scholarly journals Modified Volterra model-based non-linear model predictive control of IC engines with real-time simulations

2016 ◽  
Vol 39 (2) ◽  
pp. 208-223 ◽  
Author(s):  
Yiran Shi ◽  
Ding-Li Yu ◽  
Yantao Tian ◽  
Yaowu Shi
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Linear Model ◽  
Predictive Control ◽  
Fuel Injection ◽  
Volterra Model ◽  
Ic Engine ◽  
Model Based ◽  
Significant Difference ◽  
Non Linear

Modelling of non-linear dynamics of an air manifold and fuel injection in an internal combustion (IC) engine is investigated in this paper using the Volterra series model. Volterra model-based non-linear model predictive control (NMPC) is then developed to regulate the air–fuel ratio (AFR) at the stoichiometric value. Due to the significant difference between the time constants of the air manifold dynamics and fuel injection dynamics, the traditional Volterra model is unable to achieve a proper compromise between model accuracy and complexity. A novel method is therefore developed in this paper by using different sampling periods, to reduce the input terms significantly while maintaining the accuracy of the model. The developed NMPC system is applied to a widely used IC engine benchmark, the mean value engine model. The performance of the controlled engine under real-time simulation in the environment of dSPACE was evaluated. The simulation results show a significant improvement of the controlled performance compared with a feed-forward plus PI feedback control.

scholarly journals A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3335 ◽  
Author(s):  
Bo Wang ◽  
Muhammad Shahzad ◽  
Xianglin Zhu ◽  
Khalil Ur Rehman ◽  
Saad Uddin
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Linear Model ◽  
Predictive Control ◽  
Least Square ◽  
Support Vector ◽  
Product Concentration ◽  
Grey Wolf Optimization ◽  
Non Linear

l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used as a process model in the non-linear model predictive control to predict product concentration. GWO is also used to solve the non-convex optimization problem in non-linear model predictive control (GWO-NMPC) for calculating optimal future inputs. The proposed GWO-based prediction model (GWO-LSSVM) and non-linear model predictive control (GWO-NMPC) are compared with the Particle Swarm Optimization (PSO)-based prediction model (PSO-LSSVM) and non-linear model predictive control (PSO-NMPC) to validate their effectiveness. The comparative results show that the prediction accuracy, adaptability, real-time tracking ability, overall error and control precision of GWO-based predictive control is better compared to PSO-based predictive control.

Non-linear model based predictive control

1999 ◽  
Vol 72 (10) ◽  
pp. 919-928 ◽  
Author(s):  
B. Kouvaritakis ◽  
M. Cannon ◽  
J. A. Rossiter
Keyword(s):  
Linear Model ◽  
Predictive Control ◽  
Model Based ◽  
Non Linear

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 Demonstration of non-linear model predictive control of post-combustion CO2 capture processes

2019 ◽  
Vol 123 ◽  
pp. 184-195 ◽  
Author(s):  
S.O. Hauger ◽  
N. Enaasen Flø ◽  
H. Kvamsdal ◽  
F. Gjertsen ◽  
T. Mejdell ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Co2 Capture ◽  
Non Linear

scholarly journals Efficient move blocking strategy for multiple shooting-based non-linear model predictive control

2020 ◽  
Vol 14 (2) ◽  
pp. 343-351 ◽  
Author(s):  
Yutao Chen ◽  
Nicoló Scarabottolo ◽  
Mattia Bruschetta ◽  
Alessandro Beghi
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Multiple Shooting ◽  
Non Linear
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