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optimization control of hydrometallurgical leaching process based on IDE-MPC

MINERALS ENGINEERING 2022年第0期176卷 107341-107341页

作者： Niu, Dapeng Liu, Xudong Liu, Yuanqing Jia, Mingxing Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

The utilization rate of raw materials during the hydrometallurgical leaching process has a great influence on the whole economic benefits of the hydrometallurgy plant, so it is necessary for the leaching process to improve the utilization of materials using optimization control methods. In this paper, the dynamic model for the hydrometallurgical leaching process of a gold hydrometallurgy plant is first built based on the reaction mechanism of the process. Then, the model parameters are identified using least-squares fitting. Thereafter, with the maximum economic benefit as the objective function, the steady-state economic optimization model of the leaching process is established, and an improved particle swarm optimization algorithm is used to solve the model. Taking the optimization results as the control objective, a model predictive control method based on an improved differential evolution algorithm is proposed to control the leaching process, so as to improve the intractability and anti-disturbance performance of the controller for the leaching process. The simulation results show that the proposed optimization and control methods achieve satisfactory effects.

关键词： Hydrometallurgical leaching process optimization control Particle swarm optimization algorithm Model predictive control Differential evolution algorithm

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A Hierarchical Model-Based optimization control Approach for Cooperative Merging by Connected Automated Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第12期22卷 7712-7725页

作者： Chen, Na van Arem, Bart Alkim, Tom Wang, Meng Delft Univ Technol Dept Transport & Planning NL-2628 CN Delft Netherlands Minist Infrastruct & Water Management Rijkswaterstaat NL-2515 XP The Hague Netherlands

Gap selection and dynamic speed profiles of interacting vehicles at on-ramps affect the safety and efficiency of highway merging sections. This paper puts forward a hierarchical control approach for Connected Automated Vehicles (CAVs) to achieve efficient and safe merging operations. A tactical layer controller employs a second-order car-following model with a cooperative merging mode to represent a cooperative merging process and generates an optimal vehicle merging sequence and time instants when on-ramp CAVs start to adapt their speeds and positions to prepare merging into the target gaps respectively. An operational layer controller is designed based on Model Predictive control (MPC). It uses a third-order vehicle dynamics model and optimizes desired accelerations for CAVs and the time instants when the on-ramp CAVs initiate the lane-changing executions respectively. Both the tactical layer controller and operational layer controller derive their control commands by minimizing an objective function for different time horizons. The objective function penalizes deviations of CAVs' inter-vehicle gaps to their desired values, relative speeds to their direct predecessors, and actual or desired accelerations, subject to constraints on velocities, actual or desired accelerations, and inter-vehicle gaps. The performance of the proposed hierarchical control framework and a benchmark on-ramp merging method using a first-in-first-out rule to determine the merging sequence is demonstrated under 135 scenarios with different initial conditions, desired time gap settings, and numbers of on-ramp vehicles. The experimental results show the superiority of the hierarchical control approach.

关键词： Merging Acceleration Linear programming Process control Trajectory Vehicle dynamics optimization Connected automated vehicles (CAVs) on-ramp merging merging sequence optimization control

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Inertial Measurement Unit-Based optimization control of a Soft Exosuit for Hip Extension and Flexion Assistance

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JOURNAL OF MECHANISMS AND ROBOTICS-TRANSACTIONS OF THE ASME 2021年第2期13卷 021016页

作者： Chen, Qiang Guo, Shijie Sun, Lei Liu, Qiming Jin, Shanhai Hebei Univ Technol Sch Mech Engn State Key Lab Reliabil & Intelligence Elect Equip Hebei Key Lab Robot Sensing & Human Robot Interac Tianjin 300401 Peoples R China Yanbian Univ Sch Engn Yanji 133002 Peoples R China

The optimization of the assistive force of a soft exosuit is crucial to the assistive effect. In this paper, an inertial measurement unit (IMU)-based optimization controller was designed to provide effective hip extension and flexion assistance for a soft hip-assistive exosuit. The parameters of the assistive profiles that were defined by two functions were approximatively estimated based on an analysis of biological hip power, and then optimized in real time using the hip angles measured by two IMUs bound to the thighs of the wearer. The peak and offset timings were determined using the parameters of the previous gait, while the start and stop points were determined from those of the current gait. Confi rina lion experiment was conducted in which four subjects were tested to demonstrate the validity of the optimization by applying the optimized parameters to the soft exosuit developed by the authors' group. Two of the subjects completed the outdoor walking test at a self-determined pace while carrying a load of 15 kg. All the subjects conducted the walking test on a treadmill at a constant speed of 1.53 m/s with the same load. The results showed that the proposed optimization controller worked well without considering individual differences. In the outdoor walking test, the wearer's natural gait could be maintained by applying the optimized assistive forces. In the treadmill walking test, metabolic rate with assistance turned on was reduced by 8.53 +/- 2.65% (average +/- SEM) compared with the result of assistance turned off.

关键词： soft exosuit hip assistance assistive profiles optimization control metabolic rate robot design wearable robots

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RESEARCH ON THE DESIGN OF INTEGRATED ENERGY MANAGEMENT AND optimization control SYSTEMS FOR NOVEL POWER SYSTEMS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第2期25卷 653-660页

作者： Yang, Zhiqian Wu, Xianyou Chen, Qiuhua Lv, Liangnian Wu, Lei Goldwind Sci & Technol Co Ltd Beijing Peoples R China

This research paper is determining the impact of integrated energy management and optimization control for Novel power systems. Research objectives have been made based on integrating power system for reducing power consumption based on optimization parameters. Optimal control is mainly dealing with control law by finding out a given system and delivering certain criteria for achieving goals. There are mainly three parts for problem optimization purposes those are included decision, constraints and objective functions. Four different objectives has been made those are mainly discussed about the importance of the power systems regarding integrated management system. There are various factors that are affecting optimizations including the role of the multigrain recursion, global convergence, properties of the optimization model and local convergence. Along with this, the optimization control system has been played a crucial role for the power systems development purposes. Research has been based on the effective design of energy management for power systems. The primary application for the optimization techniques is based on the storage system, electromagnetic -based design and mapping design for the microwave structure. The rate of unsustainable energy management is increasing and sustainable energy management system is decreasing. The conclusion has been based on the significance of the optimization control for the power systems.

关键词： Global convergence Novel power systems integrated energy management optimization control Problem opti- mization local convergence Energy management

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Nonsmooth optimization control Based on a Sandwich Model with Hysteresis for Piezo-Positioning Systems

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2023年第3期33卷 449-461页

作者： Yang, Sen Tan, Yonghong Dong, Ruili Tan, Qingyuan Shanghai Normal Univ Coll Math & Phys 100 Guilin Rd Shanghai 200234 Peoples R China Shanghai Normal Univ Coll Informat Mech & Elect Engn 100 Guilin Rd Shanghai 200234 Peoples R China Donghua Univ Coll Informat Sci & Technol 2999 North Renmin Rd Shanghai 201620 Peoples R China R&D Ctr PERDC Ford Powertrain Engn 1 Qual Way Windsor ON N9A 6X3 Canada

A nonsmooth optimization control (NOC) based on a sandwich model with hysteresis is proposed to control a micropositioning system (MPS) with a piezoelectric actuator (PEA). In this control scheme, the hysteresis phenomenon inherent in the PEA is described by a Duhem submodel embedded between two linear dynamic submodels that describe the behavior of the drive amplifier and the flexible hinge with load, respectively, thus constituting a sandwich model with hysteresis. Based on this model, a nonsmooth predictor for sandwich systems with hysteresis is constructed. To avoid the complicated online search for the optimal value of the generalized gradient at a nonsmooth point, the method of the so-called weighted estimation of generalized gradient is proposed. In order to compensate for the model error caused by model uncertainty, a model error compensator (MEC) is integrated into the online optimization control strategy. Afterwards, the stability of the control system is analyzed based on Lyapunov's theory. Finally, the proposed NOC-MEC method is verified on an MPS with a PEA, and the corresponding experimental results are presented.

关键词： optimization control hysteresis compensation piezoelectric actuator sandwich system

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Hierarchical Voltage/Var optimization control of a Wind Farm 16

Hierarchical Voltage/Var Optimization Control of a Wind Farm

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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wang, Min Liao, Kai Yang, Jianwei Southwest Jiaotong Univ Sch Elect Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781665422482

With the rapid development of wind power generation technology, large-scale wind farms are connected to the power grid, which makes the voltage stability of the system vulnerable to the multi time scale characteristics of wind farms. Therefore, the voltage/var control strategies have received extensive attention. This paper proposes a hierarchical vollage/var optimization control strategy of wind farms based on the multi-time scale characteristics of voltage/var control devices in the wind farm. Compared with the traditional vollage/var control strategy, this strategy coordinates different control stages in multiple time ranges. In this way, different voltage/var control devices can cooperate to realize the voltage/var optimization control of the wind farm. The performance of the proposed strategy is verified through a wind farm model of doubly-fed induction generator type in MATLAB / Simulink. The simulation results show that this strategy can effectively reduce the power loss and voltage deviation.

关键词： wind farms voltage/var control optimization control multiple time scales

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Energy-saving optimization control of air compressor systems based on environmental data perception model 5

Energy-saving optimization control of air compressor systems...

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5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Li, Lei Deng, Lan Shen, Chao Jiang, Yaoxian Xu, Zhihong State Grid Zhejiang Elect Power Co Ltd Hangzhou 310000 Zhejiang Peoples R China State Grid Jiaxing Elect Power Supply Co Ltd Jiaxing 314000 Zhejiang Peoples R China State Grid Jiaxing Integrated Energy Serv Co Ltd Jiaxing 314000 Zhejiang Peoples R China Zhejiang Huayun Clean Energy Co Ltd Hangzhou 310000 Zhejiang Peoples R China

ISBN: (纸本)9781665426428

Energy-saving optimization control of the air compressor systems based on environmental data perception model is studied in this research. In the process of long-term continuous operation, the screw air compressor converts electrical energy into mechanical energy and mechanical energy into wind energy. In the process of converting mechanical energy into wind energy, the air is compressed at a strong high pressure, causing its temperature to rise sharply. This is an ordinary physics machine with energy conversion phenomenon, and under this scenario, the proposed model is designed. Optimal control theory and methods mainly solve the problem of general function optimization and the environmental data perception model is used to construct the efficient framework. After implementing the system, it will be applied into the real testings, and the results have shown the better efficiency.

关键词： Perception model optimization control air compressor systems data mining systematic optimization

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Intelligent optimization control for Air Starting Systems Based on Deep Reinforcement Learning

Intelligent Optimization Control for Air Starting Systems Ba...

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第35届中国控制与决策会议

作者： Jin Peng Xin Li Zhongyu Guo Wenda Yang Hongzhang Xu Yiwen Qi School of Automation Shenyang Aerospace University

In this paper,an intelligent optimization control method based on deep reinforcement learning is proposed for air starting systemis of altitude test facility.A deep Q-network(DQN) controller with self-learning capability is employed to effectively improve the dynamic and steady-state performance of the air starting system under pressure and flow *** design methods including state space selection,action output and reward function design are studied and *** results show that compared with PID control,the designed controller can realize no overshoot adjustment of the intake pressure under constant or variable operating conditions,and the adjustment time is *** results verify the rapidity,stability and robustness of the proposed intelligent optimization control method.

关键词： Altitude test facility Air starting systems Deep reinforcement learning optimization control

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optimization control of Front-End Speed Regulation (FESR) Wind Turbine Based on Improved NSGA-II

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IEEE ACCESS 2019年 7卷 45583-45593页

作者： Li Xiaoqing Dong Haiying Li Hongwei Li Mingxue Sun Zhiqiang Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Sch New Energy & Power Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Univ Technol Sch Energy & Power Engn Lanzhou 730050 Gansu Peoples R China Lanzhou Jiaotong Univ Inst Railway Technol Lanzhou 730000 Gansu Peoples R China

Aiming at the power optimization control problem of front-end speed regulation (FESR) wind turbine, when the wind speed above rated wind speed, an optimal control strategy of variable pitch and variable torque based on improved NSGA-II algorithm is proposed. For this strategy, the reverse learning mechanism was used to initialize and evolve, and the crossover probability and mutation probability were adjusted dynamically based on the exponential distribution. A fuzzy comprehensive evaluation was used to get the best solution from the non-inferior solutions. The objective function was established upon the goals of the constant output speed for hydraulic speed converter and the smoothness of FESR's output power. And pitch angle and vane opening angle were optimized by the improved NSGA-II algorithm. Afterward, the optimization control algorithm has been validated through simulation experiment to a 2MWFESR. The simulation results show that the output power of FESR was smoothed, and the speed fluctuation and power fluctuation of FESR were also reduced, better control performance is thus achieved. Therefore, it is feasible and effective to optimize the control of FESR by the improved NSGA-II algorithm proposed in this paper.

关键词： Front-end speed regulation (FESR) wind turbine hybrid automata power control optimization control improved NSGA-II algorithm

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optimization control of LVRT for FESRWT Based on Improved NSGA-II

Optimization Control of LVRT for FESRWT Based on Improved NS...

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Chinese Automation Congress (CAC)

作者： Li, Xiaoqing Dong, Haiying Li, Mingxue Sun, Zhiqiang Lanzhou JiaoTong Univ Sch Automat & Elect Engn Lanzhou Peoples R China Lanzhou JiaoTong Univ Sch New Energy & Power Engn Lanzhou Peoples R China Lanzhou Univ Technol Sch Energy & Power Engn Lanzhou Peoples R China Lanzhou JiaoTong Univ Inst Railway Technol Lanzhou Peoples R China

ISBN: (纸本)9781728140940

Aiming at the low voltage ride through (LVRT) problem of front-end speed regulation wind turbine (FESRWT), an optimal control strategy based on improved NSGA-II is put forward to change pitch angle, guide vane opening and excitation current. In this strategy, the reverse learning mechanism is used to the initialization process, crossover and mutation probabilities are dynamically adjusted based on exponential distribution. The objective function is established with the minimum active power fluctuation and the constant generator terminal voltage as the control objectives. The improved algorithm proposed in this paper is used to optimize the three control parameters of the FESRWT: pitch angle, vane opening and exciting current. The control strategy verification analysis of a 2MW FESRWT was conducted. The simulation results show that the proposed optimal control strategy can make the FESRWT provide large reactive current during the fault period, support grid voltage and meet the technical requirements of wind power access to power system.

关键词： front-end speed regulation wind turbine low voltage ride through reactive power support optimization control

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