Self-Tuning Control of a Coal-Fired Fluidized Bed Combustor

1992 ◽  
Vol 114 (1) ◽  
pp. 139-144
Author(s):  
K. W. Junk ◽  
R. R. Fullmer ◽  
R. C. Brown
Keyword(s):  
Optimal Control ◽  
Control Algorithm ◽  
Design Procedure ◽  
Gain Control ◽  
Recursive Least Squares ◽  
Self Tuning ◽  
Improved Performance ◽  
Secondary Air ◽  
Classical Control ◽  
Fluidized Bed Combustor

This paper investigates the temperature control of a two-bed fluidized combustor using a self-tuning control algorithm to vary secondary air flow rate. The controller consists of a recursive least-squares parameter estimator, an observer, and a linear optimal control design procedure. This combination enables the controller to estimate the system parameters and update the feedback gains when necessary. Further, this study addresses the tracking form of optimal control, accomplished by augmenting the state vector with an integrator. The self-tuning control algorithm was compared with a simple PI controller, which was tuned using the Ziegler-Nichols method. In this study, self-tuning control provided improved performance over classical control. Compared with conventional, constant-gain control, self-tuning control reduced steady-state variance by a factor of 6.67 while maintaining good tracking characteristics.

scholarly journals Self-Tuning Predictive Control of Nonlinear Servo-Motor

2010 ◽  
Vol 61 (6) ◽  
pp. 365-372 ◽  
Author(s):  
Vladimír Bobál ◽  
Petr Chalupa ◽  
Marek Kubalčík ◽  
Petr Dostál
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Laboratory Model ◽  
Servo Motor ◽  
Real Time Control ◽  
Highly Nonlinear ◽  
Model Control ◽  
Self Tuning

Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model — Amira DR300.

Self-Tuning Neuro-PID Controller for Indoor Entertainment Balloon Robot

2018 ◽  
Vol 30 (3) ◽  
pp. 390-396
Author(s):  
Hiroya Nagata ◽  
Soichiro Yokoyama ◽  
Tomohisa Yamashita ◽  
Hiroyuki Iizuka ◽  
Masahito Yamamoto ◽  
...  
Keyword(s):  
Pid Controller ◽  
Control Algorithm ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Target Trajectory ◽  
Self Tuning ◽  
Classical Control ◽  
Changes Over Time ◽  
Exact Ideal ◽  
Over Time

Proportional-integral-derivative (PID) controllers are a classical control algorithm that are still widely used owing to their simplicity and accuracy. However, tuning the three parameters is difficult. No methods have been known to determine the exact ideal combination of the P, I, and D gains. Moreover, controlling a system that contains dynamics changes over time using fixed parameters is difficult. A self-tuning neuro-PID controller is applied to a balloon robot for indoor entertainment to enhance its accuracy in following a target trajectory. Our experiment shows the effectiveness of the neuro-PID controller over conventional hand-tuned PID controller.

A new robust non-linear self-tuning predictive control using neural networks

1997 ◽  
Vol 211 (6) ◽  
pp. 439-446 ◽  
Author(s):  
K Y Zhu ◽  
X F Qin ◽  
T Y Chai
Keyword(s):  
Predictive Control ◽  
Feedback Linearization ◽  
Control Algorithm ◽  
Linear Part ◽  
Taylor Series Expansion ◽  
Recursive Least Squares ◽  
Non Linear ◽  
Self Tuning ◽  
Non Linear System ◽  
Robust Predictive Control

An adaptive version of a novel robust predictive control for a class of non-linear systems is presented. The non-linear system is separated into linear and non-linear parts by Taylor series expansion and then the latter part is identified by a neural network, which is then compensated in the control algorithm such that feedback linearization can be achieved. Thus the influence of the non-linearity and model uncertainties may be eliminated or reduced. In the case of time-varying or unknown systems the linear part of the system model is estimated by an RLS (recursive least-squares) algorithm. Simulation results show that the proposed scheme may improve the system performance.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

2002 ◽  
Vol 216 (3) ◽  
pp. 265-273
Author(s):  
Q M Zhu ◽  
K Warwick
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Controller Design ◽  
Design Procedure ◽  
Minimum Variance ◽  
Recursive Least Squares ◽  
Equivalent Model ◽  
Proportional Integral ◽  
Wide Range ◽  
Self Tuning

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

2018 ◽  
Author(s):  
Mohamed M. Alhneaish ◽  
Mohamed L. Shaltout ◽  
Sayed M. Metwalli
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Wind Power ◽  
Predictive Control ◽  
Economic Model ◽  
Control Algorithm ◽  
Fatigue Load ◽  
Economic Model Predictive Control ◽  
Control Framework

An economic model predictive control framework is presented in this study for an integrated wind turbine and flywheel energy storage system. The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a standard wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is studied. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load at the cost of a minimal reduction of the wind energy harvested.

scholarly journals Aero-Thermal Design and Testing of Advanced Turbine Blades

1997 ◽  
Author(s):  
M. Haendler ◽  
D. Raake ◽  
M. Scheurlen
Keyword(s):  
Gas Turbines ◽  
Full Range ◽  
Turbine Blades ◽  
Design Procedure ◽  
Thermal Design ◽  
Test Facility ◽  
Optical Pyrometer ◽  
Electrical Systems ◽  
Operation Conditions ◽  
Improved Performance

Based on the experience gained with more than 80 machines operating worldwide in 50 and 60 Hz electrical systems respectively, Siemens has developed a new generation of advanced gas turbines which yield substantially improved performance at a higher output level. This “3A-Series” comprises three gas turbine models ranging from 70 MW to 240 MW for 50 Hz and 60 Hz power generation applications. The first of the new advanced gas turbines with 170 MW and 3600 rpm was tested in the Berlin factory test facility under the full range of operation conditions. It was equipped with various measurement systems to monitor pressures, gas and metal temperatures, clearances, strains, vibrations and exhaust emissions. This paper presents the aero-thermal design procedure of the highly thermal loaded film cooled first stage blading. The predictions are compared with the extensive optical pyrometer measurements taken at the Siemens test facility on the V84.3A machine under full load conditions. The pyrometer was inserted at several locations in the turbine and radially moved giving a complete surface temperature information of the first stage vanes and blades.

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