An improved long‐horizon model predictive control for DFIG in WECS with variable sampling‐time

For the quasi-linear parameter varying (quasi-LPV) system with bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) is investigated. The estimation error set is represented by a zonotope and refreshed by the zonotopic set-membership estimation method. By properly refreshing the estimation error set online, the bounds of true state at the next sampling time can be obtained. Furthermore, the feasibility of the main optimization problem at the next sampling time can be determined at the current time. A numerical example is given to illustrate the effectiveness of the approach.

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A Variable-Sampling Time Model Predictive Control Algorithm for Improving Path-Tracking Performance of a Vehicle

Sensors ◽

10.3390/s21206845 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6845

Author(s):

Yoonsuk Choi ◽

Wonwoo Lee ◽

Jeesu Kim ◽

Jinwoo Yoo

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Path Following ◽

Path Tracking ◽

Sampling Time ◽

Tracking Performance ◽

Control Input ◽

Automated Driving ◽

Lateral Velocity ◽

Time Model

This paper proposes a novel model predictive control (MPC) algorithm that increases the path tracking performance according to the control input. The proposed algorithm reduces the path tracking errors of MPC by updating the sampling time of the next step according to the control inputs (i.e., the lateral velocity and front steering angle) calculated in each step of the MPC algorithm. The scenarios of a mixture of straight and curved driving paths were constructed, and the optimal control input was calculated in each step. In the experiment, a scenario was created with the Automated Driving Toolbox of MATLAB, and the path-following performance characteristics and computation times of the existing and proposed MPC algorithms were verified and compared with simulations. The results prove that the proposed MPC algorithm has improved path-following performance compared to those of the existing MPC algorithm.

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Three-Vector-Based Low Complexity Model Predictive Control for Soft Open Point

Mathematical Problems in Engineering ◽

10.1155/2022/1526676 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Zhengqi Wang ◽

Haoyu Zhou ◽

Qunhai Huo ◽

Sipeng Hao

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Method ◽

Low Complexity ◽

Sampling Time ◽

Vector Model ◽

Dynamic State ◽

Network Systems ◽

Voltage Vector ◽

Open Point

Soft open point (SOP) can improve the flexibility and reliability of power supplies; thus, they are widely used in distribution network systems. Traditional single-vector model predictive control (SV-MPC) can quickly and flexibly control the power and current at both ports of the SOP. However, SV-MPC can only select one voltage vector in a sampling time, producing large current ripples, and power fluctuations. In order to solve the above problems, this paper proposes a three-vector-based low complexity model predictive control (TV-MPC). In the proposed control method, two effective voltage vectors and one zero voltage vector are selected in a sampling time. For the two-port SOP, methods are given to judge the sectors on both sides and select the voltage vectors. Furthermore, the calculation method of the distribution time is proposed as well. Finally, the effectiveness of the proposed method is verified by steady-state and dynamic-state simulation results compared with the SV-MPC.

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Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Aerospace ◽

10.3390/aerospace9010034 ◽

2022 ◽

Vol 9 (1) ◽

pp. 34

Author(s):

Kaiyang Guo ◽

Pan Tang ◽

Hui Wang ◽

Defu Lin ◽

Xiaoxi Cui

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Planning Process ◽

Control Method ◽

External Disturbance ◽

Sampling Time ◽

External Disturbances ◽

Autonomous Landing ◽

Moving Platform ◽

Landing Trajectory

Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

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Synchronous distributed model predictive control for state-coupled systems

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218819129 ◽

2019 ◽

Vol 41 (10) ◽

pp. 2922-2931 ◽

Cited By ~ 1

Author(s):

Yuanqing Yang ◽

Baocang Ding

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Sampling Time ◽

Coupled Systems ◽

Distributed Model ◽

Constrained System ◽

Physical Constraints ◽

Distributed Model Predictive Control ◽

Coupled Subsystems ◽

Compatibility Constraint

In this paper, we study distributed model predictive control (MPC) for the constrained system composed of a set of dynamically coupled subsystems. The proposed approach is based on the synchronous framework, so in each sub-controller, assumed state trajectories of its neighbours are involved, which are constructed by the optimal solutions at the previous sampling time. The compatibility constraint is imposed to bound the uncertain deviation between the assumed predictive trajectory and the true one. In addition, we use the constraint tightening technique in the predicted future evolutions to counteract this uncertain deviation which appears in the dynamics of each subsystem, so that physical constraints of the state and input are satisfied at each sampling time. By applying the proposed distributed MPC approach, the recursive feasibility and asymptotic stability of the overall system are guaranteed. An example is provided to verify the effectiveness and advantage of the result.

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