/applications to date. Then, the challenges and technical barriers of electrified aircraft propulsion control system design are discussed, followed by a summary of the control methods frequently used in aircraft propulsion systems. Next, the mainstream energy management strategies are investigated and further utilized to minimize the block fuel burn, emissions, and economic cost. Finally, an overview of the development trends of aviation electrification is provided.

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Section: Model Predictive Controlmentioning

confidence: 99%

Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control

2022

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“…Experiments show that a three-layer BP neural network can complete a mapping from any n-dimensional input layer to m-dimensional output layer. [28][29][30] This paper adopts a three-layer BP neural network model, as shown in Figure 5, w ih and w hj are the weights between layers, and q is the bias.…”

Section: The Basic Network Structurementioning

confidence: 99%

Optimization of milling process parameters and prediction of abrasive wear rate increment based on cutting force experiment

Liu

2021

Advances in Mechanical Engineering

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Tuning the parameters of Computerized Numerical Control (CNC) is essential for practical manufacturers. Well configured parameters ensure the efficiency of production and the accuracy of the products. However, with the abrasive wear on the flank of the milling cutter, the milling processing parameters should re-configure to adapt to the increment of the abrasive wear. This paper aims to propose a method to predict the abrasive wear rate increment on the flank of the milling cutter and optimize the processing parameters of CNC milling. Firstly, we set a cutting data acquisition system to sample the processing time and cutting force among X, Y coordinates based on the five-factor and four-level orthogonal experiments. Then, the sampled cutting force data increment is transformed into the abrasive wear rate increment by applying the incremental model. Next, five processing parameters for CNC milling are optimized by the gray relational method, which takes the limited abrasive wear rate increment of the flank face and the non-increasing processing time as the constrained conditions. We obtain the relationship between five processing parameters and abrasive wear rate increment. We also find the basic principle of selecting process parameters is to reduce the abrasive wear rate without increasing the processing time. The experimental results verify that the optimized process parameters make the gray relational degree increase by 0.02, and the abrasive wear rate increment decreases by 0.42432 × 10−10 mm3/s without affecting the production efficiency. In the prediction section, by applying the Back Propagation (BP) neural network, we obtain an accurate prediction model from measurable five factors to the abrasive wear increment on the flank of the milling cutter. The maximum error between the predicted value and the actual value is 0.0003, and the predicted value curve fits well with the actual value curve. From the perspective of abrasive wear rate increment prediction, it provides a new idea for online tool wear monitoring.

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“…The MPC controller choses the optimal control law in addition to the prediction of next control in advance. [19][20][21] The entire analytical model of the power rectifier and its future performance are considered while controlling the active powers and reactive powers. Therefore, it is a promising solution and popular strategy for the power converters because of having many benefits, such as lack of modulator, fast dynamic response, and the controller flexibility in terms of system requirements.…”

Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode predictive power control of three-phase AC/DC converters

Zad

Ulasyar

Zohaib

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a new adaptive sliding mode–based model predictive controller for AC/DC three-phase converters achieving better dynamic performance and stability. In the classical model predictive controller available in the literature, the model-based approach, for example, proportional–integral controller is employed for producing the active power reference for the three-phase converters. The traditional proportional–integral–based model predictive controllers consist of steady-state error and slow transient response characteristics. As a result, the DC-link voltage contains uncertainties due to variations in the load demand and output voltage. To overcome these limitations, this article suggests an adaptive sliding mode controller for generating the active power reference value from the DC-link voltage which then combines with the model predictive controller in order to minimize the cost function. The proposed controller minimizes the effects of uncertainties and variations in the output voltage by adaptively regulating the gain of sliding mode controller and modifying the control law online. The cost function is then minimized using the model predictive controller in order to control the active and reactive power flow. The stability analysis of the designed controller is performed using Lyapunov theorem. The effectiveness of the designed control scheme is proved by comparing its performance with the proportional–integral model predictive controller and fixed gain sliding mode–based model predictive controller control schemes. Simulation and experimental system results are obtained for validating the presented control approach.

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Aero-engine direct thrust control with nonlinear model predictive control based on linearized deep neural network predictor

Cited by 11 publications

References 29 publications

Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control

Sustainable Aviation Electrification: A Comprehensive Review of Electric Propulsion System Architectures, Energy Management, and Control

Optimization of milling process parameters and prediction of abrasive wear rate increment based on cutting force experiment

Adaptive sliding mode predictive power control of three-phase AC/DC converters

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