Method for secondary spring load equalization of railway vehicles with two-stage spring suspension: Modeling and optimal regulation

To ensure running safety,the secondary spring loads of railway vehicles must be well equalized.Due to the coupling interactive effects of these hyper static suspended structures,the equalization adjustment through shimming procedure is quite complex.Therefore,an effective and reliable method in application is developed in this paper.Firstly,the best regulation of spring load is solved based on a mechanical model of the secondary suspension system,providing a target for actual adjustment.To reveal the relationship between secondary spring load distribution and shim quantity sequence,a forecasting model is constructed and then modified experimentally with consideration of car body’s elastic deformation.Further,a gradient-based algorithm with a momentum operation is proposed for the load optimization.Effectiveness of the whole method has been verified on a test rig.It is experimentally confirmed that this research provides an important basis for achieving an optimal regulation of spring load distribution for multiple types of railway vehicles.

Incentive Mechanism Design for Public Goods Provision:Price Cap Regulation and Optimal Regulation

This paper studies the mechanism design that induces firms to provide public goods under two regulatory means： price cap regulation and optimal regulation, respectively. We first outline two models of monopoly regulation with unobservable marginal costs and effort, which can be regard as an optimal problem with dual restrictions. By solving this problem, we get the two optimal regulatory mechanisms to induce the provision of public goods. Further, by comparative statics, the conclusion is drawn that the welfare loss as sociated with price cap regulation, with respective to optimal regulation, increases more with increase of the expense of public goods.

高转速浮装式机械密封端面变形影响因素分析及优化

Floating mechanical seals play an important part in the high-speed rotating machine,and its face deformation will lead to seal failure,also directly affects the device operation performance and service life.In this paper,based on the finite element method,a two-dimensional model of the thermal coupling numerical analysis high speed floating mechanical seal was established,and the influence of different parameters such as rotating speed,pressure,temperature and axial compression force on the deformation of seal face is analyzed.It is found that the dynamic and static face deformation increases exponentially with the increase of rotational speed.At high speed,with the increase of working pressure and temperature,the sealing face deformation increases linearly.When the working pressure reaches 8MPa,the sealing face is in dynamic balance,and no further deformation occurs.Under the condition of high speed and negative temperature difference,the deformation of the sealing end face is positive,with the increase of the axial compression force,the end face shrinked inward,and the deformation rate sudden decrease when the force reaches 4MPa.On the contrary,while the temperature difference is positive,the deformation of the seal end face is negative,and the end face expands outward,meanwhile the expansion of deformation are posi-tively correlated with the axial compression force.According to the analysis results,the control optimization method of the end face deformation is put forward,and the accuracy of the numerical analysis results is verified by the high-speed floating mechanical seal test platform,which provides theoretical guidance for the design and use of high-speed floating sealing ring.

Regulation Flexibility Assessment and Optimal Aggregation Strategy of Greenhouse Loads in Modern Agricultural Parks

With advances in modern agricultural parks,the rural energy structure has undergone profound change,leading to the emergence of an agricultural energy internet.This integrated system combines agricultural energy utilization,the information internet,and agricultural production.Accordingly,this study proposes a regulation flexibility assessment approach and optimal aggregation strategy of greenhouse loads(GHLs)for modern agricultural parks.First,taking into account the operational characteristics of typical GHLs,refined load demand models for lighting,humidification,and temperature-controlled loads are established.Secondly,the recursive least squares method-based parameter identification method is designed to accurately determine key GHL model parameters.Finally,based on the regulation flexibility of quantitatively evaluated GHLs,GHLs are optimally aggregated into multiple flexible aggregators considering minimal operational cost and greenhouse environmental constraints.The results indicate that the proposed regulation flexibility assessment approach and optimal aggregation strategy of GHLs can alleviate the peak regulation pressure on power grids by flexibly shifting the load demands of GHLs.

Predictive Functional Controller with a Similar Proportional Integral Optimal Regulator Structure:Comparison with Traditional Predic-tive Functional Controller and Application to Heavy Oil Coking Equipment

By extending the system's state variables,a novel predictive functional controller has been developed.The structure of this controller is similar to that of classical proportional integral(PI)optimal controller and in-cludes a control block that can perform a feed-forward control of future P-step set points.It considers both the state variables and the output errors in its cost function,which results in enhanced control performance compared with traditional state space predictive functional control(TSSPFC)methods that consider only the predictive output er-rors.The predictive functional controller(PFC)has been compared with TSSPFC in terms of tracking ability,dis-turbance rejection,and also based on its application to heavy oil coking equipment.The results obtained show the effectiveness of the controller.

Optimization of dispersed carbon nanoparticles synthesis for rapid desulfurization of liquid fuel

Stringent regulations and environmental concerns make the production of clean fuels with low sulfur content compulsory for the petroleum refining industry.Because of ease of operation without high energy consumption,the adsorption of sulfur compounds seems the most promising process.Central composite design was used to optimize parameters influencing the synthesis of dispersed carbon nanoparticles（CNPs）,a new class of sorbents,in order to obtain an excellent adsorbent for desulfurization of liquid fuel.The optimized dispersed CNPs,which are immiscible in liquid fuel,can effectively adsorb different benzothiophenic compounds.Equilibrium adsorption was achieved within 2 min for benzothiophene,dibenzothiophene,and 4,6-dimethyldibenzothiophene with removal efficiency values of 75 %,83 %,and 52 %,respectively.The rate of desulfurization by the prepared CNPs in the present work is seven times higher than the previously reported CNPs.Optimized CNPs were characterized by different techniques.Finally,the effect of the mass of CNPs on the removal efficiency was studied as well.

Optimal Frequency Regulation Based on Characterizing the Air Conditioning Cluster by Online Deep Learning

The air conditioning cluster(ACC)is a potential candidate to provide frequency regulation reserves.However,the effective assessment of the ACC willing reserve capacity is often an obstacle for existing demand response(DR)programs,influenced by incentive prices,temperatures,etc.In this paper,the complex relationship between the ACC willing reserve capacity and its key influence factors is defined as a demand response characteristic(DRC).To learn about DRC along with real-time frequency regulation,an online deep learning-based DRC(ODLDRC)modeling methodology is designed to continuously retrain the deep neural network-based model.The ODL-DRC model trained by incoming new data does not require massive historical training data,which makes it more time-efficient.Then,the coordinate operation between ODL-DRC modeling and optimal frequency regulation(OFR)is presented.A robust decentralized sliding mode controller(DSMC)is designed to manage the ACC response power in primary frequency regulation against any ACC response uncertainty.An ODL-DRC model-based OFR scheme is formulated by taking the learning error into consideration.Thereby,the ODL-DRC model can be applied to minimize the total operational cost while maintaining frequency stability,without waiting for a well-trained model.The simulation cases validate the superiority of the OFR based on characterizing the ACC by online learning,which can capture the real DRC and simultaneously optimize the regulation performance with strong robustness against any ACC response uncertainty and learning error.

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

Low-voltage ride through of multi-port power electronic trans-former

A low-voltage ride-through(LVRT)control strategy for the multi-port power electronic transformer(PET)based on power co-regulation is proposed.During the sag and recovery of the grid-side voltage of the medium-voltage ac(MVac)port,the grid-connected active power of the low-voltage ac(LVac)port,rather than the power from external renewable energy sources(e.g.,photovoltaic(PV)),is adjusted quickly to rebalance the power flowing across all ports,thereby preventing overcurrent and overvoltage.Moreover,a power-coordinate-frame-based LVRT mode classification is designed,and a total of six LVRT modes are classified to meet the LVRT requirements in all power configuration scenarios of the PET.In this way,the PET is endowed with the LVRT capability in both power-generation and power-consumption states,which is significantly different from traditional power generation systems such as PV or wind power.Furthermore,by optimizing the active power regulation path during LVRT transition,the overcurrent problem caused by the grid-voltage sag-depth detection delay is overcome.Finally,the effectiveness of the proposed control scheme is verified by experiments on a hardware-in-the-loop platform.

Application of the hybrid genetic particle swarm algorithm to design the linear quadratic regulator controller for the accelerator power supply

Purpose The purpose of this paper is to study a new method to improve the performance of the magnet power supply in the experimental ring of HIRFL-CSR.Methods A hybrid genetic particle swarm optimization algorithm is introduced,and the algorithm is applied to the optimal design of the LQR controller of pulse width modulated power supply.The fitness function of hybrid genetic particle swarm optimization is a multi-objective function,which combined the current and voltage,so that the dynamic performance of the closed-loop system can be better.The hybrid genetic particle swarm algorithm is applied to determine LQR controlling matrices Q and R.Results The simulation results show that adoption of this method leads to good transient responses,and the computational time is shorter than in the traditional trial and error methods.Conclusions The results presented in this paper show that the proposed method is robust,efficient and feasible,and the dynamic and static performance of the accelerator PWM power supply has been considerably improved.

Robust Hybrid Control for Ballistic Missile Longitudinal Autopilot

This paper investigates the boost phase＇s longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capacitor （RLC） network compensator as a control strategy, which does not take into account the time-varying missile dynamics. This may cause the closed-loop system instability in the presence of large disturbance and dynamics uncertainty. Therefore, the existing controller should be redesigned to achieve more stable vehicle response. In this paper, based on gain-scheduling adaptive control strategy, two different types of optimal controllers are proposed. The first controller is gain-scheduled optimal tuning-proportional-integral-derivative （PID） with actuator constraints, which supplies better response but requires a priori knowledge of the system dynamics. Moreover, the controller has oscillatory response in the presence of dynamic uncertainty. Taking this into account, gain-scheduled optimal linear quadratic （LQ） in conjunction with optimal tuning-compensator offers the greatest scope for controller improvement in the presence of dynamic uncertainty and large disturbance. The latter controller is tested through various scenarios for the validated nonlinear dynamic flight model of the real ballistic missile system with autopilot exposed to external disturbances.

NaMYB8 regulates distinct,optimally distributed herbivore defense traits

When herbivores attack, plants specifically reconfigure their metabolism. Herbivory on the wild tobacco Nicotiana attenuata strongly induces the R2R3 MYB transcriptional activator MYB8, which was reported to specifically regulate the accumulation of phenolamides （PAs）. We discovered that transcriptional regulation of trypsin protease inhibitors （TPIs） and a threonine deaminase （TD） also depend on MYB8 expression. Induced distributions of PAs, TD and TPIs all meet predictions of optimal defense theory： their leaf concentrations increase with the fitness value and the probability of attack of the tissue. Therefore, we suggest that these defensive compounds have evolved to be co-regulated by MYB8.

Multi-objective Invasive Weed Optimization of the LQR Controller

The Robogymnast is a triple link underactuated pendulum that mimics a human gymnast hanging from a horizontal bar.In this paper, two multi-objective optimization methods are developed using invasive weed optimization（IWO）. The first method is the weighted criteria method IWO（WCMIWO） and the second method is the fuzzy logic IWO hybrid（FLIWOH）. The two optimization methods were used to investigate the optimum diagonal values for the Q matrix of the linear quadratic regulator（LQR） controller that can balance the Robogymnast in an upright configuration. Two LQR controllers were first developed using the parameters obtained from the two optimization methods. The same process was then repeated, but this time with disturbance applied to the Robogymnast states to develop another set of two LQR controllers. The response of the controllers was then tested in different scenarios using simulation and their performance evaluated. The results show that all four controllers are able to balance the Robogymnast with varying accuracies. It has also been observed that the controllers trained with disturbance achieve faster settling time.