scholarly journals Model-based predictive control of dc-dc converter for EV applications

2018 ◽  
Vol 7 (2.12) ◽  
pp. 308
Author(s):  
Chang Hyun Kim ◽  
Houng Kun Joung
Keyword(s):  
Electric Vehicle ◽  
Predictive Control ◽  
Control Method ◽  
Optimal Solution ◽  
Power Converter ◽  
Sampling Time ◽  
System Model ◽  
System Response ◽  
Power Performance ◽  
Control Gain

Background/Objectives: The power performance of electric vehicle chargers depends on the control efficiency of the power converters with on-board and off-board types. In this paper, a new control method is proposed for power converter of fast electric vehicle chargers in order to improve the power efficiency.Methods/Statistical analysis: The proposed control method is the optimal control to minimize the performance objectives from the predicted output, based on the system model. The discretized model of DC-DC converter with sampling time is derived by using lifting operation for taking into account with the desired prediction time.Findings: The existing conventional controllers are obtained by off-line optimal solution and applied to the systems. Once the control gain is determined, the controller is able to reflect the system response at the real-time.Improvements/Applications: The proposed control method has advantages to deal with system performances at real-time and the control actuation is updated every sampling time via the derived mathematical model. It can be directly applicable to real electric vehicle charger systems in industry.  

Performance of a Wave-Energy-Converter Array Operating Under Model-Predictive Control Based on a Convex Formulation

2018 ◽  
Author(s):  
Qian Zhong ◽  
Ronald W. Yeung
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Reactive Power ◽  
Optimal Solution ◽  
Energy Output ◽  
Control Input ◽  
Energy Extraction ◽  
Power Performance ◽  
Point Absorbers

Economics decision drives the operation of ocean-wave energy converters (WEC) to be in a “farm mode”. Control strategy developed for a WEC array will be of high importance for improving the aggregate energy extraction efficiency of the whole system. Model-predictive control (MPC) has shown its strong potential in maximizing the energy output in devices with hard constraints on operation states and machinery inputs (See Ref. [1–3]). Computational demands for using MPC to control an array in real time can be prohibitive. In this paper, we formulate the MPC to control an array of heaving point absorbers, by recasting the optimization problem for energy extraction into a convex Quadratic Programming (QP) problem, the solution of which can be carried out very efficiently. Large slew rates are to be penalized, which can also guarantee the convexity of the QP and improve the computational efficiency for achieving the optimal solution. Constraints on both the states and the control input can be accommodated in this MPC method. Full hydro-dynamic interference effects among the WEC array components are taken into account using the theory developed in [4]. Demonstrative results of the application are presented for arrays of two, three, and four point absorbers operating at different incident-wave angles. Effects of the interacting waves on power performance of the array under the new MPC control are investigated, with simulations conducted in both regular and irregular seas. Heaving motions of individual devices at their optimal conditions are shown. Also presented is the reactive power required by the power takeoff (PTO) system of the array to achieve optimality. We are pleased to contribute this article in celebration of our collegiality with Professor Bernard Molin on the occasion of his honoring symposium.

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

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.

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

Aerospace ◽  
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.

scholarly journals Multivariable Constrained Adaptive Predictive Control based on Closed-loop Subspace Identification

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Method ◽  
Open Loop ◽  
System Model ◽  
Quadratic Program ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Prediction Errors ◽  
Adaptive Predictive Control

In order to deal with nonlinear, time-varying, and multivariable constrained characteristics in closed-loop industrial processes, a multivariable constrained adaptive predictive control (CAPC) method based on closed-loop subspace identification is proposed. The state-space model is obtained through the closed-loop subspace identification algorithm, which is regarded as the system model. The algorithm is implemented online to update the R matrix with a receding window. By comparing the prediction errors before and after updating, it considers whether or not to update the system model. The model is then used to design the model predictive controller, which involves the solution of a quadratic program solving multivariable constraints. This paper presents a comparison between the performance of the proposed control method when applied to a 2-CSTR system, and that of an open-loop subspace CAPC method. The superiority of the proposed method is illustrated by the simulation results.

scholarly journals Echtzeit-Implementierung von NMPC

atp magazin ◽  
2018 ◽  
Vol 60 (08) ◽  
pp. 46
Author(s):  
Stefan Löw ◽  
Dragan Obradovic
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Predictive Control ◽  
Control Method ◽  
Feedforward Neural Networks ◽  
Nonlinear Model Predictive Control ◽  
System Model ◽  
Mechatronic System ◽  
The Neural Networks ◽  
The Cost

Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

Vibration suppression of hub motor electric vehicle considering unbalanced magnetic pull

2021 ◽  
pp. 095440702110045
Author(s):  
Zhongxing Li ◽  
Chenlai Liu ◽  
Xinyan Song ◽  
Chengchong Wang
Keyword(s):  
Neural Networks ◽  
Model Predictive Control ◽  
Electric Vehicle ◽  
Predictive Control ◽  
Vertical Vibration ◽  
System Model ◽  
Driving Safety ◽  
Bp Neural Networks ◽  
Evaluation Indexes ◽  
Unbalanced Magnetic Pull

For the hub motor electric vehicle (HM-EV), the drive motor is directly integrated with the wheel. The unbalanced magnetic pull (UMP) of hub motor would be generated by magnet gap deformation under road surface roughness excitation. The longitudinal and vertical dynamic performances of the HM-EV system are therefore deteriorated. Firstly, to analyze and optimize the longitudinal and vertical dynamic performance of the HM-EV system, a new ten-degree-of-freedom mathematical quarter HM-EV system model equipped with air suspension model, permanent magnet brushless direct current (PM BLDC) hub motor model and rigid ring tire model is proposed. The UMP of PM BLDC hub motor is taken into consideration in this model. A HM-EV system model validation test bench is constructed. The accuracy of the model is verified by experiment. Secondly, based on quarter HM-EV system model, the BP neural network is adopted to calculate the longitudinal and vertical UMP. The relative error between results calculated by BP neural networks and electromagnetic formula is less than 5% and root-mean-square error (RMSE) is less than 2. With proposed BP neural networks calculation method, UMP calculation time is shortened by 70.3%. Finally, the adjustable force is introduced and model predictive control (MPC) method is used to suppress the longitudinal and vertical vibration of HMEV system. Two control methods, namely model predictive control (MPC) and constrained optimal control (COC) are proposed. The simulation results show that by applying MPC, the RMS value of evaluation indexes are decreased by 17.21%–44.10% respectively, which is better than COC (−14.42%–17.21%). With MPC, longitudinal and vertical vibration are suppressed. Comparison of two UMP calculation methods with MPC controller is conducted. The relative errors of evaluation indexes are within 3.85%. Therefore, the driving safety and riding comfort of the HM-EV are improved compared to the passive suspension and COC active suspension.

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