scholarly journals Triple-Mode Model Predictive Control Using Future Target Information

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 54
Author(s):  
Minghao Chen ◽  
Zuhua Xu ◽  
Jun Zhao
Keyword(s):  
Model Predictive Control ◽  
Control Problem ◽  
Predictive Control ◽  
Degrees Of Freedom ◽  
Golden Section ◽  
Tracking Performance ◽  
Control Law ◽  
Target Information ◽  
The Future ◽  
Triple Mode

In this paper, we propose a triple-mode model predictive control (MPC) algorithm that uses future target information to improve tracking performance. To explicitly take into account the future target information in the MPC optimization, the proposed triple-mode control law encompasses three parts: (i) the future target information feedforward, (ii) the output feedback, and (iii) the extra degrees of freedom for constraint satisfaction. The first two parts of the control law are off-line designed through unconstrained MPC, and the optimal future trajectory horizon is obtained by golden section search based on the integral of squared error (ISE) criterion. The final part is calculated by the on-line MPC algorithm aiming to satisfy constraints. Furthermore, we analyze the feasibility and convergence properties of the proposed algorithm. The method is demonstrated by the simulation of the shell fundamental control problem and also tested on the coordinated control problem in the power plant. The test results show that the proposed algorithm can increase tracking performance dramatically due to the proper selection of future trajectory horizon.

Move blocking Model Predictive Control of a helicopter with three degrees of freedom

2020 ◽  
Author(s):  
Rogério Toniere Giovanelli ◽  
Rubens Junqueira Magalhães Afonso
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Degrees Of Freedom ◽  
Stable Region ◽  
Linear Matrix ◽  
Feedback Gain ◽  
Control Law ◽  
Matrix Inequalities ◽  
Blocking Algorithm ◽  
Three Degrees Of Freedom

This work presents the use of a move blocking algorithm in the Model Predictive Control (MPC) of a helicopter with three degrees of freedom (3DoF). Considerations about the feasibility of the MPC solutions and robustness of the control law are developed to propose an internal feedback gain array using Linear Matrix Inequalities (LMIs). The objective of this structure is to grant adjustment  exibility of the plant dynamics through a D-stable region and to reduce the computational complexity of the problem.

Model Predictive Control of Fractional Order Systems

2014 ◽  
Vol 9 (3) ◽  
Author(s):  
Aymen Rhouma ◽  
Faouzi Bouani ◽  
Badreddine Bouzouita ◽  
Mekki Ksouri
Keyword(s):  
Model Predictive Control ◽  
Dynamic Behavior ◽  
Fractional Order ◽  
Predictive Control ◽  
Internal Model ◽  
Direct Method ◽  
Control Law ◽  
Thermal System ◽  
Fractional Order Systems ◽  
Derivation Operator

This paper provides the model predictive control (MPC) of fractional order systems. The direct method will be used as internal model to predict the future dynamic behavior of the process, which is used to achieve the control law. This method is based on the Grünwald–Letnikov's definition that consists of replacing the noninteger derivation operator of the adopted system representation by a discrete approximation. The performances and the efficiency of this approach are illustrated with practical results on a thermal system and compared to the MPC based on the integer ARX model.

scholarly journals A Wind Farm Active Power Dispatch Strategy Considering the Wind Turbine Power-Tracking Characteristic via Model Predictive Control

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 530 ◽  
Author(s):  
Wei Li ◽  
Dean Kong ◽  
Qiang Xu ◽  
Xiaoyu Wang ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wind Turbines ◽  
Control Strategy ◽  
Wind Farm ◽  
Control Object ◽  
Automatic Generation ◽  
Active Power ◽  
Tracking Performance ◽  
Equivalent Model

In this paper, an industrial application-oriented wind farm automatic generation control strategy is proposed to stabilize the wind farm power output under power limitation conditions. A wind farm with 20 units that are connected beneath four transmission lines is the selected control object. First, the power-tracking dynamic characteristic of wind turbines is modeled as a first-order inertial model. Based on the wind farm topology, the wind turbines are grouped into four clusters to fully use the clusters’ smoothing effect. A method for frequency-domain aggregation and approximation is used to obtain the clusters’ power-tracking equivalent model. From the reported analysis, a model predictive control strategy is proposed in this paper to optimize the rapidity and stability of the power-tracking performance. In this method, the power set-point for the wind farm is dispatched to the clusters. Then, the active power control is distributed from the cluster to the wind turbines using the conventional proportional distribution strategy. Ultra-short-term wind speed prediction is also included in this paper to assess the real-time performance. The proposed strategy was tested using a simulated wind farm based on an industrial wind farm. Good power-tracking performance was achieved in several scenarios, and the results demonstrate that the performance markedly improved using the proposed strategy compared with the conventional strategy.

Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoidance system with move blocking strategy

2017 ◽  
Vol 232 (10) ◽  
pp. 1353-1373 ◽  
Author(s):  
Umar Zakir Abdul Hamid ◽  
Hairi Zamzuri ◽  
Tsuyoshi Yamada ◽  
Mohd Azizi Abdul Rahman ◽  
Yuichi Saito ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Potential Field ◽  
Autonomous Vehicle ◽  
Nonlinear Model Predictive Control ◽  
Tracking Performance ◽  
Artificial Potential Field ◽  
Nonlinear Controller

The collision avoidance (CA) system is a pivotal part of the autonomous vehicle. Ability to navigate the vehicle in various hazardous scenarios demands reliable actuator interventions. In a complex CA scenario, the increased nonlinearity requires a dependable control strategy. For example, during collisions with a sudden appearing obstacle (i.e. crossing pedestrian, vehicle), the abrupt increment of vehicle longitudinal and lateral forces summation during the CA maneuver demands a system with the ability to handle coupled nonlinear dynamics. Failure to address the aforementioned issues will result in collisions and near-miss incidents. Thus, to solve these issues, a nonlinear model predictive control (NMPC)-based path tracking strategy is proposed as the automated motion guidance for the host vehicle CA architecture. The system is integrated with the artificial potential field (APF) as the motion planning strategy. In a hazardous scenario, APF measures the collision risks and formulates the desired yaw rate and deceleration metrics for the path replanning. APF ensures an optimal replanned trajectory by including the vehicle dynamics into its optimization formulation. NMPC then acts as the coupled path and speed tracking controller to enable vehicle navigation. To accommodate vehicle comfort during the avoidance, NMPC is constrained. Due to its complexity as a nonlinear controller, NMPC can be time-consuming. Therefore, a move blocking strategy is assimilated within the architecture to decrease the system’s computational burden. The modular nature of the architecture allows each strategy to be tuned and developed independently without affecting each others’ performance. The system’s tracking performance is analyzed by computational simulations with several CA scenarios (crossing pedestrian, parked bus, and sudden appearing moving vehicle at an intersection). NMPC tracking performance is compared to the nominal MPC and linear controllers. The effect of move blocking strategies on NMPC performance are analyzed, and the results are compared in terms of mean squared error values. The inclusion of nonlinear tracking controllers in the architecture is shown to provide reliable CA actions in various hazardous scenarios. The work is important for the development of a reliable controller strategy for multi-scenario CA of the fully autonomous vehicle.

scholarly journals Modeling and Control of Crane Overload Protection During Marine Lifting Operation Based on Model Predictive Control

2017 ◽  
Author(s):  
Zhengru Ren ◽  
Roger Skjetne ◽  
Zhen Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Multiple Shooting ◽  
Control Input ◽  
Lumped Mass ◽  
Modeling And Control ◽  
Overload Protection ◽  
And Control

This paper deals with a nonlinear model predictive control (NMPC) scheme for a winch servo motor to overcome the sudden peak tension in the lifting wire caused by a lumped-mass payload at the beginning of a lifting off or a lowering operation. The crane-wire-payload system is modeled in 3 degrees of freedom with the Newton-Euler approach. Direct multiple shooting and real-time iteration (RTI) scheme are employed to provide feedback control input to the winch servo. Simulations are implemented with MATLAB and CaSADi toolkit. By well tuning the weighting matrices, the NMPC controller can reduce the snatch loads in the lifting wire and the winch loads simultaneously. A comparative study with a PID controller is conducted to verify its performance.

Actuator fault-tolerance evaluation of linear constrained model predictive control using zonotope-based set computations

2007 ◽  
Vol 221 (6) ◽  
pp. 915-926 ◽  
Author(s):  
C Ocampo-Martinez ◽  
P Guerra ◽  
V Puig ◽  
J Quevedo
Keyword(s):  
Fault Tolerance ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Admissible Solution ◽  
Control Law ◽  
Actuator Fault ◽  
Control Objective ◽  
Constrained Model ◽  
Constrained Model Predictive Control ◽  
Feasible Solutions

This paper presents a computational procedure to evaluate the fault tolerance of a linear-constrained model predictive control (LCMPC) scheme for a given actuator fault configuration (AFC). Faults in actuators cause changes in the constraints related to control signals (inputs), which in turn modify the set of MPC feasible solutions. This fact may result in an empty set of admissible solutions for a given control objective. Therefore, the admissibility of the control law facing actuator faults can be determined by knowing the set of feasible solutions. One of the aims of this paper is to provide methods to compute this set and to evaluate the admissibility of the control law for a given AFC, once the control objective and the admissibility criteria have been established. In particular, the admissible solution set for the predictive control problem, including the effect of faults (either through reconfiguration or accommodation), is determined using an algorithm that is implemented using set computations based on zonotopes. Finally, the proposed method is tested on a real application consisting of a part of the Barcelona sewer network.

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