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.

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.

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.

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.

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.

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.

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.