Comparative Controller Design for a Marine Gas Turbine Propulsion System

1990 ◽  
Vol 112 (2) ◽  
pp. 182-186 ◽  
Author(s):  
D. L. Smith ◽  
V. A. Stammetti

Controller design for marine gas turbine systems should consider three measures of performance: transient control, steady-state accuracy, and disturbance rejection. This paper presents and compares two common types of controller design in terms of these measures. The goal of the controllers was shaft speed control. To meet this goal, a classical Proportional-plus-Integral controller was designed and compared to a modern Linear Quadratic Regulator design. The controllers’ performances were evaluated with respect to the three measures mentioned above, with disturbances being input as oscillations in shaft torque due to seaway cycling.

Author(s):  
David L. Smith ◽  
Vincent A. Stammetti

Controller design for marine gas turbine systems should consider three measures of performance: transient control, steady state accuracy, and distrubance rejection. This paper presents and compares two common types of controller designs in terms of these measures. The goal of the controllers was shaft speed control. To meet this goal, a classical Proportional-plus-integral controller was designed and compared to a modern Linear Quadratic Regulator design. The controllers’ performances were evaluated with respect to the three measures mentioned above, with disturbances being input as oscillations in shaft torque due to seaway cycling.


Author(s):  
Shusheng Zang ◽  
Jaqiang Pan

The design of a modern Linear Quadratic Regulator (LQR) is described for a test steam injected gas turbine (STIG) unit. The LQR controller is obtained by using the fuel flow rate and the injected steam flow rate as the output parameters. To meet the goal of the shaft speed control, a classical Proportional Differential (PD) controller is compared to the LQR controller design. The control performance of the dynamic response of the STIG plant in the case of rejection of load is evaluated. The results of the computer simulation show a remarkable improvement on the dynamic performance of the STIG unit.


Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 477 ◽  
Author(s):  
S. Augusti Lindiya ◽  
N. Subashini ◽  
K. Vijayarekha

Single Inductor (SI) converters with the advantage of using one inductor for any number of inputs/outputs find wide applications in portable electronic gadgets and electrical vehicles. SI converters can be used in Single Input Multiple Output (SIMO) and Multiple Input Multiple Output (MIMO) configurations but they need controllers to achieve good transient and steady state responses, to improve the stability against load and line disturbances and to reduce cross regulation. Cross regulation is the change in an output voltage due to change in the load current at another output and it is an added constraint in SI converters. In this paper, Single Input Dual Output (SIDO) and Dual Input Dual Output (DIDO) converters with applications capable of handling high load current working in Continuous Conduction Mode (CCM) of operation are taken under study. Conventional multivariable PID and optimal Linear Quadratic Regulator (LQR) controllers are developed and their performances are compared for the above configurations to meet the desired objectives. Generalized mathematical models for SIMO and MIMO are developed and a Genetic Algorithm (GA) is used to find the parameters of a multivariable PID controller and the weighting matrices of optimal LQR where the objective function includes cross regulation as a constraint. The simulated responses reveal that LQR controller performs well for both the systems over multivariable PID controller and they are validated by hardware prototype model with the help of DT9834® Data Acquisition Module (DAQ). The methodologies used here generate a fresh dimension for the case of such converters in practical applications.


Author(s):  
J. W. Watts ◽  
T. E. Dwan ◽  
R. W. Garman

A two-and-one-half spool gas turbine engine was modeled using the Advanced Computer Simulation Language (ACSL), a high level simulation environment based on FORTRAN. A possible future high efficiency engine for powering naval ships is an intercooled, regenerated (ICR) gas turbine engine and these features were incorporated into the model. Utilizing sophisticated instructions available in ACSL linear state-space models for this engine were obtained. A high level engineering computational language, MATLAB, was employed to exercise these models to obtain optimal feedback controllers characterized by the following methods: (1) state feedback; (2) linear quadratic regulator (LQR) theory; and (3) polygonal search. The methods were compared by examining the transient curves for a fixed off-load, and on-load profile.


Author(s):  
Soukaina Krafes ◽  
Zakaria Chalh ◽  
Abdelmjid Saka

This paper presents a Backstepping controller for five degrees of freedom Spherical Inverted Pendulum. Since the system is nonlinear, unstable, underactuated and MIMO and has a nonsquare form, the classic control design cannot be applied to control it. In order to remedy this problem, we propose in this paper a new method based on hierarchical steps of the Backstepping controller taking into a count the nonlinearities that cannot be neglected. Furthermore, a Linear Quadratic Regulator controller and LQR + PID based on the linearized system model are also designed for performance comparison. Finally, a simulation study is carried out to prove the effectiveness of proposed control scheme and is validated using the virtual reality environment that proves the performance of the Backstepping controller over the linear ones where it brings the pendulum from any initial condition in the upper hemisphere while the base is brought to the origin of the coordinates.


2011 ◽  
Vol 63-64 ◽  
pp. 533-536
Author(s):  
Xiao Jun Xing ◽  
Jian Guo Yan

With the purpose of overcoming the defect that unmanned air vehicles (UAVs) are easily disturbed by air current and tend to be unstable, an augmented-stability controller was developed for a certain UAV’s longitudinal motion. According to requirements of short-period damping ratio and control anticipation parameter (CAP) in flight quality specifications of GJB185-86 and C*, linear quadratic regulator (LQR) theory was used in the augmented-stability controller’s design. The simulation results show that the augmented-stability controller not only improves the UAV’s stability and dynamic characteristics but also enhances the UAV’s robustness.


2011 ◽  
Vol 110-116 ◽  
pp. 4977-4984 ◽  
Author(s):  
R.A. Khoshrooz ◽  
M.A.D. Vahid ◽  
M. Mirshams ◽  
M.R. Homaeinezhad ◽  
A.H. Ahadi

This paper presents a method to solve the Evolutionary Algorithm (EA) problems for optimal tuning of the Proportional-Deferential (PD) controller parameters. The major efficiency of the proposed method is the Genetic Algorithm (GA) stuck avoidance as well an appropriate estimation for GA lower and upper bounds. Also by this method for the Particle Swarm Optimization (PSO) methodology the initial choice of the controller parameters can be fulfilled to achieve the acceptable performance accuracies. For both GA and PSO methods, the Linear Quadratic Regulator (LQR) obtained trend is used as the reference for the determination of the aforementioned bounds and initial guess. The presented algorithm was applied to regulate a PD controller for the attitude control of a virtual satellite and also with Hardware-in-the-loop (HIL) reaction wheels. Heavy burden trying and error was eliminated from the PD controller design which can be mentioned as the important merit of the presented study.


Author(s):  
M. Montazeri-Gh. ◽  
D. J. Allerton ◽  
R. L. Elder

This paper describes an actuator placement methodology for the active control of purely one-dimensional instabilities of a seven-stage axial compressor using an air bleeding strategy. In this theoretical study, using stage-by-stage non-linear modelling based on the conservation equations of mass, momentum, and energy, a scheduling LQR (Linear Quadratic Regulator) controller is designed for several actuator locations in a compressor from the first stage to the plenum. In this controller design, the LQR weighting matrices are selected so that the associated cost function includes only air bleeding mass flow leading to the minimisation of the air bleed. The LQR cost function represents a measure of the consumption of air bleeding and can be calculated analytically using the solution of an Algebraic Riccati Equation. From analysis of the cost at different compressor stages, the location of an air bleeding actuator is selected at the stage with the minimum cost. Finally, using an ACSL simulation program, the scheduling controller has been integrated with a non-linear. stage-by-stage model and the time response of the air bleeding mass flow at different locations has been obtained to confirm the results from the analytical approach. Results are presented to show actively stabilised compressor flow beyond the surge point where the air bleed is minimised. These results also indicate the preferred location of the actuator at the compressor downstream stages for both low and high compressor speeds.


Author(s):  
Hanum Arrosida ◽  
Mohammad Erik Echsony

Nowadays, quadcopter motion control has become a popular research topic because of its versatile ability as an unmanned aircraft can be used to alleviate human labor and also be able to reach dangerous areas or areas which is unreachable to humans. On the other hand, the Optimal PID control method, which incorporates PID and Linear Quadratic Regulator (LQR) control methods, has also been widely used in industry and research field because it has advantages that are easy to operate, easy design, and a good level of precision. In the PID control method, the main problem to be solved is the accuracy of the gain value Kp, Ki, and Kd because the inappropriateness of those value will result in an imprecise control action. Based on these problems and referring to the previous study, the optimal PID control method was developed by using PID controller structure with tuning gain parameter of PID through Linear Quadratic Regulator (LQR) method. Through the integration of these two control methods, the optimum solutions can be obtained: easier controller design process for quadcopter control when crossing the determined trajectories, steady state error values less than 5% and a stable quadcopter movement with roll and pitch angle stabilization at position 0 radians with minimum energy function.


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