scholarly journals One-Step-Ahead Predictive Control for Hydroturbine Governor

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhihuai Xiao ◽  
Suili Meng ◽  
Na Lu ◽  
O. P. Malik
Keyword(s):  
Optimal Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Operating Conditions ◽  
Control Signal ◽  
Time Dependent ◽  
On Line ◽  
One Step ◽  
Wide Operating

The hydroturbine generator regulating system can be considered as one system synthetically integrating water, machine, and electricity. It is a complex and nonlinear system, and its configuration and parameters are time-dependent. A one-step-ahead predictive control based on on-line trained neural networks (NNs) for hydroturbine governor with variation in gate position is described in this paper. The proposed control algorithm consists of a one-step-ahead neuropredictor that tracks the dynamic characteristics of the plant and predicts its output and a neurocontroller to generate the optimal control signal. The weights of two NNs, initially trained off-line, are updated on-line according to the scalar error. The proposed controller can thus track operating conditions in real-time and produce the optimal control signal over the wide operating range. Only the inputs and outputs of the generator are measured and there is no need to determine the other states of the generator. Simulations have been performed with varying operating conditions and different disturbances to compare the performance of the proposed controller with that of a conventional PID controller and validate the feasibility of the proposed approach.

Generalized Predictive Control in Flying Shear Equipment

2007 ◽  
Vol 11 (9) ◽  
pp. 1144-1148
Author(s):  
Jiun-Yaw Wang ◽  
◽  
Mao-Lin Chen ◽  
Ching-Long Shih ◽  
Keyword(s):  
Predictive Control ◽  
Pid Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Generalized Predictive Control ◽  
Positioning Control ◽  
On Line ◽  
Equipment Control

To develop and evaluate improved on-line flying-shear equipment control tasks, we introduces a Generalized Predictive Control (GPC) PID controller for positioning control of a flying-shear cutter. After successful trials and excellent regulation results, the GPC-based PID control algorithm with constraints is proved to be very robust.

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 Rollover Prevention System Dedicated to ATVs on Natural Ground

2012 ◽  
Vol 162 ◽  
pp. 505-514 ◽  
Author(s):  
Mathieu Richier ◽  
Roland Lenain ◽  
Benoit Thuilot ◽  
Christophe Debain
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Load Transfer ◽  
Low Cost ◽  
Lateral Load ◽  
Dynamical Model ◽  
Rollover Prevention ◽  
Perception System ◽  
Prevention System ◽  
On Line

In this paper, an algorithm dedicated to light ATVs, which estimates and anticipates the rollover, is proposed. It is based on the on-line estimation of the Lateral Load Transfer (LLT), allowing the evaluation of dynamic instabilities. The LLT is computed thanks to a dynamical model split into two 2D projections. Relying on this representation and a low cost perception system, an observer is proposed to estimate on-line the terrain properties (grip conditions and slope), then allowing to deduce accurately the risk of instability. Associated to a predictive control algorithm, based on the extrapolation of riders action, the risk can be anticipated, enabling to warn the pilot and to consider the implementation of active actions.

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

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Mohamed L. Shaltout ◽  
Mohamed M. Alhneaish ◽  
Sayed M. Metwalli
Keyword(s):  
Optimal Control ◽  
Energy Storage ◽  
Optimal Control Problem ◽  
Model Predictive Control ◽  
Wind Power ◽  
Predictive Control ◽  
Economic Model ◽  
Control Algorithm ◽  
Fatigue Load ◽  
Economic Model Predictive Control

Abstract Wind power intermittency represents one of the major challenges facing the future growth of grid-connected wind energy projects. The integration of wind turbines and energy storage systems (ESS) provides a viable approach to mitigate the unfavorable impact on grid stability and power quality. In this study, an economic model predictive control (MPC) framework is presented for an integrated wind turbine and flywheel energy storage system (FESS). 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 baseline wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is investigated. 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 with negligible effect on the wind energy harvested.

A new one-step-ahead predictive control algorithm

2008 ◽  
Author(s):  
Yan Gang ◽  
Liang Ximing ◽  
Long Zuqiang ◽  
Li Xiang
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
One Step

scholarly journals Optimisation of Energy Use in Bioethanol Production Using a Control Algorithm

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 282
Author(s):  
Jarosław Knaga ◽  
Stanisław Lis ◽  
Sławomir Kurpaska ◽  
Piotr Łyszczarz ◽  
Marcin Tomasik
Keyword(s):  
Energy Consumption ◽  
Pid Controller ◽  
Control Algorithm ◽  
Control Signal ◽  
Control Algorithms ◽  
Signal Quality ◽  
Control Signals ◽  
And Control ◽  
Rectification Process

In this work, the possibility of limiting energy consumption in the manufacturing process of bioethanol to obtain biofuel was analysed. For this purpose, a control algorithm has been optimised while retaining the good quality of the control signals. New in this study is the correlation of the control algorithm not only with the signal’s quality, but also with the energy consumption in such an energy-intensive process as rectification. The rectification process in a periodic production system has been researched. The process was modelled on a test station with the distillation mixture capacity of 25 dm3. For the optimization, the following control algorithms have been applied: relay, PID and PID after modification to I-PD. The simulation was carried out on a transfer function model of the plant that has been verified on a real object, a rectification column. The simulations of energy consumption and control signal’s quality have been carried out in the Matlab®-Simulink environment after implementing the model of the research subject and control algorithms. In the simulation process, an interference signal with an amplitude of 3% and frequency of 2 mHz was used. The executed analyses of the control signal quality and the influence of the control algorithm on the energy consumption has shown some essential mutual relationships. The lowest energy consumption in the rectification process can be achieved using the I-PD controller—however, the signal quality deteriorates. The energy savings are slightly lower while using the PID controller, but the control signal quality improves significantly. From a practical point of view, in the considered problem the best control solution is the classic PID controller—the obtained energy effect was only slightly lower while retaining the good quality of the control signals.

scholarly journals Nonlinear predictive control of an industrial slurry reactor

2008 ◽  
Vol 19 (4) ◽  
pp. 417-430 ◽  
Author(s):  
C. H. Fontes ◽  
M. J. Mendes
Keyword(s):  
Predictive Control ◽  
High Performance ◽  
Control Algorithm ◽  
Dead Time ◽  
Nonlinear Predictive Control ◽  
Polymer Properties ◽  
One Step ◽  
Neural Network Structure ◽  
Algorithm Implementation ◽  
Mean Molecular Weight

A nonlinear model predictive control (NMPC) is applied to a slurry polymerization stirred tank reactor for the production of high-density polyethylene. Its performance is examined to reach the required mean molecular weight and comonomer composition, together with the temperature setpoint. A complete phenomenological model including the microscale, the mesoscale and the macroscale levels was developed to represent the plant. The control algorithm comprises a neural dynamic model that uses a neural network structure with a feedforward topology. The algorithm implementation considers the optimization problem, the manipulated and controlled variables adopted and presents results for the regulatory and servo problems, including the possibility of dead time and multi-rate sampling in the controlled variables. The simulation results show the high performance of the NMPC algorithm based in a model for one-step ahead prediction only, and, at the same time, attests the strong difficulty to control polymer properties with dead time in their measurements.

A Smart Predictive Control Algorithm for Heating Furnace

2011 ◽  
Vol 255-260 ◽  
pp. 2253-2256
Author(s):  
Jie Yang ◽  
Jian Bin Tang ◽  
Zhi Hua Xu
Keyword(s):  
Predictive Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
Heating Furnace ◽  
Model Parameters ◽  
Fuzzy Pid ◽  
Complex Matrix ◽  
Fuzzy Pid Controller ◽  
Heating Furnaces ◽  
Better Than

A smart predictive control algorithm which can be designed for temperature control is proposed in this paper. The control algorithm is implemented by establishing the model of heating furnaces and calculating its model parameters online. The computational load of this algorithm is very small since the complex matrix computing can be avoided. Experimental results show that the temperature controller using this algorithm has good tracking performance and little overshoot. Moreover, it is better than a fuzzy PID controller.

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