Grade-change control using INCA model predictive controller: application on a Dow polystyrene process model

2004 ◽  
Author(s):  
W. Van Brempt ◽  
P. Van Overschee ◽  
T. Backx ◽  
J. Ludlage ◽  
P. Hayot ◽  
...  
Keyword(s):  
Process Model ◽  
Model Predictive Controller ◽  
Grade Change ◽  
Predictive Controller

scholarly journals Modelling and optimized water management of artificial inland waterway systems

2012 ◽  
Vol 15 (2) ◽  
pp. 348-365 ◽  
Author(s):  
J. Wagenpfeil ◽  
E. Arnold ◽  
H. Linke ◽  
O. Sawodny
Keyword(s):  
Water Management ◽  
State Estimation ◽  
Process Model ◽  
Water Levels ◽  
Estimation Methods ◽  
Strong Impact ◽  
Initial State ◽  
Optimal Control Strategy ◽  
Model Predictive Controller ◽  
Predictive Controller

A decision support system (DSS) for optimized operational water management of artificial inland waterways is presented. It will be deployed as part of a supervisory control and data acquisition (SCADA) system of the Mittellandkanal (MLK), a large canal structure in northern Germany, and relies on experience gained from a similar system. The DSS uses a model predictive controller with a 48 h prediction horizon to calculate optimal pump and discharge strategies that will ensure navigable water levels and at the same time minimize operational costs. The internal process model for the model predictive controller is obtained from a numerical integration of the Saint Venant equations using Godunov's method. The initial state needed for an accurate prediction is estimated using moving horizon state estimation (MHE) or unscented Kalman filtering. Additionally, the state estimation methods are used to estimate non-measurable disturbance inflows, which may have a strong impact on the control performance if not compensated for by the model predictive controller. The optimal control strategy is transformed into discrete-valued pump and discharge jobs that account for technical and operational input constraints. Closed-loop simulations with a high-resolution hydrodynamic numerical model of the MLK illustrate the ability of the control algorithm to adapt to model uncertainties and non-controllable inputs.

Enlarging the region of stability in robust model predictive controller based on dual-mode control

2021 ◽  
pp. 014233122110123
Author(s):  
Fatemeh Khani ◽  
Mohammad Haeri
Keyword(s):  
Stability Region ◽  
Linear Matrix ◽  
Input State ◽  
Dual Mode ◽  
Model Predictive Controller ◽  
Mode Control ◽  
Robust Model ◽  
Output Constraints ◽  
Predictive Controller ◽  
The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

scholarly journals A Perception-Aware Flatness-Based Model Predictive Controller for Fast Vision-Based Multirotor Flight

2020 ◽  
Vol 53 (2) ◽  
pp. 9412-9419
Author(s):  
Melissa Greeff ◽  
Timothy D. Barfoot ◽  
Angela P. Schoellig
Keyword(s):  
Model Predictive Controller ◽  
Predictive Controller

scholarly journals Research on Model Predictive Control for Automobile Active Tilt Based on Active Suspension

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 671
Author(s):  
Jialing Yao ◽  
Meng Wang ◽  
Zhihong Li ◽  
Yunyi Jia
Keyword(s):  
Load Transfer ◽  
Ride Comfort ◽  
Active Suspension ◽  
Path Tracking ◽  
Roll Angle ◽  
Lateral Acceleration ◽  
Vehicle Speed ◽  
Model Predictive Controller ◽  
Advantages And Disadvantages ◽  
Predictive Controller

To improve the handling stability of automobiles and reduce the odds of rollover, active or semi-active suspension systems are usually used to control the roll of a vehicle. However, these kinds of control systems often take a zero-roll-angle as the control target and have a limited effect on improving the performance of the vehicle when turning. Tilt control, which actively controls the vehicle to tilt inward during a curve, greatly benefits the comprehensive performance of a vehicle when it is cornering. After analyzing the advantages and disadvantages of the tilt control strategies for narrow commuter vehicles by combining the structure and dynamic characteristics of automobiles, a direct tilt control (DTC) strategy was determined to be more suitable for automobiles. A model predictive controller for the DTC strategy was designed based on an active suspension. This allowed the reverse tilt to cause the moment generated by gravity to offset that generated by the centrifugal force, thereby significantly improving the handling stability, ride comfort, vehicle speed, and rollover prevention. The model predictive controller simultaneously tracked the desired tilt angle and yaw rate, achieving path tracking while improving the anti-rollover capability of the vehicle. Simulations of step-steering input and double-lane change maneuvers were performed. The results showed that, compared with traditional zero-roll-angle control, the proposed tilt control greatly reduced the occupant’s perceived lateral acceleration and the lateral load transfer ratio when the vehicle turned and exhibited a good path-tracking performance.

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