High-efficiency predictive control of pumping

This study designs an active equilibrium control strategy based on model predictive control (MPC) for series battery packs. To shorten equalisation time and reduce unnecessary energy consumption, bidirectional active equalisation is modelled and analysed, and the model predictive control algorithm is then applied to the established state space equation. The optimisation problem that minimises the equilibrium time is transformed to a linear programming form in each cycle. By solving the linear programming problem online, a group of control optimal solutions is found and the series equalisation problem is decoupled. The equalisation time is shortened by dynamically adjusting the equalisation current. Simulation results show that the MPC algorithm can avoid unnecessary energy transfer and shorten equalisation time. The bench experimental result shows that the equilibrium time is reduced by 31%, verifying the rationality of the MPC strategy.

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Identification-based predictive control of semi-active shock-absorbers for adaptive dynamic excitation mitigation

Meccanica ◽

10.1007/s11012-020-01239-6 ◽

2020 ◽

Vol 55 (12) ◽

pp. 2571-2597 ◽

Cited By ~ 1

Author(s):

Cezary Graczykowski ◽

Rami Faraj

Keyword(s):

Predictive Control ◽

High Efficiency ◽

Optimal Path ◽

Tracking Error ◽

Practical Implementation ◽

Dynamic Excitation ◽

Shock Absorbers ◽

Valve Opening ◽

Impact Absorption ◽

Double Chamber

AbstractThe paper is aimed at detailed discussion of the Identification-based Predictive Control (IPC) developed for semi-active fluid-based shock-absorbers which protect structures and machines against impact excitations. The problem addressed is the optimal impact absorption providing adaptive mitigation of dynamic response of the mechanical system. The goal of applied control is dissipation of the entire impact energy and minimization of the impacting object deceleration during the process. Three proposed implementations of the IPC are based on sequentially repeated procedures, which include identification of excitation parameters and calculation of the valve opening providing minimization of tracking error of the optimal path. The presented numerical examples concerning mitigation of the dynamic excitation acting on the double-chamber pneumatic shock-absorber reveal high efficiency and prove robustness of the proposed control methods. The developed algorithms are compared against each other in terms of path-tracking efficiency and character of required control actions. The most important challenges in practical implementation of the proposed methods are indicated.

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A Highly Efficient Single-Phase Three-Level Neutral Point Clamped (NPC) Converter Based on Predictive Control with Reduced Number of Commutations

Energies ◽

10.3390/en11123524 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3524 ◽

Cited By ~ 9

Author(s):

Eun-Su Jun ◽

Sangshin Kwak

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Power Factor ◽

Single Phase ◽

High Efficiency ◽

Input Power ◽

Neutral Point ◽

Optimal State ◽

Highly Efficient ◽

Switching Losses

This paper proposes a highly efficient single-phase three-level neutral point clamped (NPC) converter operated by a model predictive control (MPC) method with reduced commutations of switches. The proposed method only allows switching states with none or a single commutation at the next step as candidates for future switching states for the MPC method. Because the proposed method preselects switching states with reduced commutations when selecting an optimal state at a future step, the proposed method can reduce the number of switchings and the corresponding switching losses. Although the proposed method slightly increases the peak-to-peak variations of the two dc capacitor voltages, the developed method does not deteriorate the input current quality and input power factor despite the reduced number of switching numbers and losses. Thus, the proposed method can reduce the number of switching losses and lead to high efficiency, in comparison with the conventional MPC method.

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A Predictive Guidance Obstacle Avoidance Algorithm for AUV in Unknown Environments

Sensors ◽

10.3390/s19132862 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2862 ◽

Cited By ~ 2

Author(s):

Juan Li ◽

Jianxin Zhang ◽

Honghan Zhang ◽

Zheping Yan

Keyword(s):

Obstacle Avoidance ◽

Predictive Control ◽

High Efficiency ◽

Simulation Experiment ◽

Autonomous Underwater Vehicle ◽

Trajectory Point ◽

Unknown Environments ◽

Different Types ◽

Optimization Function ◽

Guidance Algorithm

A predictive guidance obstacle avoidance algorithm (PGOA) in unknown environments is proposed for autonomous underwater vehicle (AUV) that must adapt to multiple complex obstacle environments. Using the environmental information collected by the Forward-looking Sonar (FLS), the obstacle boundary is simplified by the convex algorithm and Bessel interpolation. Combining the predictive control secondary optimization function and the obstacle avoidance weight function, the predicting obstacle avoidance trajectory parameters are obtained. According to different types of obstacle environments, the corresponding obstacle avoidance rules are formulated. Lastly, combining with the obstacle avoidance parameters and rules, the AUV’s predicting obstacle avoidance trajectory point is obtained. Then AUV can successfully achieve obstacle avoidance using the guidance algorithm. The simulation results show that the PGOA algorithm can better predict the trajectory point of the obstacle avoidance path of AUV, and the secondary optimization function can successfully achieve collision avoidance for different complex obstacle environments. Lastly, comparing the execution efficiency and cost of different algorithms, which deal with various complex obstacle environments, simulation experiment results indicate the high efficiency and great adaptability of the proposed algorithm.

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