scholarly journals Multi-Parameter Quadratic Programming Explicit Model Predictive Based Real Time Turboshaft Engine Control

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5539
Author(s):  
Nannan Gu ◽  
Xi Wang ◽  
Meiyin Zhu
Keyword(s):  
Steady State ◽  
Real Time ◽  
Predictive Control ◽  
Control Law ◽  
Rotor Systems ◽  
Piecewise Affine ◽  
Time Performance ◽  
Limit Protection ◽  
Turboshaft Engine ◽  
Engine Rotor

The traditional model predictive control (tMPC) algorithms have a large amount of online calculation, which makes it difficult to apply them directly to turboshaft engine–rotor systems because of real time requirements. Therefore, based on the theory of the perturbed piecewise affine system (PWA) and multi-parameter quadratic programming explicit model predictive control (mpQP-eMPC) algorithm, we develop a controller design method for turboshaft engine–rotor systems, which can be used for engine steady-state, transient state and limit protection control. This method consists of two steps: controller offline design and online implementation. Firstly, the parameter space of the PWA system is divided into several partitions offline based on the disturbance and performance constraints. Each partition has its own control law, which is in the form of piecewise affine linear function between the controller and the parameters. The control laws for those partitions are also obtained in this offline step. After which, for the online control implementation step, the corresponding control law can be obtained by a real-time query of a corresponding partition, which the current engine state falls into. This greatly reduces the amount of online calculation and thus improves the real-time performance of the MPC controller. The effectiveness of the proposed method is verified by simulating the steady-state and transient process of a turboshaft engine–rotor system with a limit protection requirement. Compared with tMPC, an mpQP-eMPC based controller can not only guarantee good steady-state, dynamic control performance and limit protection, but can also significantly improve the real-time performance of the control system.

scholarly journals Review and Comparison of Path Tracking Based on Model Predictive Control

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1077 ◽  
Author(s):  
Guoxing Bai ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Path Tracking ◽  
Error Model ◽  
The Real ◽  
Time Performance ◽  
Reference Path ◽  
Reference Velocity ◽  
Positioning Errors

Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile devices, such as mobile robots. The characteristics of these MPC-based controllers are not identical due to the different approaches taken during design. According to the differences in the prediction models, we believe that the existing MPC-based path tracking controllers can be divided into four categories. We named them linear model predictive control (LMPC), linear error model predictive control (LEMPC), nonlinear model predictive control (NMPC), and nonlinear error model predictive control (NEMPC). Subsequently, we built these four controllers for the same mobile robot and compared them. By comparison, we got some conclusions. The real-time performance of LMPC and LEMPC is good, but they are less robust to reference paths and positioning errors. NMPC performs well when the reference velocity is high and the radius of the reference path is small. It is also robust to positioning errors. However, the real-time performance of NMPC is slightly worse. NEMPC has many disadvantages. Like LMPC and LEMPC, it performs poorly when the reference velocity is high and the radius of the reference path is small. Its real-time performance is also not good enough.

scholarly journals A New Path Tracking Method Based on Multilayer Model Predictive Control

2019 ◽  
Vol 9 (13) ◽  
pp. 2649 ◽  
Author(s):  
Guoxing Bai ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Control Method ◽  
Control Period ◽  
Path Tracking ◽  
Positioning Systems ◽  
The Real ◽  
Time Performance ◽  
Tracking Controllers

At present, many path tracking controllers are unable to actively adjust the longitudinal velocity according to path information, such as the radius of the curve, to further improve tracking accuracy. For this problem, we propose a new path tracking framework based on model predictive control (MPC). This is a multilayer control system that includes three path tracking controllers with fixed velocities and a velocity decision controller. This new control method is named multilayer MPC. This new control method is compared to other control methods through simulation. In this paper, the maximum values of the displacement error and the heading error of multilayer MPC are 92.92% and 77.02%, respectively, smaller than those of nonlinear MPC. The real-time performance of multilayer MPC is very good, and parallel computation can further improve the real-time performance of this control method. In simulation results, the calculation time of multilayer MPC in each control period does not exceed 0.0130 s, which is much smaller than the control period. In addition, when the error of positioning systems is at the centimeter level, the performance of multilayer MPC is still good.

scholarly journals The PVC Stripping Process Predictive Control Based on the Implicit Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Shuzhi Gao ◽  
Liangliang Luan
Keyword(s):  
Real Time ◽  
Predictive Control ◽  
Temperature Control ◽  
Temperature Control System ◽  
Generalized Predictive Control ◽  
Control Law ◽  
Time Varying ◽  
Time Model ◽  
Implicit Algorithm ◽  
Online Correction

According to the nonlinear and parameters time-varying characteristics of stripper temperature control system, the PVC stripping process Generalized Predictive Control based on implicit algorithm is proposed. Firstly, supporting vector machine is adopted to dynamically modelize for the stripper temperature; Secondly, combining with real-time model linearized of nonlinear model, a predictive model is linearized for real-time online correction. Then, the implicit algorithm is used for optimal control law. Finally, the simulation results show that the algorithm has excellent validity and robustness of temperature control of the stripper.

scholarly journals Explicit GPC Control Applied to an Approximated Linearized Crane System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Daniel Guerra Vale da Fonseca ◽  
André Felipe O. de A. Dantas ◽  
Carlos Eduardo Trabuco Dórea ◽  
André Laurindo Maitelli
Keyword(s):  
Quadratic Programming ◽  
Predictive Control ◽  
Gantry Crane ◽  
Generalized Predictive Control ◽  
Control Law ◽  
Time Step ◽  
Constraint Violation ◽  
Piecewise Affine ◽  
Output Constraints ◽  
Overall Performance

This paper proposes a MIMO Explicit Generalized Predictive Control (EGPC) for minimizing payload oscillation of a Gantry Crane System subject to input and output constraints. In order to control the crane system efficiently, the traditional GPC formulation, based on online Quadratic Programming (QP), is rewritten as a multiparametric quadratic programming problem (mp-QP). An explicit Piecewise Affine (PWA) control law is obtained and holds the same performance as online QP. To test effectiveness, the proposed method is compared with two GPC formulations: one that handle constraints (CGPC) and another that does not handle constraints (UGPC). Results show that both EGPC and CGPC have better performance, reducing the payload swing when compared to UGPC. Also both EGPC and CGPC are able to control the system without constraint violation. When comparing EGPC to CGPC, the first is able to calculate (during time step) the control action faster than the second. The simulations prove that the overall performance of EGPC is superior to the other used formulations.

Event-triggered nonlinear model predictive control for trajectory tracking of unmanned vehicles

2021 ◽  
pp. 095440702199216
Author(s):  
Kai Zou ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Xiaoqiang Sun
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Unmanned Vehicles ◽  
Tracking Accuracy ◽  
Model Predictive Controller ◽  
Time Performance ◽  
Predictive Controller ◽  
Event Triggered

In order to increase the real-time performance of lateral trajectory tracking of unmanned vehicles, this paper designs an event-triggered nonlinear model predictive controller, which can save computation resource to a large extent while the tracking accuracy is still guaranteed. Firstly, a simplified vehicle is established using a two-degree-of-freedom dynamics model. Then, according to the theory of model predictive control, a nonlinear model predictive controller (NMPC) is designed. Since traditional NMPCs often have poor real-time control performance, this paper introduces an event-triggered mechanism, which allows the remaining elements of the control variables in the control horizon to be applied to the system once a specific condition is satisfied. Finally, the proposed controller is established by Matlab/Simulink, and the different trigger conditions are compared and verified in a double lane change maneuvers Then a system for evaluation is designed to quantify the performance of the controller in different trigger conditions. For further verification of the proposed controller, a Hard-in-the-loop simulation system based on Xpack package is established to conduct an HIL experiment. The results show that compared with traditional nonlinear model predictive control, our method offers greatly improved real-time performance while the tracking accuracy is guaranteed.

Fast Model Predictive Control for Aircraft Engine Based on Automatic Differentiation

2020 ◽  
Author(s):  
Yunlai Wang ◽  
Xi Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Model ◽  
Real Time ◽  
Predictive Control ◽  
Automatic Differentiation ◽  
Hessian Matrix ◽  
Aircraft Engine ◽  
Component Level ◽  
The Real ◽  
Time Performance

Abstract Nonlinear model predictive control (NMPC) is a strategy suitable for dealing with highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. Because of the complexity of the algorithm and the real-time performance of the predictive model, it has thus far been infeasible to implement model predictive control in the realtime control system of aircraft engine. In most nonlinear model predictive control, nonlinear interior point methods (IPM) are used to calculate the optimal solution, which iterate to the optimal solution based on the Jacobian and Hessian matrix. Most nonlinear IPM solver, such as MATLAB fmincon and IPOPT, cannot calculate the Jacobian and Hessian matrix precisely and quickly, instead of using numerical differentiation to calculate the Jacobian matrix and BFGS method to approach to the Hessian matrix. From what has been discussed above, we will 1) improve the real-time performance of predictive model by replacing the time-consuming component level model (CLM) with a neural network model, which is trained based on the data of component level model, 2) precisely calculate the Jacobian and Hessian matrix using automatic differentiation, and propose a group of algorithms to make NMPC strategy quicker, which include making use of the structure of predictive model, and the integrity of weighted sums of Hessian matrix in IPM. Finally, considering input and output constraints, the fast NMPC strategy is compared with normal NMPC. Simulation results with mean time of 19.3% – 27.9% of normal NMPC on different platforms, verify that the fast NMPC proposed can improve the real-time performance during the process of acceleration, deceleration for aircraft engine.

scholarly journals Robust Tube-Based MPC with Piecewise Affine Control Laws

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Meng Zhao ◽  
Xiaoming Tang
Keyword(s):  
Predictive Control ◽  
Discrete Time ◽  
Computation Time ◽  
Control Law ◽  
Control Laws ◽  
Piecewise Affine ◽  
Online Computation ◽  
Bounded Disturbances ◽  
Time Linear ◽  
Constraint Sets

This paper presents a tube-based model predictive control (MPC) algorithm with piecewise affine control laws for discrete-time linear systems in the presence of bounded disturbances. By solving the standard multiparametric quadratic programming (mp-QP), the explicit piecewise affine control laws for tube-based MPC are obtained. Each control law is piecewise affine with respect to the corresponding region (one of the partitions of the feasible set). Due to the fact that the above-mentioned procedures are totally offline, the online computation time is short enough for stabilizing those systems with fast dynamics. In this paper, all the involved constraint sets are assumed to be polytopes. An illustrative example is utilized to verify the feasibility and efficiency of the proposed algorithm.

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