Design of Control Laws Based on Inverted Decoupling and Linear Matrix Inequality for a Turboprop Engine

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Huairong Chen ◽  
Xi Wang ◽  
Meiyin Zhu ◽  
Nannan Gu ◽  
Shubo Yang
Keyword(s):  
Linear Matrix Inequality ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Shaft Speed ◽  
Blade Angle ◽  
Gas Generator ◽  
Control Laws ◽  
Control Loops ◽  
Turboprop Engine ◽  
Power Turbine

Abstract This paper proposes a systematic approach to design control laws for a turboprop engine. The proposed approach includes interactions decoupling and control laws design based on linear matrix inequality (LMI). First, since the main objective of the turboprop engine control system is to ensure propeller-absorbed power at a constant propeller speed, the linear model of a turboprop engine can be linearized into a two-input two-output (TITO) plants, and there exist the interactions between two control loops. Because inverted decoupling can well retain the dynamic characteristics of the original system, it is used to decouple the interactions so that the TITO plant can be divided into two single-input single-output plants, that is, gas-generator shaft speed is controlled by fuel flowrate and power turbine shaft speed is controlled by blade angle. Then, the control laws are designed separately for each control loop by solving the LMI group derived from static output feedback (SOF) and regional pole placement. Finally, the proposed approach is implemented on a two-spool turboprop engine (TSTPE) integrated model. The simulation results show that there exist strong interactions between two control loops of TSTPE, applying inverted decoupling to decouple these interactions is effective, and the gas-generator shaft speed and the power turbine speed can track their commands with appropriate performance by controlling the fuel flowrate and blade angle under the action of the designed control laws and inverted decoupling.

Design of Control Laws Based on Inverted Decoupling and LMI for a Turboprop Engine

2019 ◽  
Author(s):  
Huairong Chen ◽  
Xi Wang ◽  
Meiyin Zhu ◽  
Nannan Gu ◽  
Shubo Yang
Keyword(s):  
High Pressure ◽  
Systematic Approach ◽  
Shaft Speed ◽  
Blade Angle ◽  
Fuel Flow Rate ◽  
Control Laws ◽  
Control Loops ◽  
Fuel Flow ◽  
Turboprop Engine ◽  
Power Turbine

Abstract A systematic approach of designing control laws for a turboprop engine is proposed. Firstly, the interactions between the control loops of a class of two-input two-output (TITO) plant are qualitatively analyzed. Since inverted decoupling well retains the dynamic characteristics of the original system, it is chosen to decouple the interactions so that the control loops can be divided into two single-input single-output (SISO) control loops. Then, the designed PI controller parameters for each control loop are obtained by solving linear matrix inequalities (LMIs) derived from Static Output Feedback (SOF) and regional pole placement. Considering that the main objective of turboprop engine control system is to ensure the demanded power at a constant propeller speed and component power is difficult to measure, this paper chooses fuel flow rate to control high pressure shaft speed and blade angle to control power turbine speed. Finally, this systematic approach is implemented and tested on integrated model of two-spool turboprop engine (TSTPE) on MATLAB. The simulation analysis and results of each step are presented in this paper. The simulation results show that applying inverted decoupling to decouple the interactions between the control loops of TSTPE is effective, and the designed control laws are capable of controlling fuel flow rate and blade angle of turboprop engine for high pressure shaft speed and power turbine shaft speed commands with appropriate tracking performance.

scholarly journals Analysis of Robust Stability for a Class of Stochastic Systems via Output Feedback: The LMI Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Xin-rong Cong ◽  
Long-suo Li
Keyword(s):  
Linear Matrix Inequality ◽  
Robust Stability ◽  
Output Feedback ◽  
Stochastic Systems ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Convex Optimization Problem ◽  
Control Laws ◽  
The Stability ◽  
And Control

This paper investigates the robust stability for a class of stochastic systems with both state and control inputs. The problem of the robust stability is solved via static output feedback, and we convert the problem to a constrained convex optimization problem involving linear matrix inequality (LMI). We show how the proposed linear matrix inequality framework can be used to select a quadratic Lyapunov function. The control laws can be produced by assuming the stability of the systems. We verify that all controllers can robustly stabilize the corresponding system. Further, the numerical simulation results verify the theoretical analysis results.

scholarly journals A New Robust Training Law for Dynamic Neural Networks with External Disturbance: An LMI Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Exponential Stability ◽  
External Disturbance ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Input Output ◽  
Lmi Approach ◽  
Dynamic Neural Networks ◽  
Numerical Examples

A new robust training law, which is called an input/output-to-state stable training law (IOSSTL), is proposed for dynamic neural networks with external disturbance. Based on linear matrix inequality (LMI) formulation, the IOSSTL is presented to not only guarantee exponential stability but also reduce the effect of an external disturbance. It is shown that the IOSSTL can be obtained by solving the LMI, which can be easily facilitated by using some standard numerical packages. Numerical examples are presented to demonstrate the validity of the proposed IOSSTL.

Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Linear Matrix Inequality ◽  
Color Image ◽  
Particle Swarm ◽  
Cellular Neural Network ◽  
Noise Removal ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Swarm Optimization

This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.

scholarly journals Global Dissipativity on Uncertain Discrete-Time Neural Networks with Time-Varying Delays

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
Qiankun Song ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Discrete Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Computationally Efficient ◽  
Inequality Technique ◽  
General Activation ◽  
Delay Dependent

The problems on global dissipativity and global exponential dissipativity are investigated for uncertain discrete-time neural networks with time-varying delays and general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing linear matrix inequality technique, several new delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in linear matrix inequality (LMI), which can be checked numerically using the effective LMI toolbox in MATLAB. Illustrated examples are given to show the effectiveness of the proposed criteria. It is noteworthy that because neither model transformation nor free-weighting matrices are employed to deal with cross terms in the derivation of the dissipativity criteria, the obtained results are less conservative and more computationally efficient.

scholarly journals LMI-Based Approach for Exponential Robust Stability of High-Order Hopfield Neural Networks with Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Yangfan Wang ◽  
Linshan Wang
Keyword(s):  
Neural Networks ◽  
Linear Matrix Inequality ◽  
Robust Stability ◽  
High Order ◽  
Hopfield Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequality ◽  
Global Exponential Robust Stability

This paper studies the problems of global exponential robust stability of high-order hopfield neural networks with time-varying delays. By employing a new Lyapunov-Krasovskii functional and linear matrix inequality, some criteria of global exponential robust stability for the high-order neural networks are established, which are easily verifiable and have a wider adaptive.

scholarly journals The matricial relaxation of a linear matrix inequality

2012 ◽  
Vol 138 (1-2) ◽  
pp. 401-445 ◽  
Author(s):  
J. William Helton ◽  
Igor Klep ◽  
Scott McCullough
Keyword(s):  
Linear Matrix Inequality ◽  
Linear Matrix ◽  
Matrix Inequality
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