Data-driven modelling and fuzzy multiple-model predictive control of oxygen content in coal-fired power plant

2016 ◽  
Vol 39 (11) ◽  
pp. 1631-1642 ◽  
Author(s):  
Huang Xiaoying ◽  
Wang Jingcheng ◽  
Zhang Langwen ◽  
Wang Bohui
Keyword(s):  
Power Plant ◽  
Real Time ◽  
Oxygen Content ◽  
Predictive Control ◽  
Tracking Control ◽  
Process Model ◽  
Combustion Process ◽  
Data Driven ◽  
Multiple Model ◽  
Real Process

In the combustion system of a boiler, oxygen content in the flue gas is a significant economic parameter for combustion efficiency. As a combustion system is highly complex and there are many constraints in a real process, traditional control cannot achieve satisfying performance in the practical oxygen content tracking control problem. In this paper, we build a combustion process model with a data-driven method and present a multiple-model-based fuzzy predictive control algorithm for the oxygen content tracking control. The combustion process model is presented as a multiple-model form, which can represent the real process more accurately. A data-driven method with fuzzy c-means clustering and subspace identification is used to identify the model parameters. Then, model predictive control integrated with a fuzzy multiple-model is used to control the oxygen content tracking problem. As the coal manipulated variable is decided by the load demand in the real process, a real-time measured value is applied to the process. All data used to obtain the process model is historical real-time data generated from a 300-MW power plant in Gui Zhou Province, China. Real-time simulation results on the 300-MW power plant show the effectiveness of the modelling and control algorithms proposed in this paper.

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

scholarly journals Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

2022 ◽  
Vol 12 (2) ◽  
pp. 682
Author(s):  
Yuzhan Wu ◽  
Chenlong Li ◽  
Changshun Yuan ◽  
Meng Li ◽  
Hao Li
Keyword(s):  
Objective Function ◽  
Real Time ◽  
Predictive Control ◽  
Tracking Control ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Velocity Signal ◽  
The Stability

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

Data-Driven Predictive Control for Autonomous Systems

2018 ◽  
Vol 1 (1) ◽  
pp. 259-286 ◽  
Author(s):  
Ugo Rosolia ◽  
Xiaojing Zhang ◽  
Francesco Borrelli
Keyword(s):  
Real Time ◽  
Predictive Control ◽  
Performance Index ◽  
Autonomous Systems ◽  
Control Policy ◽  
Data Driven ◽  
Large Set ◽  
Time Step ◽  
Time Optimal ◽  
Operating Constraints

In autonomous systems, the ability to make forecasts and cope with uncertain predictions is synonymous with intelligence. Model predictive control (MPC) is an established control methodology that systematically uses forecasts to compute real-time optimal control decisions. In MPC, at each time step an optimization problem is solved over a moving horizon. The objective is to find a control policy that minimizes a predicted performance index while satisfying operating constraints. Uncertainty in MPC is handled by optimizing over multiple uncertain forecasts. In this case, performance index and operating constraints take the form of functions defined over a probability space, and the resulting technique is called stochastic MPC. Our research over the past 10 years has focused on predictive control design methods that systematically handle uncertain forecasts in autonomous and semiautonomous systems. In the first part of this article, we present an overview of the approach we use, its main advantages, and its challenges. In the second part, we present our most recent results on data-driven predictive control. We show how to use data to efficiently formulate stochastic MPC problems and autonomously improve performance in repetitive tasks. The proposed framework is able to handle a large set of predicted scenarios in real time and learn from historical data.

Simulation of Circulating Fluidized Bed (CFB) Boiler Oxygen-Enriched Combustion

2012 ◽  
Vol 614-615 ◽  
pp. 139-142
Author(s):  
Yuan Ping Xu ◽  
Hua Zhou ◽  
Qing Yin Jiang
Keyword(s):  
Oxygen Content ◽  
Circulating Fluidized Bed ◽  
Combustion Process ◽  
Combustion Efficiency ◽  
Configuration Software ◽  
Combustion Model ◽  
Cfb Boiler ◽  
Industrial Data ◽  
Real Process ◽  
The Relationship

In this paper, we focus on the relationship between oxygen-enriched combustion efficiency and oxygen content of primary air under N2 /O2 atmosphere combustion on CFB boiler. Firstly, an entirely possible of CFB boiler oxygen-enriched combustion model was proposed. Secondly, a platform was built for simulation of CFB combustion process on XD-APC configuration software. Finally, industrial simulation with industrial data was going on to prove the platform was reasonable. The simulation results were consistent of industrial data. It shows the simulation platform reliability, and the model accuracy. On this basis, coal combustion efficiency was simulated. It shows that the combustion efficiency increases following by oxygen content increasing. It’s economic for real process when oxygen content chooses from 25% to 30%.

Sign in / Sign up

Export Citation Format

Share Document