scholarly journals Application of Model Predictive Control for Large-Scale Inverted Siphon in Water Distribution System in the Case of Emergency Operation

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2733
Author(s):  
Zheli Zhu ◽  
Guanghua Guan ◽  
Zhonghao Mao ◽  
Kang Wang ◽  
Shixiang Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Distribution System ◽  
Large Scale ◽  
Control Method ◽  
Water Release ◽  
Emergency Control ◽  
Inverted Siphon ◽  
Combination System

The emergency control of Menglou~Qifang inverted siphon, which is about 72 km long, is the key to the safety of the Northern Hubei Water Transfer Project. Given the complicated layout of this project, traditional emergency control method has been challenged with the fast hydraulic transient characteristics of pressurized flow. This paper describes the application of model predictive control (MPC), a popular automatic control algorithm advanced in explicitly accounting for various constraints and optimizing control operation, in emergency condition. For the fast prediction to the pipe-canal combination system, a linear model for large-scale inverted siphon proposed by the latest research and the integrator-delay (ID) model for open canals are used. Simulation results show that the proposed MPC algorithm has promising performance on guaranteeing the safety of the project when there are sudden flow obstruction incidents of varying degrees downstream. Compared with control groups, the peak pressure can be reduced by 17.2 m by MPC under the most critical scenario, albeit with more complicated gates operations and more water release (up to 9.75 × 104 m3). Based on the linear model for long inverted siphon, this work highlights the applicability of MPC in the emergency control of large-scale pipe-canal combination system.

A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances

2021 ◽  
pp. 014233122110430
Author(s):  
Zhi Qi ◽  
Qianyue Luo ◽  
Hui Zhang
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Controller Design ◽  
Control Approach ◽  
Bounded State ◽  
Non Linear ◽  
System States ◽  
Trajectory Tracking Controller

In this paper, we aim to design the trajectory tracking controller for variable curvature duty-cycled rotation flexible needles with a tube-based model predictive control approach. A non-linear model is adopted according to the kinematic characteristics of the flexible needle and a bicycle method. The modeling error is assumed to be an unknown but bounded disturbance. The non-linear model is transformed to a discrete time form for the benefit of predictive controller design. From the application perspective, the flexible needle system states and control inputs are bounded within a robust invariant set when subject to disturbance. Then, the tube-based model predictive control is designed for the system with bounded state vector and inputs. Finally, the simulation experiments are carried out with tube-based model predictive control and proportional integral derivative controller based on the particle swarm optimisation method. The simulation results show that the tube-based model predictive control method is more robust and it leads to much smaller tracking errors in different scenarios.

scholarly journals Linear model of water movements for large-scale inverted siphon in water distribution system

2019 ◽  
Vol 21 (6) ◽  
pp. 1048-1063 ◽  
Author(s):  
Mao Zhonghao ◽  
Guan Guanghua ◽  
Yang Zhonghua ◽  
Zhong Ke
Keyword(s):  
Linear Model ◽  
Distribution System ◽  
Water Distribution ◽  
Large Scale ◽  
Low Frequency ◽  
Virtual Water ◽  
Pressure Head ◽  
Inverted Siphon ◽  
L2 Norm ◽  
Simulation Results

Abstract This paper proposes a linear model that relates the pressure head variations at the downstream end of an inverted siphon to the flow rate variations at two ends. It divides the pressure head variations in the inverted siphon into low-frequency part and high-frequency part. The two parts are caused by the deformation of the siphon wall and the reflection of acoustic wave, respectively. In order to build a simplified relation between wall deformation and low-frequency pressure head variations, the Preissmann slot method (PSM) is adopted in this paper. The linear model can also be used in other forms of structures, such as pipes and tunnels, where a pressurized flow condition is present. In comparison with simulation results using the finite volume method, the linear model shows an L2 norm of 0.177 for a large-scale inverted siphon and 0.044 for a PVC pipe. To this end, the linear model is adopted to model a large-scale inverted siphon in a virtual water delivery system. Simulation results show that the inverted siphon can reduce water fluctuations. An equation to quantify this effect is proposed based on the linear model.

scholarly journals Non-linear Model Predictive Control for Smart Heating of Buildings

2021 ◽  
Vol 246 ◽  
pp. 09005
Author(s):  
Christian Ankerstjerne Thilker ◽  
Hjörleifur G Bergsteinsson ◽  
Peder Bacher ◽  
Henrik Madsen ◽  
Davide Calì ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Renewable Energy Sources ◽  
District Heating ◽  
Heating System ◽  
Thermal Dynamics ◽  
Non Linear ◽  
Danish School

Smart and flexible operation of components in district heating systems can play a crucial role in integrating larger shares of renewable energy sources in energy systems. Buildings are one of the crucial components that will enable flexibility in the district heating by using intelligent operation. Recent work suggests that such improved operation at the same time can increase thermal comfort and lower economic costs. We have digitalised the heating system in a Danish school by adding IoT devices, such as smart thermostats and temperature sensors to demonstrate the possibilities of making buildings smart. Based on experimental data, this paper introduces a non-linear grey-box model of the thermal dynamics of the building. A non-linear model predictive control method is presented for the thermostatic set-point control of the building's radiators. Based on the building model and the control algorithm, simulation studies are carried out to show the flexibility potential of the building. When used for lowering the return temperature the results suggest that operational costs can be lowered by around 10% using predictive control.

Application of Neural Network for Nonlinear Predictive Control

2012 ◽  
Vol 562-564 ◽  
pp. 1964-1967 ◽  
Author(s):  
Zhi Cheng Xu ◽  
Bin Zhu ◽  
Qing Bin Jiang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Nonlinear Process ◽  
Effective Control ◽  
Nonlinear Predictive Control ◽  
The Stability

A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear process, which doesn’t need solve nonlinear optimization problem. Consequently, the computational efforts are reduced drastically. The simulation example shows that the proposed method is an effective control strategy with excellent tracing characteristics and strong robustness.

scholarly journals Fast Model Predictive Control Method for Large-Scale Structural Dynamic Systems: Computational Aspects

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Haijun Peng ◽  
Qiang Gao ◽  
Zhigang Wu ◽  
Wanxie Zhong
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Systems ◽  
Large Scale ◽  
Integration Method ◽  
Control Method ◽  
Matrix Exponential ◽  
Precise Integration ◽  
Structural Dynamic ◽  
The Matrix

A fast and accurate model predictive control method is presented for dynamic systems representing large-scale structures. The fast model predictive control formulation is based on highly efficient computations of the state transition matrix, that is, the matrix exponential, using an improved precise integration method. The enhanced efficiency for model predictive control is achieved by exploiting the sparse structure of the matrix exponential at each discrete time step. Accuracy is maintained using the precise integration method. Compared with the general model predictive control method, the reduced central processing unit (CPU) time required by the fast model predictive control scheme can result in a shorter control update interval and a lower online computational burden. Therefore, the proposed method is more efficient for large-scale structural dynamic systems.

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